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Tutorial - Deploy Azure Machine Learning to a device using Azure IoT Edge
In this tutorial, you create an Azure Machine Learning model, then deploy it as a module to an edge device
kgremban
philmea
kgremban
07/29/2020
tutorial
iot-edge
iot-edge
mvc

Tutorial: Deploy Azure Machine Learning as an IoT Edge module (preview)

[!INCLUDE iot-edge-version-all-supported]

Use Azure Notebooks to develop a machine learning module and deploy it to a device running Azure IoT Edge with Linux containers. You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning on IoT Edge, see Azure Machine Learning documentation.

Note

Azure Machine Learning modules on Azure IoT Edge are in public preview.

The Azure Machine Learning module that you create in this tutorial reads the environmental data generated by your device and labels the messages as anomalous or not.

In this tutorial, you learn how to:

[!div class="checklist"]

  • Create an Azure Machine Learning module.
  • Push a module container to an Azure container registry.
  • Deploy an Azure Machine Learning module to your IoT Edge device.
  • View generated data.

[!INCLUDE quickstarts-free-trial-note]

Prerequisites

An Azure IoT Edge device:

  • You can use the quickstarts to set up a Linux device or Windows device.
  • The Azure Machine Learning module doesn't support Windows containers.
  • The Azure Machine Learning module doesn't support ARM processors.

Cloud resources:

  • A free or standard-tier IoT Hub in Azure.
  • An Azure Machine Learning workspace. Follow the instructions in Use the Azure portal to get started with Azure Machine Learning to create one and learn how to use it.
    • Make a note of the workspace name, resource group, and subscription ID. These values are all available on the workspace overview in the Azure portal. You'll use these values later in the tutorial to connect an Azure Notebooks file to your workspace resources.

Create and deploy Azure Machine Learning module

In this section, you convert trained machine learning model files and into an Azure Machine Learning container. All the components required for the Docker image are in the AI Toolkit for Azure IoT Edge Git repo. Follow these steps to upload that repository into Microsoft Azure Notebooks to create the container and push it to Azure Container Registry.

  1. Navigate to your Azure Notebooks projects. You can get there from your Azure Machine Learning workspace in the Azure portal or by signing in to Microsoft Azure Notebooks with your Azure account.

  2. Select Upload GitHub Repo.

  3. Provide the following GitHub repository name: Azure/ai-toolkit-iot-edge. Uncheck the Public box if you want to keep your project private. Select Import.

  4. Once the import is finished, navigate into the new ai-toolkit-iot-edge project and open the IoT Edge anomaly detection tutorial folder.

  5. Verify that your project is running. If not, select Run on Free Compute.

    Run on free compute

  6. Open the aml_config/config.json file.

  7. Edit the config file to include the values for your Azure subscription ID, a resource group in your subscription, and your Azure Machine Learning workspace name. You can get all these values from the Overview section of your workspace in Azure.

  8. Save the config file.

  9. Open the 00-anomaly-detection-tutorial.ipynb file.

  10. When prompted, select the Python 3.6 kernel then select Set Kernel.

  11. Edit the first cell in the notebook according to the instructions in the comments. Use the same resource group, subscription ID, and workspace name that you added to the config file.

  12. Run the cells in the notebook by selecting them and selecting Run or pressing Shift + Enter.

    [!TIP] Some of the cells in the anomaly detection tutorial notebook are optional, because they create resources that some users may or may not have yet, like an IoT Hub. If you put your existing resource information in the first cell, you'll receive errors if you run the cells that create new resources because Azure won't create duplicate resources. This is fine, and you can ignore the errors or skip those optional sections entirely.

By completing all the steps in the notebook, you trained an anomaly detection model, built it as a Docker container image, and pushed that image to Azure Container Registry. Then, you tested the model and finally deployed it to your IoT Edge device.

View container repository

Check that your container image was successfully created and stored in the Azure container registry associated with your machine learning environment. The notebook that you used in the previous section automatically provided the container image and the registry credentials to your IoT Edge device, but you should know where they're stored so that you can find the information yourself later.

  1. In the Azure portal, navigate to your Machine Learning service workspace.

  2. The Overview section lists the workspace details as well its associated resources. Select the Registry value, which should be your workspace name followed by random numbers.

  3. In the container registry, under Services, select Repositories. You should see a repository called tempanomalydetection that was created by the notebook you ran in the earlier section.

  4. Select tempanomalydetection. You should see that the repository has one tag: 1.

    Now that you know the registry name, repository name, and tag, you know the full image path of the container. Image paths look like <registry_name>.azurecr.io/tempanomalydetection:1. You can use the image path to deploy this container to IoT Edge devices.

  5. In the container registry, under Settings, select Access keys. You should see a number of access credentials, including Login server and the Username, and Password for an admin user.

    These credentials can be included in the deployment manifest to give your IoT Edge device access to pull container images from the registry.

Now you know where the Machine Learning container image is stored. The next section walks through steps to view the container running as a module on your IoT Edge device.

View the generated data

You can view messages being generated by each IoT Edge module, and you can view messages that are delivered to your IoT hub.

View data on your IoT Edge device

On your IoT Edge device, you can view the messages being sent from every individual module.

You may need to use sudo for elevated permissions to run iotedge commands. Signing out and signing back in to your device automatically updates your permissions.

  1. View all modules on your IoT Edge device.

    iotedge list

  2. View the messages being sent from a specific device. Use the module name from the output of the previous command.

    iotedge logs <module_name> -f

View data arriving at your IoT hub

You can view the device-to-cloud messages that your IoT hub receives by using the Azure IoT Hub extension for Visual Studio Code.

The following steps show you how to set up Visual Studio Code to monitor device-to-cloud messages that arrive at your IoT hub.

  1. In the Visual Studio Code explorer, under the Azure IoT Hub section, expand Devices to see your list of IoT devices.

  2. Right-click the name of your IoT Edge device and select Start Monitoring Built-in Event Endpoint.

  3. Observe the messages coming from tempSensor every five seconds. The message body contains a property called anomaly, which the machinelearningmodule provides with a true or false value. The AzureMLResponse property contains the value "OK" if the model ran successfully.

    Azure Machine Learning response in message body

Clean up resources

If you plan to continue to the next recommended article, you can keep the resources and configurations that you created and reuse them. You can also keep using the same IoT Edge device as a test device.

Otherwise, you can delete the local configurations and the Azure resources that you created in this article to avoid charges.

[!INCLUDE iot-edge-clean-up-cloud-resources]

Next steps

In this tutorial, you deployed an IoT Edge module powered by Azure Machine Learning. You can continue on to any of the other tutorials to learn about other ways that Azure IoT Edge can help you turn data into business insights at the edge.

[!div class="nextstepaction"] Classify images with Custom Vision service