1. Scalable API Serving for LLMs with Cloud Run


    To serve a scalable API for large language models (LLMs) using Google Cloud Run, you'll create a cloud service that can automatically scale the number of instances based on the incoming request load. Cloud Run is a managed platform that enables you to run stateless containers that are invocable via web requests.

    Here's a brief overview of the steps involved:

    1. Containerize the LLM API: Create a Docker container image that includes the code for your LLM API. The container should listen for HTTP requests and be stateless in nature.

    2. Upload the Container to Google Container Registry (GCR): Push the Docker image to GCR so that it can be deployed to Cloud Run.

    3. Deploy to Cloud Run: Using the pushed image in GCR, deploy your API to Cloud Run, which will handle the instantiation, routing, scaling, and management of your containers.

    4. Configure IAM Permissions (if necessary): Assign the correct IAM roles and permissions to allow your Cloud Run service to interact with other Google Cloud resources, if needed.

    5. Testing and Revision: Test your deployment to ensure it's serving requests as expected. You may need to create new revisions with different configurations based on your needs.

    Below is a Pulumi Python program that models this deployment. The program assumes you've already built your Docker image and are ready to deploy it to Cloud Run.

    import pulumi import pulumi_gcp as gcp # Replace with the name of your Google Cloud project and the location for your Cloud Run deployment. project_name = 'your-gcp-project' location = 'us-central1' # Replace with the URL to your container image. container_image_url = 'gcr.io/your-gcp-project/your-llm-api-image' # Cloud Run Service Creation service_name = 'llm-api-service' cloud_run_service = gcp.cloudrunv2.Service(service_name, project=project_name, location=location, template=gcp.cloudrunv2.ServiceTemplateArgs( containers=[gcp.cloudrunv2.ServiceTemplateContainersArgs( image=container_image_url, resources=gcp.cloudrunv2.ServiceTemplateContainersResourcesArgs( limits={'memory': '1Gi', 'cpu': '1000m'}) # Configure based on your LLM's requirements. )], scaling=gcp.cloudrunv2.ServiceTemplateScalingArgs( max_instance_count=100, # Configure the maximum number of instances. min_instance_count=1, # Configure the minimum number of instances. ), ), autogenerate_revision_name=True, # Automatically generate revision names. traefik=gcp.cloudrunv2.ServiceTraefikDefaultArgs( allow_unauthenticated=True, # Allow unauthenticated access to your API. ), ) # IAM configuration to bind roles to the service account used by Cloud Run service (if needed). service_iam_policy = gcp.cloudrun.ServiceIamPolicy(f'iam-policy-{service_name}', project=project_name, location=location, service=cloud_run_service.name, bindings=[{ 'role': 'roles/run.invoker', # Role that allows invoking the Cloud Run service. 'members': ['allUsers'], # You can configure more precise access control if needed }], ) # Export the URL of the Cloud Run service for easy access. pulumi.export('cloud_run_service_url', cloud_run_service.statuses[-1].url)

    This Pulumi program:

    • Defines a Cloud Run Service resource to host the containerized LLM API.
    • Specifies the desired scaling behavior such as the maximum and minimum instance count.
    • Allows for easy revisions and rollback by autogenerating revision names.
    • Sets IAM permissions to allow unauthenticated access to the API endpoint (this can be tailored based on your security requirements).
    • Exports the resulting Cloud Run service URL for easy access.

    Remember that after creating a Pulumi program, you run pulumi up to deploy your infrastructure. Pulumi translates your program into the necessary cloud resource configurations and provisions them in the specified cloud provider.

    Make sure to replace placeholder values such as your-gcp-project and gcr.io/your-gcp-project/your-llm-api-image with actual values relevant to your project and container image hosted in Google Container Registry.

    Note: The specifics of your LLM API such as the allocated memory ('1Gi') and CPU ('1000m') resources, instance counts, and IAM roles may change based on your actual requirements and security guidelines.