1. Automated ML Model Training Pipelines in Azure DevOps


    To create an automated machine learning (ML) model training pipeline in Azure DevOps using Pulumi, we will set up an Azure DevOps project, define a Build pipeline that contains the steps to train an ML model, and create necessary service connections for accessing cloud resources. In our scenario, we will use Pulumi's azuredevops package to interact with the Azure DevOps services.

    Below is a Python program that demonstrates how to:

    1. Create an Azure DevOps project.
    2. Set up a Git repository in Azure DevOps for your ML code.
    3. Configure a service connection to Azure (if your training pipeline needs to interact with Azure services, such as Azure Machine Learning).
    4. Define a build pipeline YAML file that trains an ML model.
    5. Create a build definition in Azure DevOps that references the build pipeline YAML.

    First, we'll define the Azure DevOps project and repo:

    import pulumi import pulumi_azuredevops as azuredevops # Create an Azure DevOps project. project = azuredevops.Project("ml-project", description="Project for ML Model Training Pipeline", visibility="private", version_control="Git", work_item_template="Agile" ) # Initialize a Git repository within the Azure DevOps project. repo = azuredevops.Git("ml-repo", project_id=project.id, initialization=azuredevops.GitInitializationArgs( init_type="Clean" ) )

    Next, we would potentially define a service connection to interact with Azure resources (such as if deployment or training happens in Azure Machine Learning). In this example, the ServiceEndpointAzureRm is the resource representing the service connection to Azure Resource Manager. For simplicity, we assume service principal credentials are already provisioned:

    # Define the service connection to Azure Resource Manager (assuming you have service principal details). service_connection = azuredevops.ServiceEndpointAzureRm("ml-azure-service-connection", project_id=project.id, service_endpoint_name="AzureServiceConnection", credentials=azuredevops.ServiceEndpointAzureRmCredentialsArgs( service_principal_id="<service_principal_id>", service_principal_key="<service_principal_key>", tenant_id="<tenant_id>", subscription_id="<subscription_id>" ) )

    (Please replace <service_principal_id>, <service_principal_key>, <tenant_id>, and <subscription_id> with your actual Azure service principal credentials.)

    Now, we'll create a build definition that includes a pipeline for training an ML model. We typically define the steps of the pipeline in a YAML file stored in the Git repository. The exact steps will depend on the ML framework and tools you're using, but here's a hypothetical example:

    trigger: - main pool: vmImage: 'ubuntu-latest' steps: - script: echo "Start ML model training pipeline" displayName: 'Run a one-line script' - script: | echo "Add ML training steps here" # Example: # pip install -r requirements.txt # python train_model.py displayName: 'Train ML Model'

    In Pulumi, we would reference this YAML file to create a build definition:

    # Create a build definition for the ML training pipeline using the YAML file from the repository. build_definition = azuredevops.BuildDefinition("ml-build-definition", project_id=project.id, repository=azuredevops.BuildDefinitionRepositoryArgs( repo_id=repo.id, repo_type="TfsGit", yml_path="path/to/your/azure-pipelines.yml" # Path to the YAML file in the repository. ), agent_pool_name="Azure Pipelines", ci_trigger=azuredevops.BuildDefinitionCiTriggerArgs( use_yaml=True ) )

    Finally, we'll export the project and repository details for reference:

    pulumi.export('project_name', project.name) pulumi.export('repository_id', repo.id) pulumi.export('build_definition_id', build_definition.id)

    The above code sets up the basic structure of an Azure DevOps environment for an ML training pipeline. The actual training steps will need to be defined according to the specifics of your ML project. The YAML file should include steps to install dependencies, run training scripts, validate the model, and potentially deploy it or store the trained model artifacts.

    Azure DevOps pipelines provide a robust platform for automating these kinds of workflows, making it easier for teams to integrate ML model training into their CI/CD processes. With Pulumi, managing the infrastructure and configuration as code makes this process reproducible and version-controlled.