1. Workflow Orchestration for AI Pipelines using Cloud Tasks


    To orchestrate workflows for AI pipelines using Cloud Tasks in Google Cloud Platform, we'll create a Cloud Tasks queue and then add tasks to it. These tasks can represent different steps in your AI pipeline, such as data preprocessing, training models, and inference.

    We'll follow these steps in the Pulumi program:

    1. Create a Cloud Tasks queue: This queue serves as a management system that receives tasks and dispatches them to worker services for execution.
    2. Add tasks to the Cloud Tasks queue: Each AI pipeline step is represented as a task with a unique HTTP request. When a task is added to the queue, Cloud Tasks will make an HTTP request to the worker service responsible for that step of the pipeline.

    Let's begin by creating a Cloud Tasks queue.

    import pulumi import pulumi_gcp as gcp # Set up a GCP project and location for Cloud Tasks resources. project = 'my-gcp-project' location = 'us-central1' # Create a Cloud Tasks queue. queue = gcp.cloudtasks.Queue("aiPipelineQueue", project=project, location=location, # Set any rate limits or retry configurations as needed. rate_limits=gcp.cloudtasks.QueueRateLimitsArgs( max_dispatches_per_second=10 ), retry_config=gcp.cloudtasks.QueueRetryConfigArgs( max_attempts=5, min_backoff="1s", max_backoff="10s" )) pulumi.export('queue_name', queue.name)

    The above code defines a queue with rate limits and retry configurations that suit typical AI pipeline needs. We've exported the queue name for reference, as this information will be necessary when appending tasks to the queue.

    Next, let's add a dummy task to the queue. We assume that an HTTP-triggered Cloud Function or Cloud Run service is already set up to handle the task requests.

    # Add a task to the Cloud Tasks queue. task = gcp.cloudtasks.Task("exampleTask", queue=queue.name, project=project, location=location, # Define the HTTP request for this task. http_request=gcp.cloudtasks.TaskHttpRequestArgs( http_method="POST", url="https://example-service-ai-step.com/taskhandler", # Provide authorization token if required. oauth_token=gcp.cloudtasks.TaskHttpRequestOauthTokenArgs( service_account_email="ai-pipeline-sa@my-gcp-project.iam.gserviceaccount.com" ), # Body and headers can be specified if needed. headers={"Content-Type": "application/json"}, body="YOUR_BASE64_ENCODED_BODY" ), # Configure schedule time or other parameters as needed. schedule_time=pulumi.Output.none()) pulumi.export('task_name', task.name)

    In this part, we create a task that makes a POST request to a specified URL with optional authorization, headers, and body. Replace YOUR_BASE64_ENCODED_BODY with the actual payload encoded in base64 format as required by Cloud Tasks.

    For actual AI pipelines, you might encode the step details, like input data locations or model parameters, in the task's body so the worker service knows what to do. Keep in mind that task handlers (e.g., Cloud Functions or Cloud Run services) need to be implemented to process these tasks and take appropriate actions in the AI pipeline.

    The above program demonstrates workflow orchestration for AI pipelines using Cloud Tasks. You would have to scale this up based on the complexity of your AI pipeline, adding error handling and more sophisticated retry logic as needed.

    Remember to replace my-gcp-project with your actual GCP project ID, and https://example-service-ai-step.com/taskhandler with the endpoint of the service that will handle task execution. Also, set up IAM permissions accordingly so that Cloud Tasks can invoke the task handler service on your behalf.