1. Auto-scaling Compute for AI Batch Jobs on Cloud Run


    Auto-scaling compute resources are ideal for workloads that may experience variable levels of demand. In the context of AI batch jobs, this could mean scaling up to accommodate a large number of job executions and then scaling down when the jobs have completed, optimizing cost and resource usage.

    Google Cloud Run is a managed compute platform that automatically scales your stateless containers. It is built on Knative, providing a serverless execution environment that can run in Google Kubernetes Engine, Anthos, or on Google's infrastructure.

    In this context, we'll assume your AI batch jobs have been containerized, and we will use Pulumi to define the required infrastructure using Google Cloud Run.

    Here's a step-by-step walkthrough for setting up an auto-scaling compute environment for AI batch jobs on Cloud Run:

    1. Define a google-native.run/v2.Service resource with the appropriate image for the AI jobs and configure the autoscaling settings in the template section.

    2. Provide a service account that the Cloud Run service will use with the roles necessary to perform the tasks needed by your AI batch jobs.

    3. Apply IAM bindings to the service to ensure the right levels of access.

    For this demonstration, we'll deploy a simple placeholder container image, gcr.io/google-samples/hello-app:1.0, as a stand-in for your AI batch jobs container image.

    Now, let's dive into the Python program using Pulumi to configure this environment:

    import pulumi import pulumi_google_native as google_native project = 'your-gcp-project' location = 'us-central1' # Choose the right region for you service_name = 'ai-batch-job-service' container_image = 'gcr.io/google-samples/hello-app:1.0' # Replace with the path to your container image # Define the service on Cloud Run cloud_run_service = google_native.run.v2.Service( service_name, project=project, location=location, template=google_native.run.v2.ServiceTemplateArgs( spec=google_native.run.v2.ServiceTemplateSpecArgs( # Autoscaling parameters # Configure according to the needs of your job template=google_native.run.v2.ServiceTemplateTemplateArgs( spec=google_native.run.v2.RevisionTemplateSpecArgs( containers=[ google_native.run.v2.ContainerArgs( image=container_image, ), ], scaling=google_native.run.v2.RevisionScalingArgs( max_instance_count=10, # Max number of instances for auto-scaling ), ), ), ), ), ) # Output the URL of the deployed service pulumi.export('service_url', cloud_run_service.statuses.apply(lambda status: status.url))

    The cloud_run_service resource in the program sets up Cloud Run to manage and auto-scale your AI batch jobs. We specified the maximum number of instances as 10 for demonstration purposes, but you should adjust this to meet your application requirements.

    Once you deploy this Pulumi stack, your Cloud Run service will be ready to receive requests that trigger your AI batch jobs. Cloud Run will automatically scale the number of container instances based on demand. When the demand drops, Cloud Run will scale back down the instances, ensuring you only pay for the compute resources your jobs actually use.

    This example assumes that you have a Docker container that encapsulates your AI batch job which is stored in a container registry that Cloud Run can access. In real-world usage, you would replace the container_image with the path to your actual container image.

    You would also want to configure other settings according to the nature of your AI batch jobs, such as memory and CPU allocation, request timeout, concurrency, and potentially connecting to other GCP resources such as Cloud Storage, Pub/Sub, or databases which is not covered in this basic example.

    Before running this Pulumi code, make sure you have authenticated with GCP and set up your Pulumi stack with the required settings for your GCP project.