1. Distributed AI Workloads with Pub/Sub Messaging Patterns


    To set up distributed AI workloads with Pub/Sub messaging patterns, we'll need a distributed system capable of handling large-scale messaging and concurrent processes. This setup typically involves a messaging service that allows different components of the AI system to publish and subscribe to messages efficiently.

    Google Cloud Platform (GCP) offers a service called Pub/Sub, which is a scalable and flexible messaging service that allows you to send and receive messages between independent applications. For workloads requiring AI, you can use other GCP services like AI Platform, Compute Engine, or Kubernetes Engine to run your AI models and workloads. Here, messages published to a Pub/Sub topic can trigger events or compute processes that are part of your AI workload.

    In the Pulumi program below, I will create:

    1. A Pub/Sub topic that will be used to publish messages.
    2. A subscription to that topic that AI services can listen to and process messages from.
    3. A simple Cloud Function triggered by the Pub/Sub topic for demonstration (in a production environment, this would be replaced by your AI workload, which would be more complex).

    Before running this code, ensure that you have authenticated your local environment with GCP and set the desired project and region using gcloud CLI.

    Let's write the Pulumi program:

    import pulumi import pulumi_gcp as gcp # Create a Google Cloud Pub/Sub topic where messages will be published topic = gcp.pubsub.Topic("ai-workloads-topic", name="ai-workloads-topic") # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/pubsub/topic/ # Create a subscription to the topic. # AI workload services will listen for messages on this subscription. subscription = gcp.pubsub.Subscription("ai-workloads-subscription", name="ai-workloads-subscription", topic=topic.name) # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/pubsub/subscription/ # A simple Cloud Function to represent an AI workload processing messages from the Pub/Sub topic. # In a real scenario, this could be a service running AI models and performing tasks on receipt of messages. cloud_function = gcp.cloudfunctions.Function("ai-workloads-function", name="ai-workloads-function", runtime="python39", entry_point="main", # This should correspond to the function in your Python file that handles events. source_archive_bucket=gcp.storage.Bucket("cloud-function-source-bucket").name, source_archive_object=gcp.storage.BucketObject("source-archive-object", bucket="cloud-function-source-bucket", source=pulumi.FileArchive("./source-code.zip") # Replace "./source-code.zip" with the path to your function's source archive. ).name, trigger_http=False, event_trigger=gcp.cloudfunctions.FunctionEventTriggerArgs( event_type="providers/cloud.pubsub/eventTypes/topic.publish", resource=topic.id, )) # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/cloudfunctions/function/ # Export the URLs where the function can be triggered. pulumi.export("topic_name", topic.name) pulumi.export("subscription_name", subscription.name) pulumi.export("cloud_function_name", cloud_function.name)

    In the code above:

    • We create a Topic which is where messages will be published.
    • We define a Subscription that is attached to the topic. This will allow our downstream AI workloads to receive messages published to this topic.
    • We simulate an AI workload with a Function, which will be triggered by messages published to our topic. The actual AI model execution code would be part of the source code you package as a .zip file and upload to the cloud storage bucket.

    The pulumi.export lines are used to output the important details of our resources, such as the Pub/Sub topic name, subscription name, and Cloud Function name. This information is useful for integrating these components with the rest of your system.

    To complete this program, you'd need to replace ./source-code.zip with the actual path to your Cloud Function source code archive which should include a Python file with a main function that would be the entry point for your workload.

    This is a simple example. In practice, for distributed AI workloads, you might have more complex infrastructures involving multiple services, message filtering, dead-letter queues for handling undeliverable messages, and using Pub/Sub message attributes to route messages dynamically.