Use GitHub Actions with Azure Machine Learning

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Get started with GitHub Actions to train a model on Azure Machine Learning.

This article will teach you how to create a GitHub Actions workflow that builds and deploys a machine learning model to Azure Machine Learning. You'll train a scikit-learn linear regression model on the NYC Taxi dataset.

GitHub Actions uses a workflow YAML (.yml) file in the /.github/workflows/ path in your repository. This definition contains the various steps and parameters that make up the workflow.

Prerequisites

Before following the steps in this article, make sure you have the following prerequisites:

• An Azure Machine Learning workspace. If you don't have one, use the steps in the Quickstart: Create workspace resources article to create one.

• To install the Python SDK v2, use the following command:

  pip install azure-ai-ml azure-identity
  

  To update an existing installation of the SDK to the latest version, use the following command:

  pip install --upgrade azure-ai-ml azure-identity
  

  For more information, see Install the Python SDK v2 for Azure Machine Learning.

• A GitHub account. If you don't have one, sign up for free.

Step 1: Get the code

Fork the following repo at GitHub:

https://github.com/azure/azureml-examples

Step 2: Authenticate with Azure

You'll need to first define how to authenticate with Azure. You can use a service principal or OpenID Connect.

Generate deployment credentials

Service principal

Create a service principal with the az ad sp create-for-rbac command in the Azure CLI. Run this command with Azure Cloud Shell in the Azure portal or by selecting the Try it button.

az ad sp create-for-rbac --name "myML" --role contributor \
                            --scopes /subscriptions/<subscription-id>/resourceGroups/<group-name> \
                            --json-auth

The parameter --json-auth is available in Azure CLI versions >= 2.51.0. Versions prior to this use --sdk-auth with a deprecation warning.

In the example above, replace the placeholders with your subscription ID, resource group name, and app name. The output is a JSON object with the role assignment credentials that provide access to your App Service app similar to below. Copy this JSON object for later.

  {
    "clientId": "<GUID>",
    "clientSecret": "<GUID>",
    "subscriptionId": "<GUID>",
    "tenantId": "<GUID>",
    (...)
  }

OpenID Connect

OpenID Connect is an authentication method that uses short-lived tokens. Setting up OpenID Connect with GitHub Actions is more complex process that offers hardened security.

1. If you do not have an existing application, register a new Microsoft Entra application and service principal that can access resources.

   az ad app create --display-name myApp
   

   This command will output JSON with an appId that is your client-id. The objectId is APPLICATION-OBJECT-ID and it will be used for creating federated credentials with Graph API calls. Save the value to use as the AZURE_CLIENT_ID GitHub secret later.

2. Create a service principal. Replace the $appID with the appId from your JSON output. This command generates JSON output with a different objectId will be used in the next step. The new objectId is the assignee-object-id.

   This command generates JSON output with a different objectId and will be used in the next step. The new objectId is the assignee-object-id.

   Copy the appOwnerTenantId to use as a GitHub secret for AZURE_TENANT_ID later.

    az ad sp create --id $appId
   

3. Create a new role assignment by subscription and object. By default, the role assignment will be tied to your default subscription. Replace $subscriptionId with your subscription ID, $resourceGroupName with your resource group name, and $assigneeObjectId with generated assignee-object-id (the newly created service principal object id).

   az role assignment create --role contributor --subscription $subscriptionId --assignee-object-id  $assigneeObjectId --assignee-principal-type ServicePrincipal --scope /subscriptions/$subscriptionId/resourceGroups/$resourceGroupName
   

4. Run the following command to create a new federated identity credential for your Microsoft Entra application.

   • Replace APPLICATION-OBJECT-ID with the objectId (generated while creating app) for your Microsoft Entra application.
   • Set a value for CREDENTIAL-NAME to reference later.
   • Set the subject. The value of this is defined by GitHub depending on your workflow:
     • Jobs in your GitHub Actions environment: repo:< Organization/Repository >:environment:< Name >
     • For Jobs not tied to an environment, include the ref path for branch/tag based on the ref path used for triggering the workflow: repo:< Organization/Repository >:ref:< ref path>. For example, repo:n-username/ node_express:ref:refs/heads/my-branch or repo:n-username/ node_express:ref:refs/tags/my-tag.
     • For workflows triggered by a pull request event: repo:< Organization/Repository >:pull_request.

   az ad app federated-credential create --id <APPLICATION-OBJECT-ID> --parameters credential.json
   ("credential.json" contains the following content)
   {
       "name": "<CREDENTIAL-NAME>",
       "issuer": "https://token.actions.githubusercontent.com",
       "subject": "repo:octo-org/octo-repo:environment:Production",
       "description": "Testing",
       "audiences": [
           "api://AzureADTokenExchange"
       ]
   }
   

Create secrets

Service principal

1. In GitHub, go to your repository.

2. Go to Settings in the navigation menu.

3. Select Security > Secrets and variables > Actions.

   

4. Select New repository secret.

5. Paste the entire JSON output from the Azure CLI command into the secret's value field. Give the secret the name AZURE_CREDENTIALS.

6. Select Add secret.

OpenID Connect

You need to provide your application's Client ID, Tenant ID, and Subscription ID to the login action. These values can either be provided directly in the workflow or can be stored in GitHub secrets and referenced in your workflow. Saving the values as GitHub secrets is the more secure option.

1. In GitHub, go to your repository.

2. Select Security > Secrets and variables > Actions.

   

3. Select New repository secret.

4. Create secrets for AZURE_CLIENT_ID, AZURE_TENANT_ID, and AZURE_SUBSCRIPTION_ID. Use these values from your Microsoft Entra application for your GitHub secrets:

   GitHub secret	Microsoft Entra application
   AZURE_CLIENT_ID	Application (client) ID
   AZURE_TENANT_ID	Directory (tenant) ID
   AZURE_SUBSCRIPTION_ID	Subscription ID

5. Save each secret by selecting Add secret.

Step 3: Update setup.sh to connect to your Azure Machine Learning workspace

You'll need to update the CLI setup file variables to match your workspace.

1. In your cloned repository, go to azureml-examples/cli/.

2. Edit setup.sh and update these variables in the file.

   Variable	Description
   GROUP	Name of resource group
   LOCATION	Location of your workspace (example: eastus2)
   WORKSPACE	Name of Azure Machine Learning workspace

Step 4: Update pipeline.yml with your compute cluster name

You'll use a pipeline.yml file to deploy your Azure Machine Learning pipeline. This is a machine learning pipeline and not a DevOps pipeline. You only need to make this update if you're using a name other than cpu-cluster for your computer cluster name.

1. In your cloned repository, go to azureml-examples/cli/jobs/pipelines/nyc-taxi/pipeline.yml.
2. Each time you see compute: azureml:cpu-cluster, update the value of cpu-cluster with your compute cluster name. For example, if your cluster is named my-cluster, your new value would be azureml:my-cluster. There are five updates.

Step 5: Run your GitHub Actions workflow

Your workflow authenticates with Azure, sets up the Azure Machine Learning CLI, and uses the CLI to train a model in Azure Machine Learning.

Service principal

Your workflow file is made up of a trigger section and jobs:

• A trigger starts the workflow in the on section. The workflow runs by default on a cron schedule and when a pull request is made from matching branches and paths. Learn more about events that trigger workflows.
• In the jobs section of the workflow, you checkout code and log into Azure with your service principal secret.
• The jobs section also includes a setup action that installs and sets up the Machine Learning CLI (v2). Once the CLI is installed, the run job action runs your Azure Machine Learning pipeline.yml file to train a model with NYC taxi data.

Enable your workflow

1. In your cloned repository, open .github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml and verify that your workflow looks like this.

   name: cli-jobs-pipelines-nyc-taxi-pipeline
   on:
     workflow_dispatch:
     schedule:
       - cron: "0 0/4 * * *"
     pull_request:
       branches:
         - main
         - sdk-preview
       paths:
         - cli/jobs/pipelines/nyc-taxi/**
         - .github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml
         - cli/run-pipeline-jobs.sh
         - cli/setup.sh
   jobs:
     build:
       runs-on: ubuntu-latest
       steps:
       - name: check out repo
         uses: actions/checkout@v2
       - name: azure login
         uses: azure/login@v1
         with:
           creds: ${{secrets.AZURE_CREDENTIALS}}
       - name: setup
         run: bash setup.sh
         working-directory: cli
         continue-on-error: true
       - name: run job
         run: bash -x ../../../run-job.sh pipeline.yml
         working-directory: cli/jobs/pipelines/nyc-taxi
   

2. Select View runs.

3. Enable workflows by selecting I understand my workflows, go ahead and enable them.

4. Select the cli-jobs-pipelines-nyc-taxi-pipeline workflow and choose to Enable workflow.

5. Select Run workflow and choose the option to Run workflow now.

OpenID Connect

Your workflow file is made up of a trigger section and jobs:

• A trigger starts the workflow in the on section. The workflow runs by default on a cron schedule and when a pull request is made from matching branches and paths. Learn more about events that trigger workflows.
• In the jobs section of the workflow, you checkout code and log into Azure with the Azure login action using OpenID Connect.
• The jobs section also includes a setup action that installs and sets up the Machine Learning CLI (v2). Once the CLI is installed, the run job action runs your Azure Machine Learning pipeline.yml file to train a model with NYC taxi data.

Enable your workflow

1. In your cloned repository, open .github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml and verify that your workflow looks like this.

   name: cli-jobs-pipelines-nyc-taxi-pipeline
   on:
     workflow_dispatch:
     schedule:
       - cron: "0 0/4 * * *"
     pull_request:
       branches:
         - main
         - sdk-preview
       paths:
         - cli/jobs/pipelines/nyc-taxi/**
         - .github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml
         - cli/run-pipeline-jobs.sh
         - cli/setup.sh
   jobs:
     build:
       runs-on: ubuntu-latest
       steps:
       - name: check out repo
         uses: actions/checkout@v2
       - name: azure login
         uses: azure/login@v1
         with:
             client-id: ${{ secrets.AZURE_CLIENT_ID }}
             tenant-id: ${{ secrets.AZURE_TENANT_ID }}
             subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
       - name: setup
         run: bash setup.sh
         working-directory: cli
         continue-on-error: true
       - name: run job
         run: bash -x ../../../run-job.sh pipeline.yml
         working-directory: cli/jobs/pipelines/nyc-taxi
   

2. Select View runs.

3. Enable workflows by selecting I understand my workflows, go ahead and enable them.

4. Select the cli-jobs-pipelines-nyc-taxi-pipeline workflow and choose to Enable workflow.

   

5. Select Run workflow and choose the option to Run workflow now.

   

Step 6: Verify your workflow run

1. Open your completed workflow run and verify that the build job ran successfully. You'll see a green checkmark next to the job.

2. Open Azure Machine Learning studio and navigate to the nyc-taxi-pipeline-example. Verify that each part of your job (prep, transform, train, predict, score) completed and that you see a green checkmark.

   

Clean up resources

When your resource group and repository are no longer needed, clean up the resources you deployed by deleting the resource group and your GitHub repository.

Next steps

Create production ML pipelines with Python SDK