1. Continuous Integration for AI Workflows with Azure Container Registry


    To create a continuous integration (CI) workflow for AI applications using Azure Container Registry (ACR), you would need several components:

    1. Azure Container Registry: It is a managed Docker registry service based on the open-source Docker Registry 2.0. It's used for storing and managing container images, but it also integrates well with orchestrators like Kubernetes, and Azure services for CI/CD, such as Azure Pipelines.

    2. Agent Pool: A pool of agents where you can run your containerized jobs on Azure. This is necessary for setting up the environment where your CI pipeline will execute.

    3. Webhook: ACR webhooks allow you to trigger actions in response to events happening in your container registry, like pushing a new image. This is useful to automate tasks, for example, triggering a deployment when a new image is available.

    4. Service Principal: To access Azure services programmatically, you create a service principal, which is an identity created for use with applications, hosted services, and automated tools to access Azure resources.

    Below is a Pulumi program written in Python which sets up a simple CI workflow for an AI application using Azure Container Registry:

    import pulumi import pulumi_azure_native as azure_native # Set up an Azure resource group resource_group = azure_native.resources.ResourceGroup('ai-workflow-rg') # Create an Azure Container Registry acr = azure_native.containerregistry.Registry( 'acregistry', resource_group_name=resource_group.name, sku=azure_native.containerregistry.SkuArgs( name='Standard' ), admin_user_enabled=True, # Admin user enabled for simplicity, consider more secure options for production location=resource_group.location ) # Configure an agent pool for running the CI tasks agent_pool = azure_native.containerregistry.AgentPool( 'aicirunners', resource_group_name=resource_group.name, location=resource_group.location, registry_name=acr.name, os='Linux', tier='S1', # Choose a tier that matches your expected workload and scaling requirements count=1, # Start with one agent, scale out as necessary ) # Create a webhook for triggering events webhook = azure_native.containerregistry.Webhook( 'acwebhook', resource_group_name=resource_group.name, registry_name=acr.name, location=resource_group.location, service_uri='https://example.com/webhook', # URI for your webhook endpoint (adjust to actual listener) status='Enabled', scope='myrepository:mytag', # Adjust the scope to match the repository and tag to watch for events actions=['push'] # Configure the actions that will trigger the webhook, in this case, a push event ) # Export the login server for the created container registry pulumi.export('registry_login_server', acr.login_server) # Export the agent pool id pulumi.export('agent_pool_id', agent_pool.id) # To use the ACR, you can now push Docker images to this registry, # set up a CI pipeline to build images on push events, or run tasks directly in the agent pool.

    This program sets up the necessary resources for a continuous integration flow using Azure Container Registry. It creates a new container registry that will store your Docker images. It also sets up an agent pool with an instance to run CI tasks and a webhook to listen for push events on your specified repository and tag.

    After the registry is created, you'll need to push Docker images to it, typically within your CI pipeline. When new images are pushed, the webhook can be used to trigger any kind of automation you need, like updating services running those images or running integration tests.

    To improve the security of your CI setup, you can add additional configurations like network restrictions, private endpoints, and manage the access keys for your registry.

    To use this Pulumi program:

    1. Install Pulumi and set up the Azure command-line tools.
    2. Configure the Pulumi Azure Native provider by setting appropriate environment variables with your Azure credentials.
    3. Save the above program in a file with a .py extension, e.g., setup_acr.py.
    4. Run pulumi up to deploy your infrastructure.

    Important Notes:

    • Replace https://example.com/webhook with the actual listener endpoint you wish to use to handle the webhook events.
    • In the scope parameter of the webhook, specify the specific repository and tag you wish to monitor.
    • admin_user_enabled is set to True for simplicity, but in a production environment, it's recommended to use more secure authentication methods.