Scalable API Serving for LLMs with Cloud Run

Question

Pulumi · Accepted Answer

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.

```python
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.*