update samples from Release-92 as a part of 1.31.0 SDK stable release

CODE_OF_CONDUCT.md

@@ -1,9 +0,0 @@
-# Microsoft Open Source Code of Conduct
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-
-Resources:
-
-- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
-- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
-- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

README.md

@@ -1,43 +1,77 @@
-# Azure Machine Learning Python SDK notebooks
+# Azure Machine Learning service example notebooks
 
 > a community-driven repository of examples using mlflow for tracking can be found at https://github.com/Azure/azureml-examples
 
-Welcome to the Azure Machine Learning Python SDK notebooks repository!
+This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
-## Getting started
+![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)
 
-These notebooks are recommended for use in an Azure Machine Learning [Compute Instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance), where you can run them without any additional set up.
 
-However, the notebooks can be run in any development environment with the correct `azureml` packages installed.
+## Quick installation
+```sh
+pip install azureml-sdk
+```
+Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
 
-Install the `azureml.core` Python package:
+## How to navigate and use the example notebooks?
+If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
+This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+
+If you want to...
+
+ * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb).
+ * ...learn about experimentation and tracking run history: [track and monitor experiments](./how-to-use-azureml/track-and-monitor-experiments).
+ * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
+ * ...deploy models as a realtime scoring service, first learn the basics by [deploying to Azure Container Instance](./how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as a batch scoring service: [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
+ * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
+
+## Tutorials
+
+The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
+  
+## How to use Azure ML
+
+The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
+
+- [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
+- [Training with ML and DL frameworks](./how-to-use-azureml/ml-frameworks) - Examples demonstrating how to build and train machine learning models at scale on Azure ML and perform hyperparameter tuning.
+- [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
+- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
+- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
+- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
+- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+- [Reinforcement Learning](./how-to-use-azureml/reinforcement-learning) - Examples showing how to train reinforcement learning agents
+
+---
+## Documentation
+
+ * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+ * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
+ * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
+
+---
+
+
+## Community Repository 
+Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
+
+## Projects using Azure Machine Learning
+
+Visit following repos to see projects contributed by Azure ML users:
+ - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
+ - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
+ - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
+ 
+## Data/Telemetry 
+This repository collects usage data and sends it to Microsoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
+
+To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
 
 ```sh
-pip install azureml-core
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
 ```
+This URL will be slightly different depending on the file. 
 
-Install additional packages as needed:
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
-
-```sh
-pip install azureml-mlflow
-pip install azureml-dataset-runtime
-pip install azureml-automl-runtime
-pip install azureml-pipeline
-pip install azureml-pipeline-steps
-...
-```
-
-We recommend starting with one of the [quickstarts](tutorials/compute-instance-quickstarts).
-
-## Contributing
-
-This repository is a push-only mirror. Pull requests are ignored.
-
-## Code of Conduct
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](CODE_OF_CONDUCT.md) for details.
-
-## Reference
-
-- [Documentation](https://docs.microsoft.com/azure/machine-learning)
-

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/fairness/fairlearn-azureml-mitigation.ipynb

@@ -49,7 +49,7 @@
         "* `fairlearn>=0.6.2` (pre-v0.5.0 will work with minor modifications)\n",
         "* `joblib`\n",
         "* `liac-arff`\n",
-        "* `raiwidgets~=0.7.0`\n",
+        "* `raiwidgets==0.4.0`\n",
         "\n",
         "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
       ]

contrib/fairness/fairlearn-azureml-mitigation.yml

@@ -6,4 +6,4 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0

contrib/fairness/upload-fairness-dashboard.ipynb

@@ -51,7 +51,7 @@
         "* `fairlearn>=0.6.2` (also works for pre-v0.5.0 with slight modifications)\n",
         "* `joblib`\n",
         "* `liac-arff`\n",
-        "* `raiwidgets~=0.7.0`\n",
+        "* `raiwidgets==0.4.0`\n",
         "\n",
         "Fairlearn relies on features introduced in v0.22.1 of `scikit-learn`. If you have an older version already installed, please uncomment and run the following cell:"
       ]

contrib/fairness/upload-fairness-dashboard.yml

@@ -6,4 +6,4 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0

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

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==21.1.2
+- pip==20.2.4
 - python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
@@ -21,8 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.32.0
+  - azureml-widgets~=1.31.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.32.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.31.0/validated_win32_requirements.txt [--no-deps]

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

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==21.1.2
+- pip==20.2.4
 - python>=3.5.2,<3.8
 - nb_conda
 - boto3==1.15.18
@@ -21,8 +21,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.32.0
+  - azureml-widgets~=1.31.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.32.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.31.0/validated_linux_requirements.txt [--no-deps]

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

@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip==21.1.2
+- pip==20.2.4
 - nomkl
 - python>=3.5.2,<3.8
 - nb_conda
@@ -22,8 +22,8 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.32.0
+  - azureml-widgets~=1.31.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.32.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.31.0/validated_darwin_requirements.txt [--no-deps]

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

@@ -105,7 +105,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -190,7 +190,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",

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

@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -162,7 +162,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_DS12_V2\" \n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
         "                                                           # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\" \n",
         "                                                           # or similar GPU option\n",
         "                                                           # available in your workspace\n",

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

@@ -81,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -166,7 +166,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -49,8 +49,6 @@ print("Argument 1(ds_name): %s" % args.ds_name)
 
 dstor = ws.get_default_datastore()
 register_dataset = False
-end_time = datetime.utcnow()
-
 try:
     ds = Dataset.get_by_name(ws, args.ds_name)
     end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
@@ -60,9 +58,9 @@ except Exception:
     print(traceback.format_exc())
     print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
     register_dataset = True
-    end_time = datetime(2021, 5, 1, 0, 0)
+    end_time_last_slice = datetime.today() - relativedelta(weeks=4)
-    end_time_last_slice = end_time - relativedelta(weeks=2)
 
+end_time = datetime.utcnow()
 train_df = get_noaa_data(end_time_last_slice, end_time)
 
 if train_df.size > 0:

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

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -91,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -143,7 +143,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",

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

@@ -113,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -187,7 +187,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
@@ -662,7 +662,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.6.7"
     }
   },
   "nbformat": 4,

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

@@ -87,7 +87,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -154,7 +154,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",

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

@@ -24,11 +24,10 @@
         "_**Forecasting using the Energy Demand Dataset**_\n",
         "\n",
         "## Contents\n",
-        "1. [Introduction](#introduction)\n",
+        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#setup)\n",
+        "1. [Setup](#Setup)\n",
-        "1. [Data and Forecasting Configurations](#data)\n",
+        "1. [Data and Forecasting Configurations](#Data)\n",
-        "1. [Train](#train)\n",
+        "1. [Train](#Train)\n",
-        "1. [Generate and Evaluate the Forecast](#forecast)\n",
         "\n",
         "Advanced Forecasting\n",
         "1. [Advanced Training](#advanced_training)\n",
@@ -39,7 +38,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Introduction<a id=\"introduction\"></a>\n",
+        "## Introduction\n",
         "\n",
         "In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n",
         "\n",
@@ -50,16 +49,15 @@
         "1. Configure AutoML using 'AutoMLConfig'\n",
         "1. Train the model using AmlCompute\n",
         "1. Explore the engineered features and results\n",
-        "1. Generate the forecast and compute the out-of-sample accuracy metrics\n",
         "1. Configuration and remote run of AutoML for a time-series model with lag and rolling window features\n",
-        "1. Run and explore the forecast with lagging features"
+        "1. Run and explore the forecast"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Setup<a id=\"setup\"></a>"
+        "## Setup"
       ]
     },
     {
@@ -99,7 +97,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -179,7 +177,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Data<a id=\"data\"></a>\n",
+        "# Data\n",
         "\n",
         "We will use energy consumption [data from New York City](http://mis.nyiso.com/public/P-58Blist.htm) for model training. The data is stored in a tabular format and includes energy demand and basic weather data at an hourly frequency. \n",
         "\n",
@@ -311,7 +309,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Train<a id=\"train\"></a>\n",
+        "## Train\n",
         "\n",
         "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the forecasting parameters to hold all the additional forecasting parameters.\n",
         "\n",
@@ -453,11 +451,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Forecasting<a id=\"forecast\"></a>\n",
+        "## Forecasting\n",
         "\n",
-        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
+        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
-        "\n",
-        "The inference will run on a remote compute. In this example, it will re-use the training compute."
       ]
     },
     {
@@ -466,15 +462,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+        "X_test = test.to_pandas_dataframe().reset_index(drop=True)\n",
+        "y_test = X_test.pop(target_column_name).values"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retreiving forecasts from the model\n",
+        "### Forecast Function\n",
-        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
+        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see the [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {
@@ -483,16 +480,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from run_forecast import run_remote_inference\n",
+        "# The featurized data, aligned to y, will also be returned.\n",
-        "remote_run_infer = run_remote_inference(test_experiment=test_experiment,\n",
+        "# This contains the assumptions that were made in the forecast\n",
-        "                                        compute_target=compute_target,\n",
+        "# and helps align the forecast to the original data\n",
-        "                                        train_run=best_run,\n",
+        "y_predictions, X_trans = fitted_model.forecast(X_test)"
-        "                                        test_dataset=test,\n",
-        "                                        target_column_name=target_column_name)\n",
-        "remote_run_infer.wait_for_completion(show_output=False)\n",
-        "\n",
-        "# download the inference output file to the local machine\n",
-        "remote_run_infer.download_file('outputs/predictions.csv', 'predictions.csv')"
       ]
     },
     {
@@ -500,7 +491,9 @@
       "metadata": {},
       "source": [
         "### Evaluate\n",
-        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals)."
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
+        "\n",
+        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
       ]
     },
     {
@@ -509,9 +502,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# load forecast data frame\n",
+        "from forecasting_helper import align_outputs\n",
-        "fcst_df = pd.read_csv('predictions.csv', parse_dates=[time_column_name])\n",
+        "\n",
-        "fcst_df.head()"
+        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
       ]
     },
     {
@@ -526,8 +519,8 @@
         "\n",
         "# use automl metrics module\n",
         "scores = scoring.score_regression(\n",
-        "    y_test=fcst_df[target_column_name],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    y_pred=fcst_df['predicted'],\n",
+        "    y_pred=df_all['predicted'],\n",
         "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
@@ -536,8 +529,8 @@
         "    \n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df['predicted'], color='b')\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(fcst_df[target_column_name], fcst_df[target_column_name], color='g')\n",
+        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
@@ -546,7 +539,23 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Advanced Training <a id=\"advanced_training\"></a>\n",
+        "Looking at `X_trans` is also useful to see what featurization happened to the data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_trans"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Advanced Training <a id=\"advanced_training\"></a>\n",
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
@@ -629,7 +638,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Advanced Results<a id=\"advanced_results\"></a>\n",
+        "## Advanced Results<a id=\"advanced_results\"></a>\n",
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, time series identifier columns and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation."
       ]
     },
@@ -639,17 +648,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_experiment_advanced = Experiment(ws, experiment_name + \"_inference_advanced\")\n",
+        "# The featurized data, aligned to y, will also be returned.\n",
-        "advanced_remote_run_infer = run_remote_inference(test_experiment=test_experiment_advanced,\n",
+        "# This contains the assumptions that were made in the forecast\n",
-        "                                                 compute_target=compute_target,\n",
+        "# and helps align the forecast to the original data\n",
-        "                                                 train_run=best_run_lags,\n",
+        "y_predictions, X_trans = fitted_model_lags.forecast(X_test)"
-        "                                                 test_dataset=test,\n",
-        "                                                 target_column_name=target_column_name,\n",
-        "                                                 inference_folder='./forecast_advanced')\n",
-        "advanced_remote_run_infer.wait_for_completion(show_output=False)\n",
-        "\n",
-        "# download the inference output file to the local machine\n",
-        "advanced_remote_run_infer.download_file('outputs/predictions.csv', 'predictions_advanced.csv')"
       ]
     },
     {
@@ -658,8 +660,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "fcst_adv_df = pd.read_csv('predictions_advanced.csv', parse_dates=[time_column_name])\n",
+        "from forecasting_helper import align_outputs\n",
-        "fcst_adv_df.head()"
+        "\n",
+        "df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)"
       ]
     },
     {
@@ -674,8 +677,8 @@
         "\n",
         "# use automl metrics module\n",
         "scores = scoring.score_regression(\n",
-        "    y_test=fcst_adv_df[target_column_name],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    y_pred=fcst_adv_df['predicted'],\n",
+        "    y_pred=df_all['predicted'],\n",
         "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
@@ -684,8 +687,8 @@
         "    \n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(fcst_adv_df[target_column_name], fcst_adv_df['predicted'], color='b')\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(fcst_adv_df[target_column_name], fcst_adv_df[target_column_name], color='g')\n",
+        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
@@ -716,7 +719,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_helper.py

@@ -1,15 +1,5 @@
-"""
-This is the script that is executed on the compute instance. It relies
-on the model.pkl file which is uploaded along with this script to the
-compute instance.
-"""
-
-import argparse
 import pandas as pd
 import numpy as np
-from azureml.core import Dataset, Run
-from azureml.automl.core.shared.constants import TimeSeriesInternal
-from sklearn.externals import joblib
 from pandas.tseries.frequencies import to_offset
 
 
@@ -52,38 +42,3 @@ def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
     clean = together[together[[target_column_name,
                                predicted_column_name]].notnull().all(axis=1)]
     return(clean)
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
-parser.add_argument(
-    '--test_dataset', type=str, dest='test_dataset',
-    help='Test Dataset')
-
-args = parser.parse_args()
-target_column_name = args.target_column_name
-test_dataset_id = args.test_dataset
-
-run = Run.get_context()
-ws = run.experiment.workspace
-
-# get the input dataset by id
-test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
-
-X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True)
-y_test = X_test.pop(target_column_name).values
-
-# generate forecast
-fitted_model = joblib.load('model.pkl')
-y_predictions, X_trans = fitted_model.forecast(X_test)
-
-# align output
-df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)
-
-file_name = 'outputs/predictions.csv'
-export_csv = df_all.to_csv(file_name, header=True, index=False)  # added Index
-
-# Upload the predictions into artifacts
-run.upload_file(name=file_name, path_or_stream=file_name)

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/metrics_helper.py

@@ -0,0 +1,22 @@
+import pandas as pd
+import numpy as np
+
+
+def APE(actual, pred):
+    """
+    Calculate absolute percentage error.
+    Returns a vector of APE values with same length as actual/pred.
+    """
+    return 100 * np.abs((actual - pred) / actual)
+
+
+def MAPE(actual, pred):
+    """
+    Calculate mean absolute percentage error.
+    Remove NA and values where actual is close to zero
+    """
+    not_na = ~(np.isnan(actual) | np.isnan(pred))
+    not_zero = ~np.isclose(actual, 0.0)
+    actual_safe = actual[not_na & not_zero]
+    pred_safe = pred[not_na & not_zero]
+    return np.mean(APE(actual_safe, pred_safe))

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/run_forecast.py

@@ -1,38 +0,0 @@
-import os
-import shutil
-from azureml.core import ScriptRunConfig
-
-
-def run_remote_inference(test_experiment, compute_target, train_run,
-                         test_dataset, target_column_name, inference_folder='./forecast'):
-    # Create local directory to copy the model.pkl and forecsting_script.py files into.
-    # These files will be uploaded to and executed on the compute instance.
-    os.makedirs(inference_folder, exist_ok=True)
-    shutil.copy('forecasting_script.py', inference_folder)
-
-    train_run.download_file('outputs/model.pkl',
-                            os.path.join(inference_folder, 'model.pkl'))
-
-    inference_env = train_run.get_environment()
-
-    config = ScriptRunConfig(source_directory=inference_folder,
-                             script='forecasting_script.py',
-                             arguments=['--target_column_name',
-                                        target_column_name,
-                                        '--test_dataset',
-                                        test_dataset.as_named_input(test_dataset.name)],
-                             compute_target=compute_target,
-                             environment=inference_env)
-
-    run = test_experiment.submit(config,
-                                 tags={'training_run_id':
-                                       train_run.id,
-                                       'run_algorithm':
-                                       train_run.properties['run_algorithm'],
-                                       'valid_score':
-                                       train_run.properties['score'],
-                                       'primary_metric':
-                                       train_run.properties['primary_metric']})
-
-    run.log("run_algorithm", run.tags['run_algorithm'])
-    return run

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

@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -285,7 +285,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",

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

@@ -24,20 +24,20 @@
         "_**Orange Juice Sales Forecasting**_\n",
         "\n",
         "## Contents\n",
-        "1. [Introduction](#introduction)\n",
+        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#setup)\n",
+        "1. [Setup](#Setup)\n",
-        "1. [Compute](#compute)\n",
+        "1. [Compute](#Compute)\n",
-        "1. [Data](#data)\n",
+        "1. [Data](#Data)\n",
-        "1. [Train](#train)\n",
+        "1. [Train](#Train)\n",
-        "1. [Forecast](#forecast)\n",
+        "1. [Predict](#Predict)\n",
-        "1. [Operationalize](#operationalize)"
+        "1. [Operationalize](#Operationalize)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Introduction<a id=\"introduction\"></a>\n",
+        "## Introduction\n",
         "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
         "\n",
         "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
@@ -49,7 +49,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup<a id=\"setup\"></a>"
+        "## Setup"
       ]
     },
     {
@@ -82,7 +82,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -122,7 +122,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compute<a id=\"compute\"></a>\n",
+        "## Compute\n",
         "You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
         "\n",
         "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
@@ -149,7 +149,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=6)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
@@ -160,7 +160,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Data<a id=\"data\"></a>\n",
+        "## Data\n",
         "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
       ]
     },
@@ -287,8 +287,7 @@
       "outputs": [],
       "source": [
         "from azureml.core.dataset import Dataset\n",
-        "train_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_train.csv'))\n",
+        "train_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_train.csv'))"
-        "test_dataset = Dataset.Tabular.from_delimited_files(path=datastore.path('dataset/dominicks_OJ_test.csv'))"
       ]
     },
     {
@@ -381,7 +380,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Train<a id=\"train\"></a>\n",
+        "## Train\n",
         "\n",
         "The [AutoMLConfig](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters.\n",
         "\n",
@@ -522,11 +521,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Forecast<a id=\"forecast\"></a>\n",
+        "# Forecasting\n",
         "\n",
-        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. We will do batch scoring on the test dataset which should have the same schema as training dataset.\n",
+        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
-        "\n",
-        "The inference will run on a remote compute. In this example, it will re-use the training compute."
       ]
     },
     {
@@ -535,15 +532,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "test_experiment = Experiment(ws, experiment_name + \"_inference\")"
+        "X_test = test\n",
+        "y_test = X_test.pop(target_column_name).values"
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
-        "### Retreiving forecasts from the model\n",
+        "X_test.head()"
-        "We have created a function called `run_forecast` that submits the test data to the best model determined during the training run and retrieves forecasts. This function uses a helper script `forecasting_script` which is uploaded and expecuted on the remote compute."
       ]
     },
     {
@@ -559,16 +558,18 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from run_forecast import run_remote_inference\n",
+        "# forecast returns the predictions and the featurized data, aligned to X_test.\n",
-        "remote_run_infer = run_remote_inference(test_experiment=test_experiment, \n",
+        "# This contains the assumptions that were made in the forecast\n",
-        "                                        compute_target=compute_target,\n",
+        "y_predictions, X_trans = fitted_model.forecast(X_test)"
-        "                                        train_run=best_run,\n",
+      ]
-        "                                        test_dataset=test_dataset,\n",
+    },
-        "                                        target_column_name=target_column_name)\n",
+    {
-        "remote_run_infer.wait_for_completion(show_output=False)\n",
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
         "\n",
-        "# download the forecast file to the local machine\n",
+        "The [forecast function notebook](../forecasting-forecast-function/auto-ml-forecasting-function.ipynb)."
-        "remote_run_infer.download_file('outputs/predictions.csv', 'predictions.csv')"
       ]
     },
     {
@@ -588,9 +589,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# load forecast data frame\n",
+        "assign_dict = {'predicted': y_predictions, target_column_name: y_test}\n",
-        "fcst_df = pd.read_csv('predictions.csv', parse_dates=[time_column_name])\n",
+        "df_all = X_test.assign(**assign_dict)"
-        "fcst_df.head()"
       ]
     },
     {
@@ -605,8 +605,8 @@
         "\n",
         "# use automl scoring module\n",
         "scores = scoring.score_regression(\n",
-        "    y_test=fcst_df[target_column_name],\n",
+        "    y_test=df_all[target_column_name],\n",
-        "    y_pred=fcst_df['predicted'],\n",
+        "    y_pred=df_all['predicted'],\n",
         "    metrics=list(constants.Metric.SCALAR_REGRESSION_SET))\n",
         "\n",
         "print(\"[Test data scores]\\n\")\n",
@@ -615,8 +615,8 @@
         "    \n",
         "# Plot outputs\n",
         "%matplotlib inline\n",
-        "test_pred = plt.scatter(fcst_df[target_column_name], fcst_df['predicted'], color='b')\n",
+        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
-        "test_test = plt.scatter(fcst_df[target_column_name], fcst_df[target_column_name], color='g')\n",
+        "test_test = plt.scatter(df_all[target_column_name], df_all[target_column_name], color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
         "plt.show()"
       ]
@@ -625,7 +625,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Operationalize<a id=\"operationalize\"></a>"
+        "# Operationalize"
       ]
     },
     {
@@ -723,8 +723,7 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "X_query = test.copy()\n",
+        "X_query = X_test.copy()\n",
-        "X_query.pop(target_column_name)\n",
         "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",
@@ -806,7 +805,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.6.8"
     },
     "tags": [
       "None"

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/forecasting_script.py

@@ -1,89 +0,0 @@
-"""
-This is the script that is executed on the compute instance. It relies
-on the model.pkl file which is uploaded along with this script to the
-compute instance.
-"""
-
-import argparse
-import pandas as pd
-import numpy as np
-from azureml.core import Dataset, Run
-from azureml.automl.core.shared.constants import TimeSeriesInternal
-from sklearn.externals import joblib
-from pandas.tseries.frequencies import to_offset
-
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--target_column_name', type=str, dest='target_column_name',
-    help='Target Column Name')
-parser.add_argument(
-    '--test_dataset', type=str, dest='test_dataset',
-    help='Test Dataset')
-
-args = parser.parse_args()
-target_column_name = args.target_column_name
-test_dataset_id = args.test_dataset
-
-run = Run.get_context()
-ws = run.experiment.workspace
-
-# get the input dataset by id
-test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
-
-X_test = test_dataset.to_pandas_dataframe().reset_index(drop=True)
-y_test = X_test.pop(target_column_name).values
-
-# generate forecast
-fitted_model = joblib.load('model.pkl')
-y_predictions, X_trans = fitted_model.forecast(X_test)
-
-# align output
-df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)
-
-file_name = 'outputs/predictions.csv'
-export_csv = df_all.to_csv(file_name, header=True, index=False)  # added Index
-
-# Upload the predictions into artifacts
-run.upload_file(name=file_name, path_or_stream=file_name)

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/run_forecast.py

@@ -1,38 +0,0 @@
-import os
-import shutil
-from azureml.core import ScriptRunConfig
-
-
-def run_remote_inference(test_experiment, compute_target, train_run,
-                         test_dataset, target_column_name, inference_folder='./forecast'):
-    # Create local directory to copy the model.pkl and forecsting_script.py files into.
-    # These files will be uploaded to and executed on the compute instance.
-    os.makedirs(inference_folder, exist_ok=True)
-    shutil.copy('forecasting_script.py', inference_folder)
-
-    train_run.download_file('outputs/model.pkl',
-                            os.path.join(inference_folder, 'model.pkl'))
-
-    inference_env = train_run.get_environment()
-
-    config = ScriptRunConfig(source_directory=inference_folder,
-                             script='forecasting_script.py',
-                             arguments=['--target_column_name',
-                                        target_column_name,
-                                        '--test_dataset',
-                                        test_dataset.as_named_input(test_dataset.name)],
-                             compute_target=compute_target,
-                             environment=inference_env)
-
-    run = test_experiment.submit(config,
-                                 tags={'training_run_id':
-                                       train_run.id,
-                                       'run_algorithm':
-                                       train_run.properties['run_algorithm'],
-                                       'valid_score':
-                                       train_run.properties['score'],
-                                       'primary_metric':
-                                       train_run.properties['primary_metric']})
-
-    run.log("run_algorithm", run.tags['run_algorithm'])
-    return run

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

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -154,7 +154,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",

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

@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -145,7 +145,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",

how-to-use-azureml/azure-synapse/Synapse_Job_Scala_Support.ipynb

@@ -42,31 +42,6 @@
         "## Leverage ScriptRunConfig to submit scala job to an attached synapse spark cluster"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Prepare data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.datastore import Datastore\n",
-        "# Use the default blob storage\n",
-        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
-        "\n",
-        "# We are uploading a sample file in the local directory to be used as a datasource\n",
-        "file_name = \"shakespeare.txt\"\n",
-        "def_blob_store.upload_files(files=[\"./{}\".format(file_name)], overwrite=False)\n",
-        "\n",
-        "# Create file dataset\n",
-        "file_dataset = Dataset.File.from_files(path=[(def_blob_store, file_name)])"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -84,11 +59,11 @@
         "run_config.spark.configuration[\"spark.executor.memory\"] = \"2g\"\n",
         "run_config.spark.configuration[\"spark.executor.cores\"] = 1\n",
         "run_config.spark.configuration[\"spark.executor.instances\"] = 1\n",
-        "# This can be removed if you are using local jars in source folder\n",
+        "\n",
-        "run_config.spark.configuration[\"spark.yarn.dist.jars\"]=\"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
+        "run_config.spark.configuration[\"spark.yarn.dist.jars\"]=\"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"  # this can be removed if you are using local jars in source folder\n",
         "\n",
         "dir_name = \"wordcount-{}\".format(str(uuid.uuid4()))\n",
-        "input = file_dataset.as_named_input(\"input\").as_hdfs()\n",
+        "input = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/shakespeare.txt\"\n",
         "output = HDFSOutputDatasetConfig(destination=(ws.get_default_datastore(), \"{}/result\".format(dir_name)))\n",
         "\n",
         "from azureml.core import ScriptRunConfig\n",
@@ -128,10 +103,9 @@
         "from azureml.pipeline.steps import SynapseSparkStep\n",
         "\n",
         "configs = {}\n",
-        "#configs[\"spark.yarn.dist.jars\"] = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
+        "configs[\"spark.yarn.dist.jars\"] = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\"\n",
         "step_1 = SynapseSparkStep(name = 'synapse-spark',\n",
         "                          file = 'start_script.py',\n",
-        "                          jars = \"wasbs://synapse@azuremlexamples.blob.core.windows.net/shared/wordcount.jar\",\n",
         "                          source_directory=\".\",\n",
         "                          arguments = args,\n",
         "                          compute_target = 'link-pool',\n",

how-to-use-azureml/azure-synapse/shakespeare.txt

@@ -1,270 +0,0 @@
-This is the 100th Etext file presented by Project Gutenberg, and
-is presented in cooperation with World Library, Inc., from their
-Library of the Future and Shakespeare CDROMS.  Project Gutenberg
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-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS
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-
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-******This file should be named shaks12.txt or shaks12.zip*****
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-
-<<THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM         
-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS  
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-
-
-1609
-
-THE SONNETS
-
-by William Shakespeare
-
-
-THE END
-
-
-
-<<THIS ELECTRONIC VERSION OF THE COMPLETE WORKS OF WILLIAM
-SHAKESPEARE IS COPYRIGHT 1990-1993 BY WORLD LIBRARY, INC., AND IS
-PROVIDED BY PROJECT GUTENBERG ETEXT OF ILLINOIS BENEDICTINE COLLEGE
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-
-
-
-End of this Etext of The Complete Works of William Shakespeare
-
-
-

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

@@ -2,22 +2,23 @@
   "cells": [
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "Copyright (c) Microsoft Corporation. All rights reserved.\n",
         "\n",
         "Licensed under the MIT License."
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "# Register Spark Model and deploy as Webservice\n",
         "\n",
@@ -25,128 +26,120 @@
         "\n",
         " 1. Register Spark Model\n",
         " 2. Deploy Spark Model as Webservice"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Prerequisites\n",
         "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
+      "metadata": {},
-        "# Check core SDK version number\r\n",
-        "import azureml.core\r\n",
-        "\r\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ],
       "outputs": [],
-      "metadata": {}
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Initialize Workspace\n",
         "\n",
         "Initialize a workspace object from persisted configuration."
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core import Workspace\r\n",
-        "\r\n",
-        "ws = Workspace.from_config()\r\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "create workspace"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Register Model"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "You can add tags and descriptions to your Models. Note you need to have a `iris.model` file in the current directory. This model file is generated using [train in spark](../training/train-in-spark/train-in-spark.ipynb) notebook. The below call registers that file as a Model with the same name `iris.model` in the workspace.\n",
         "\n",
         "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core.model import Model\r\n",
-        "\r\n",
-        "model = Model.register(model_path=\"iris.model\",\r\n",
-        "                       model_name=\"iris.model\",\r\n",
-        "                       tags={'type': \"regression\"},\r\n",
-        "                       description=\"Logistic regression model to predict iris species\",\r\n",
-        "                       workspace=ws)"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "register model from file"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "model = Model.register(model_path=\"iris.model\",\n",
+        "                       model_name=\"iris.model\",\n",
+        "                       tags={'type': \"regression\"},\n",
+        "                       description=\"Logistic regression model to predict iris species\",\n",
+        "                       workspace=ws)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Fetch Environment"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment.\n",
         "\n",
         "In this notebook, we will be using 'AzureML-PySpark-MmlSpark-0.15', a curated environment.\n",
         "\n",
         "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
+      "metadata": {},
-        "from azureml.core import Environment\r\n",
-        "from azureml.core.environment import SparkPackage\r\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\r\n",
-        "\r\n",
-        "myenv = Environment('my-pyspark-environment')\r\n",
-        "myenv.docker.base_image = \"mcr.microsoft.com/mmlspark/release:0.15\"\r\n",
-        "myenv.inferencing_stack_version = \"latest\"\r\n",
-        "myenv.python.conda_dependencies = CondaDependencies.create(pip_packages=[\"azureml-core\",\"azureml-defaults\",\"azureml-telemetry\",\"azureml-train-restclients-hyperdrive\",\"azureml-train-core\"], python_version=\"3.6.2\")\r\n",
-        "myenv.python.conda_dependencies.add_channel(\"conda-forge\")\r\n",
-        "myenv.spark.packages = [SparkPackage(\"com.microsoft.ml.spark\", \"mmlspark_2.11\", \"0.15\"), SparkPackage(\"com.microsoft.azure\", \"azure-storage\", \"2.0.0\"), SparkPackage(\"org.apache.hadoop\", \"hadoop-azure\", \"2.7.0\")]\r\n",
-        "myenv.spark.repositories = [\"https://mmlspark.azureedge.net/maven\"]\r\n"
-      ],
       "outputs": [],
-      "metadata": {}
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Create Inference Configuration\n",
         "\n",
@@ -164,109 +157,109 @@
         " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
         " - entry_script = contains logic specific to initializing your model and running predictions\n",
         " - environment = An environment object to use for the deployment. Doesn't have to be registered"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core.model import InferenceConfig\r\n",
-        "\r\n",
-        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "create image"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Deploy Model as Webservice on Azure Container Instance\n",
         "\n",
         "Note that the service creation can take few minutes."
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "from azureml.core.webservice import AciWebservice, Webservice\r\n",
-        "from azureml.exceptions import WebserviceException\r\n",
-        "\r\n",
-        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\r\n",
-        "aci_service_name = 'aciservice1'\r\n",
-        "\r\n",
-        "try:\r\n",
-        "    # if you want to get existing service below is the command\r\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\r\n",
-        "    # we use aci_service_name to create azure aci\r\n",
-        "    service = Webservice(ws, name=aci_service_name)\r\n",
-        "    if service:\r\n",
-        "        service.delete()\r\n",
-        "except WebserviceException as e:\r\n",
-        "    print()\r\n",
-        "\r\n",
-        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\r\n",
-        "\r\n",
-        "service.wait_for_deployment(True)\r\n",
-        "print(service.state)"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "azuremlexception-remarks-sample"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
+        "\n",
+        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
+        "aci_service_name = 'aciservice1'\n",
+        "\n",
+        "try:\n",
+        "    # if you want to get existing service below is the command\n",
+        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
+        "    # we use aci_service_name to create azure aci\n",
+        "    service = Webservice(ws, name=aci_service_name)\n",
+        "    if service:\n",
+        "        service.delete()\n",
+        "except WebserviceException as e:\n",
+        "    print()\n",
+        "\n",
+        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
+        "\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "#### Test web service"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
+      "metadata": {},
-        "import json\r\n",
-        "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\r\n",
-        "\r\n",
-        "test_sample_encoded = bytes(test_sample, encoding='utf8')\r\n",
-        "prediction = service.run(input_data=test_sample_encoded)\r\n",
-        "print(prediction)"
-      ],
       "outputs": [],
-      "metadata": {}
+      "source": [
+        "import json\n",
+        "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\n",
+        "\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "print(prediction)"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "#### Delete ACI to clean up"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "source": [
-        "service.delete()"
-      ],
-      "outputs": [],
       "metadata": {
         "tags": [
           "deploy service",
           "aci"
         ]
-      }
+      },
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Model Profiling\n",
         "\n",
@@ -278,11 +271,11 @@
         "profiling_results = profile.get_results()\n",
         "print(profiling_results)\n",
         "```"
-      ],
+      ]
-      "metadata": {}
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "### Model Packaging\n",
         "\n",
@@ -303,8 +296,7 @@
         "package.wait_for_creation(show_output=True)\n",
         "package.save(\"./local_context_dir\")\n",
         "```"
-      ],
+      ]
-      "metadata": {}
     }
   ],
   "metadata": {

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

@@ -11,4 +11,4 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0

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

@@ -10,4 +10,4 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0

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

@@ -10,4 +10,4 @@ dependencies:
   - ipython
   - matplotlib
   - ipywidgets
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0

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

@@ -12,4 +12,4 @@ dependencies:
   - azureml-dataset-runtime
   - azureml-core
   - ipywidgets
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0

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

@@ -148,7 +148,7 @@
         "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',# for GPU, use \"STANDARD_NC6\"\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',# for GPU, use \"STANDARD_NC6\"\n",
         "                                                           #vm_priority = 'lowpriority', # optional\n",
         "                                                           max_nodes=4)\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",

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

@@ -247,7 +247,7 @@
         "    aml_compute = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
         "    print('Found existing cluster, use it.')\n",
         "except ComputeTargetException:\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
         "                                                           max_nodes=4)\n",
         "    aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",

how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md

@@ -122,8 +122,4 @@ pipeline_run.wait_for_completion(show_output=True)
 -  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset using TabularDataset.
 -  [pipeline-style-transfer.ipynb](../pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb) demonstrates using ParallelRunStep in multi-step pipeline and using output from one step as input to ParallelRunStep.
 
-# Troubleshooting guide
-
-- [Troubleshooting the ParallelRunStep](https://aka.ms/prstsg) includes answers to frequently asked questions. You can find more references there.
-
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.png)

how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb

@@ -451,8 +451,9 @@
       "metadata": {},
       "source": [
         "### Create a dataset of training artifacts\n",
-        "To evaluate a trained policy (a checkpoint) we need to make the checkpoint accessible to the rollout script.\n",
+        "To evaluate a trained policy (a checkpoint) we need to make the checkpoint accessible to the rollout script. All the training artifacts are stored in workspace default datastore under **azureml/<run_id>** directory.\n",
-        "We can use the Run API to download policy training artifacts (saved model and checkpoints) to local compute."
+        "\n",
+        "Here we create a file dataset from the stored artifacts, and then use this dataset to feed these data to rollout estimator."
       ]
     },
     {
@@ -461,90 +462,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from os import path\n",
-        "from distutils import dir_util\n",
-        "\n",
-        "training_artifacts_path = path.join(\"logs\", training_algorithm)\n",
-        "print(\"Training artifacts path:\", training_artifacts_path)\n",
-        "\n",
-        "if path.exists(training_artifacts_path):\n",
-        "    dir_util.remove_tree(training_artifacts_path)\n",
-        "\n",
-        "# Download run artifacts to local compute\n",
-        "child_run_0.download_files(training_artifacts_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now let's find the checkpoints and the last checkpoint number."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# A helper function to find checkpoint files in a directory\n",
-        "def find_checkpoints(file_path):\n",
-        "    print(\"Looking in path:\", file_path)\n",
-        "    checkpoints = []\n",
-        "    for root, _, files in os.walk(file_path):\n",
-        "        for name in files:\n",
-        "            if os.path.basename(root).startswith('checkpoint_'):\n",
-        "                checkpoints.append(path.join(root, name))\n",
-        "    return checkpoints"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Find checkpoints and last checkpoint number\n",
-        "checkpoint_files = find_checkpoints(training_artifacts_path)\n",
-        "\n",
-        "checkpoint_numbers = []\n",
-        "for file in checkpoint_files:\n",
-        "    file = os.path.basename(file)\n",
-        "    if file.startswith('checkpoint-') and not file.endswith('.tune_metadata'):\n",
-        "        checkpoint_numbers.append(int(file.split('-')[1]))\n",
-        "\n",
-        "print(\"Checkpoints:\", checkpoint_numbers)\n",
-        "\n",
-        "last_checkpoint_number = max(checkpoint_numbers)\n",
-        "print(\"Last checkpoint number:\", last_checkpoint_number)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we upload checkpoints to default datastore and create a file dataset. This dataset will be used to pass in the checkpoints to the rollout script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Upload the checkpoint files and create a DataSet\n",
         "from azureml.core import Dataset\n",
         "\n",
+        "run_id = child_run_0.id # Or set to run id of a completed run (e.g. 'rl-cartpole-v0_1587572312_06e04ace_head')\n",
+        "run_artifacts_path = os.path.join('azureml', run_id)\n",
+        "print(\"Run artifacts path:\", run_artifacts_path)\n",
+        "\n",
+        "# Create a file dataset object from the files stored on default datastore\n",
         "datastore = ws.get_default_datastore()\n",
-        "checkpoint_dataref = datastore.upload_files(checkpoint_files, target_path='cartpole_checkpoints_' + run_id, overwrite=True)\n",
+        "training_artifacts_ds = Dataset.File.from_files(datastore.path(os.path.join(run_artifacts_path, '**')))"
-        "checkpoint_ds = Dataset.File.from_files(checkpoint_dataref)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To verify, we can print out the number (and paths) of all the files in the dataset."
+        "To verify, we can print out the number (and paths) of all the files in the dataset, as follows."
       ]
     },
     {
@@ -553,7 +486,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "artifacts_paths = checkpoint_ds.to_path()\n",
+        "artifacts_paths = training_artifacts_ds.to_path()\n",
         "print(\"Number of files in dataset:\", len(artifacts_paths))\n",
         "\n",
         "# Uncomment line below to print all file paths\n",
@@ -572,6 +505,36 @@
         "\n",
         "The checkpoints dataset will be accessible to the rollout script as a mounted folder. The mounted folder and the checkpoint number, passed in via `checkpoint-number`, will be used to create a path to the checkpoint we are going to evaluate. The created checkpoint path then will be passed into RLlib rollout script for evaluation.\n",
         "\n",
+        "Let's find the checkpoints and the last checkpoint number first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Find checkpoints and last checkpoint number\n",
+        "checkpoint_files = [\n",
+        "    os.path.basename(file) for file in training_artifacts_ds.to_path() \\\n",
+        "        if os.path.basename(file).startswith('checkpoint-') and \\\n",
+        "            not os.path.basename(file).endswith('tune_metadata')\n",
+        "]\n",
+        "\n",
+        "checkpoint_numbers = []\n",
+        "for file in checkpoint_files:\n",
+        "    checkpoint_numbers.append(int(file.split('-')[1]))\n",
+        "\n",
+        "print(\"Checkpoints:\", checkpoint_numbers)\n",
+        "\n",
+        "last_checkpoint_number = max(checkpoint_numbers)\n",
+        "print(\"Last checkpoint number:\", last_checkpoint_number)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
         "Now let's configure rollout estimator. Note that we use the last checkpoint for evaluation. The assumption is that the last checkpoint points to our best trained agent. You may change this to any of the checkpoint numbers printed above and observe the effect."
       ]
     },
@@ -613,8 +576,8 @@
         "    \n",
         "    # Data inputs\n",
         "    inputs=[\n",
-        "        checkpoint_ds.as_named_input('artifacts_dataset'),\n",
+        "        training_artifacts_ds.as_named_input('artifacts_dataset'),\n",
-        "        checkpoint_ds.as_named_input('artifacts_path').as_mount()],\n",
+        "        training_artifacts_ds.as_named_input('artifacts_path').as_mount()],\n",
         "    \n",
         "    # The Azure Machine Learning compute target\n",
         "    compute_target=compute_target,\n",

how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb

@@ -474,14 +474,61 @@
         "from os import path\n",
         "from distutils import dir_util\n",
         "\n",
-        "training_artifacts_path = path.join(\"logs\", training_algorithm)\n",
+        "path_prefix = path.join(\"logs\", training_algorithm)\n",
-        "print(\"Training artifacts path:\", training_artifacts_path)\n",
+        "print(\"Path prefix:\", path_prefix)\n",
         "\n",
-        "if path.exists(training_artifacts_path):\n",
+        "if path.exists(path_prefix):\n",
-        "    dir_util.remove_tree(training_artifacts_path)\n",
+        "    dir_util.remove_tree(path_prefix)\n",
         "\n",
-        "# Download run artifacts to local compute\n",
+        "# Uncomment line below to download run artifacts to local compute\n",
-        "child_run_0.download_files(training_artifacts_path)"
+        "#child_run_0.download_files(path_prefix)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a dataset of training artifacts\n",
+        "To evaluate a trained policy (a checkpoint) we need to make the checkpoint accessible to the rollout script. All the training artifacts are stored in workspace default datastore under **azureml/<run_id>** directory.\n",
+        "\n",
+        "Here we create a file dataset from the stored artifacts, and then use this dataset to feed these data to rollout estimator."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "\n",
+        "run_id = child_run_0.id # Or set to run id of a completed run (e.g. 'rl-cartpole-v0_1587572312_06e04ace_head')\n",
+        "run_artifacts_path = os.path.join('azureml', run_id)\n",
+        "print(\"Run artifacts path:\", run_artifacts_path)\n",
+        "\n",
+        "# Create a file dataset object from the files stored on default datastore\n",
+        "datastore = ws.get_default_datastore()\n",
+        "training_artifacts_ds = Dataset.File.from_files(datastore.path(os.path.join(run_artifacts_path, '**')))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To verify, we can print out the number (and paths) of all the files in the dataset, as follows."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "artifacts_paths = training_artifacts_ds.to_path()\n",
+        "print(\"Number of files in dataset:\", len(artifacts_paths))\n",
+        "\n",
+        "# Uncomment line below to print all file paths\n",
+        "#print(\"Artifacts dataset file paths: \", artifacts_paths)"
       ]
     },
     {
@@ -503,6 +550,21 @@
       "source": [
         "import shutil\n",
         "\n",
+        "# A helper function to download movies from a dataset to local directory\n",
+        "def download_movies(artifacts_ds, movies, destination):\n",
+        "    # Create the local destination directory    \n",
+        "    if path.exists(destination):\n",
+        "        dir_util.remove_tree(destination)\n",
+        "    dir_util.mkpath(destination)\n",
+        "\n",
+        "    for i, artifact in enumerate(artifacts_ds.to_path()):\n",
+        "        if artifact in movies:\n",
+        "            print('Downloading {} ...'.format(artifact))\n",
+        "            artifacts_ds.skip(i).take(1).download(target_path=destination, overwrite=True)\n",
+        "\n",
+        "    print('Downloading movies completed!')\n",
+        "\n",
+        "\n",
         "# A helper function to find movies in a directory\n",
         "def find_movies(movie_path):\n",
         "    print(\"Looking in path:\", movie_path)\n",
@@ -528,6 +590,34 @@
         "    )"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now let's find the first and the last recorded videos in training artifacts dataset and download them to a local directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Find first and last movie\n",
+        "mp4_files = [file for file in training_artifacts_ds.to_path() if file.endswith('.mp4')]\n",
+        "mp4_files.sort()\n",
+        "\n",
+        "first_movie = mp4_files[0] if len(mp4_files) > 0 else None\n",
+        "last_movie = mp4_files[-1] if len(mp4_files) > 1 else None\n",
+        "\n",
+        "print(\"First movie:\", first_movie)\n",
+        "print(\"Last movie:\", last_movie)\n",
+        "\n",
+        "# Download movies\n",
+        "training_movies_path = path.join(\"training\", \"videos\")\n",
+        "download_movies(training_artifacts_ds, [first_movie, last_movie], training_movies_path)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -541,7 +631,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "mp4_files = find_movies(training_artifacts_path)\n",
+        "mp4_files = find_movies(training_movies_path)\n",
         "mp4_files.sort()"
       ]
     },
@@ -608,25 +698,6 @@
         "Let's find the checkpoints and the last checkpoint number first."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# A helper function to find checkpoint files in a directory\n",
-        "def find_checkpoints(file_path):\n",
-        "    print(\"Looking in path:\", file_path)\n",
-        "    checkpoints = []\n",
-        "    for root, _, files in os.walk(file_path):\n",
-        "        for name in files:\n",
-        "            if os.path.basename(root).startswith('checkpoint_'):\n",
-        "                checkpoints.append(path.join(root, name))\n",
-        "    return checkpoints\n",
-        "\n",
-        "checkpoint_files = find_checkpoints(training_artifacts_path)"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -634,11 +705,15 @@
       "outputs": [],
       "source": [
         "# Find checkpoints and last checkpoint number\n",
+        "checkpoint_files = [\n",
+        "    os.path.basename(file) for file in training_artifacts_ds.to_path() \\\n",
+        "        if os.path.basename(file).startswith('checkpoint-') and \\\n",
+        "            not os.path.basename(file).endswith('tune_metadata')\n",
+        "]\n",
+        "\n",
         "checkpoint_numbers = []\n",
         "for file in checkpoint_files:\n",
-        "    file = os.path.basename(file)\n",
+        "    checkpoint_numbers.append(int(file.split('-')[1]))\n",
-        "    if file.startswith('checkpoint-') and not file.endswith('.tune_metadata'):\n",
-        "        checkpoint_numbers.append(int(file.split('-')[-1]))\n",
         "\n",
         "print(\"Checkpoints:\", checkpoint_numbers)\n",
         "\n",
@@ -646,20 +721,6 @@
         "print(\"Last checkpoint number:\", last_checkpoint_number)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Upload the checkpoint files and create a DataSet\n",
-        "from azureml.core import Dataset\n",
-        "\n",
-        "datastore = ws.get_default_datastore()\n",
-        "checkpoint_dataref = datastore.upload_files(checkpoint_files, target_path='cartpole_checkpoints_' + run_id, overwrite=True)\n",
-        "checkpoint_ds = Dataset.File.from_files(checkpoint_dataref)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -735,8 +796,8 @@
         "    \n",
         "    # Data inputs\n",
         "    inputs=[\n",
-        "        checkpoint_ds.as_named_input('artifacts_dataset'),\n",
+        "        training_artifacts_ds.as_named_input('artifacts_dataset'),\n",
-        "        checkpoint_ds.as_named_input('artifacts_path').as_mount()],\n",
+        "        training_artifacts_ds.as_named_input('artifacts_path').as_mount()],\n",
         "    \n",
         "    # The Azure Machine Learning compute target set up for Ray head nodes\n",
         "    compute_target=compute_target,\n",
@@ -818,15 +879,16 @@
         "print('Number of child runs:', len(child_runs))\n",
         "child_run_0 = child_runs[0]\n",
         "\n",
-        "# Download rollout artifacts\n",
+        "run_id = child_run_0.id # Or set to run id of a completed run (e.g. 'rl-cartpole-v0_1587572312_06e04ace_head')\n",
-        "rollout_artifacts_path = path.join(\"logs\", \"rollout\")\n",
+        "run_artifacts_path = os.path.join('azureml', run_id)\n",
-        "print(\"Rollout artifacts path:\", rollout_artifacts_path)\n",
+        "print(\"Run artifacts path:\", run_artifacts_path)\n",
         "\n",
-        "if path.exists(rollout_artifacts_path):\n",
+        "# Create a file dataset object from the files stored on default datastore\n",
-        "    dir_util.remove_tree(rollout_artifacts_path)\n",
+        "datastore = ws.get_default_datastore()\n",
+        "rollout_artifacts_ds = Dataset.File.from_files(datastore.path(os.path.join(run_artifacts_path, '**')))\n",
         "\n",
-        "# Download videos to local compute\n",
+        "artifacts_paths = rollout_artifacts_ds.to_path()\n",
-        "child_run_0.download_files(\"logs/video\", output_directory = rollout_artifacts_path)"
+        "print(\"Number of files in dataset:\", len(artifacts_paths))"
       ]
     },
     {
@@ -842,11 +904,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Look for the downloaded movie in local directory\n",
+        "# Find last movie\n",
-        "mp4_files = find_movies(rollout_artifacts_path)\n",
+        "mp4_files = [file for file in rollout_artifacts_ds.to_path() if file.endswith('.mp4')]\n",
         "mp4_files.sort()\n",
+        "\n",
         "last_movie = mp4_files[-1] if len(mp4_files) > 1 else None\n",
-        "print(\"Last movie:\", last_movie)"
+        "print(\"Last movie:\", last_movie)\n",
+        "\n",
+        "# Download last movie\n",
+        "rollout_movies_path = path.join(\"rollout\", \"videos\")\n",
+        "download_movies(rollout_artifacts_ds, [last_movie], rollout_movies_path)\n",
+        "\n",
+        "# Look for the downloaded movie in local directory\n",
+        "mp4_files = find_movies(rollout_movies_path)\n",
+        "mp4_files.sort()"
       ]
     },
     {
@@ -889,12 +960,16 @@
         "#compute_target.delete()\n",
         "\n",
         "# To delete downloaded training artifacts\n",
-        "#if os.path.exists(training_artifacts_path):\n",
+        "#if os.path.exists(path_prefix):\n",
-        "#    dir_util.remove_tree(training_artifacts_path)\n",
+        "#    dir_util.remove_tree(path_prefix)\n",
+        "\n",
+        "# To delete downloaded training videos\n",
+        "#if path.exists(training_movies_path):\n",
+        "#    dir_util.remove_tree(training_movies_path)\n",
         "\n",
         "# To delete downloaded rollout videos\n",
-        "#if path.exists(rollout_artifacts_path):\n",
+        "#if path.exists(rollout_movies_path):\n",
-        "#    dir_util.remove_tree(rollout_artifacts_path)"
+        "#    dir_util.remove_tree(rollout_movies_path)"
       ]
     },
     {
@@ -911,9 +986,6 @@
     "authors": [
       {
         "name": "hoazari"
-      },
-      {
-        "name": "dasommer"
       }
     ],
     "kernelspec": {

how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb

@@ -35,7 +35,7 @@
       "source": [
         "## Install required packages\n",
         "\n",
-        "This notebook works with Fairlearn v0.7.0, but not with versions pre-v0.5.0. If needed, please uncomment and run the following cell:"
+        "This notebook works with Fairlearn v0.6.1, but not with versions pre-v0.5.0. If needed, please uncomment and run the following cell:"
       ]
     },
     {

how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml

@@ -8,5 +8,5 @@ dependencies:
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets
-  - raiwidgets~=0.7.0
+  - raiwidgets==0.4.0
   - liac-arff

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 1.32.0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.31.0, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.32.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.31.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },