1. Distributed ML Workload Messaging with Pub/Sub

    Python

    To set up messaging for a distributed machine learning (ML) workload using Pub/Sub, you will need to create a Pub/Sub topic and a subscription. Google Cloud Pub/Sub offers a scalable messaging service that enables you to send and receive messages between independent applications. Here's what we'll do:

    1. Create a Pub/Sub Topic: A topic is a resource to which messages can be sent by publishers.
    2. Create a Pub/Sub Subscription: A subscription is a named resource representing the stream of messages from a single, specific topic, to be delivered to the subscribing application.

    I'll show you how to create a Pub/Sub topic and subscription using Pulumi with the gcp.pubsub.Topic and gcp.pubsub.Subscription resources from the Google Cloud provider.

    Here's the Pulumi program in Python:

    import pulumi import pulumi_gcp as gcp # Create a Pub/Sub topic for distributing ML workloads ml_topic = gcp.pubsub.Topic("mlTopic", name="ml-workload-topic") # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/pubsub/topic/ # Create a subscription to the ML workload Pub/Sub topic ml_subscription = gcp.pubsub.Subscription("mlSubscription", topic=ml_topic.name, ack_deadline_seconds=20) # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/pubsub/subscription/ # Export the names of the topic and subscription pulumi.export("topic_name", ml_topic.name) pulumi.export("subscription_name", ml_subscription.name)

    Here’s a step-by-step explanation:

    • We import the pulumi module, which is the Pulumi Package to define resources and the pulumi_gcp module, which contains the Google Cloud resources.

    • We create a Pub/Sub topic named ml-workload-topic. This is where messages related to the ML workload can be published.

    • We then create a subscription to this topic with an acknowledgment deadline of 20 seconds. This means that the subscriber has 20 seconds to acknowledge receipt of a message before the message is re-delivered.

    • Lastly, we export the names of the created topic and subscription. This is useful if you need to reference these resources in other parts of your infrastructure or when you need to interact with these resources using the GCP SDK or CLI.

    Remember to configure Pulumi to use your GCP credentials. Generally, this involves running gcloud auth application-default login to obtain the necessary credentials for Pulumi to work with Google Cloud resources. Your Pulumi stack also needs to be configured with the appropriate GCP project where the resources will be deployed.


GPT4o|Modeexplainbalancedcode