1. Automated Training Workflows with Azure DevOps Pipelines


    Automating training workflows can be a powerful way to ensure consistent, repeatable processes for machine learning or other computational tasks. In the context of cloud infrastructure with Azure DevOps Pipelines, you would generally set up a pipeline that responds to code changes, starts running the training tasks, manages the resources, and possibly deploys the trained model.

    To accomplish this, you would set up Azure DevOps Projects and Pipelines, which could include tasks like fetching code from a repository, running training scripts, and storing artifacts such as trained models. Below is a Python program using Pulumi to create resources for an automated training workflow in Azure DevOps.

    This Pulumi Python program does the following:

    • Creates an Azure DevOps project using the azuredevops.Project resource.
    • Configures an Azure Pipeline within the project, using the azure-native.devops.Pipeline resource.
    • Sets up various Service Endpoints to connect external services or tools to Azure DevOps, like a Git repository, container registry, or others using resources like azuredevops.ServiceEndpointGit, etc. (specific resources depend on the external services you require for your training workflows).

    Let's proceed with the program:

    import pulumi import pulumi_azuredevops as azuredevops # Create an Azure DevOps Project # The project will contain all your pipelines, repositories, and other DevOps resources. devops_project = azuredevops.Project("training-project", description="Project for automated training workflows", visibility="private", work_item_template="Agile", version_control="Git", ) # Define a pipeline for the project # This pipeline can be configured to perform various training tasks. The actual definition can be imported # from an existing YAML pipeline file in your repository which defines the steps for training. pipeline = azuredevops.BuildDefinition("training-pipeline", project_id=devops_project.id, repository={ "repo_type": "AzureReposGit", "repo_id": "<Your repository ID here>", # Replace with your repository ID "branch_name": "main", "yml_path": "azure-pipelines.yml", # Path to the pipeline definition file }, ) # Define ServiceEndpoints if needed # ServiceEndpoints connect your pipeline to external services and tools, like GitHub, Docker registry, # or others that are required for your training workflow to function. # Example of a GitHub service endpoint, replace parameters with appropriate values github_service_endpoint = azuredevops.ServiceEndpointGitHub("github-connection", project_id=devops_project.id, service_endpoint_name="GitHubConnection", auth_personal={ "personal_access_token": "<Your GitHub token here>", # Replace with your GitHub token }, ) # Example of a Docker registry service endpoint, replace parameters with appropriate values docker_service_endpoint = azuredevops.ServiceEndpointDockerRegistry("docker-registry-connection", project_id=devops_project.id, service_endpoint_name="DockerRegistryConnection", docker_registry_connection={ "registry_type": "Others", "docker_registry": "<Your Docker registry URL here>", # Replace with your Docker registry URL "docker_username": "<Your Docker username here>", # Replace with your Docker username "docker_email": "<Your Docker email here>", # Replace with your Docker email "docker_password": "<Your Docker password here>", # Replace with your Docker password }, ) # Exports # Output the created project name and pipeline ID. It can be useful for further automations # or integrations with other systems. pulumi.export('devops_project_name', devops_project.name) pulumi.export('pipeline_id', pipeline.id)

    To use the above program, you need to have Pulumi and the Azure DevOps extension installed and configured. The placeholders in the code, such as <Your repository ID here> or <Your GitHub token here>, need to be replaced with actual values corresponding to your setup.

    Moreover, ensure that the YAML pipeline file (azure-pipelines.yml in the example) is created within the repository with the correct steps for your training task. This file dictates the sequence of jobs, steps, and tasks that run during your pipeline execution, such as setting up environments, running training scripts, and storing outputs.

    Remember, for tasks such as training machine learning models, you might have to specify additional resources, like GPU-enabled virtual machines, storage accounts for your datasets and models, or connections to data sources, all of which can be automated and provisioned using Pulumi in a similar fashion as the resources above.

    Once your code is ready, running pulumi up in your Pulumi project directory will create the Azure DevOps resources specified in your program. After the resources are created, you'll be able to see your project and pipelines within Azure DevOps and begin automating your training workflows.