1. Async Batch Processing for Machine Learning with Cloud Tasks


    Asynchronous batch processing is a common pattern in machine learning and other compute-intensive tasks. It involves processing large volumes of data without requiring the input/output operations to be done in real-time, which allows more flexibility in resource usage and can lead to cost savings.

    To implement this pattern, you would set up a system where tasks are queued for processing, and a fleet of worker instances process these tasks from the queue. With Cloud Tasks, you get a fully managed service that allows you to manage the execution, dispatch, and delivery of a large number of distributed tasks.

    To demonstrate, we'll build an infrastructure with Pulumi that includes:

    1. A Cloud Tasks Queue in Google Cloud Platform (GCP) for queueing tasks.
    2. A Google Cloud Function acting as the worker, which will receive tasks from the queue and perform the batch processing.
    3. IAM Policies to define the permissions for the queue and the cloud function to interact securely.

    First, we'll need to set up a Google Cloud Function that represents a worker. For simplicity, this example uses a basic HTTP function stub, but in a real-world scenario, you would replace this with your own machine learning batch processing function.

    import pulumi import pulumi_gcp as gcp # Step 1: Define the Cloud Function that will process our tasks # In a real-world application, this function would carry out the machine learning tasks - for the sake of example, we're keeping it simple. machine_learning_function = gcp.cloudfunctions.Function("machineLearningFunction", source_archive_bucket=gcp.storage.Bucket("source-bucket").name, runtime="python37", # Assuming Python 3.7 for our Cloud Function entry_point="handler", # The name of the entry point function trigger_http=True, # We create an HTTP-triggered function available_memory_mb=256) # Set the memory needed for the function # After deploying your function, you can use the function URL to trigger it. pulumi.export('function_url', machine_learning_function.https_trigger_url) # Step 2: Set up the Cloud Tasks Queue # We define a queue where tasks will be sent before being processed by our worker function task_queue = gcp.cloudtasks.Queue("taskQueue", location="us-central1", # Specify the location for the queue ) # Step 3: Set up IAM Policies to allow Cloud Tasks to create Tasks in the queue # This policy allows Cloud Tasks to invoke our Cloud Function gcp.cloudtasks.QueueIamPolicy("queueIamPolicy", queue=task_queue.name, bindings=[ gcp.cloudtasks.QueueIamPolicyBindingArgs( role="roles/cloudtasks.enqueuer", members=[f"serviceAccount:{machine_learning_function.service_account_email}"] ), ]) # Note: The actual implementation of submitting tasks to the queue and # processing them is beyond the scope of this infrastructure setup. # You would typically use a client library for Google Cloud Tasks to create # tasks, and in the Cloud Function, you'd write the code to process these tasks asynchronously.

    In the above program:

    • A Google Cloud Function is set up to act as the 'worker' that would process our tasks. It's an HTTP-triggered function which, in a real application, would take a task from the queue and perform some processing.
    • A Cloud Tasks Queue is created where the tasks will be stored and dispatched.
    • IAM policies are configured to ensure that the Cloud Tasks service can interact with the Cloud Function securely.

    To deploy this, you would need Pulumi installed and configured for your GCP account. You would save this code into a __main__.py file, run pulumi up, and Pulumi would handle provisioning these resources in your GCP account.

    For this infrastructure to function effectively for batch processing, you would:

    • Submit tasks to the Cloud Tasks queue using the Google Cloud client libraries in whatever system is generating your tasks.
    • Implement the processing logic in the Google Cloud Function (handler function in this example) to handle the tasks from the queue.

    This pattern can effectively decouple the task submission from the processing, allowing for flexibility in processing intensive tasks such as those commonly found in machine learning workloads.