1. Scalable API Endpoints for LLMs on GCP Cloud Run.


    Creating scalable API endpoints for Language Learning Models (LLMs) on GCP Cloud Run involves setting up a Cloud Run service that can handle requests to your model. Cloud Run is a fully managed platform that enables you to run stateless containers that are invocable via HTTP requests. It is scalable by design because it automatically adjusts the number of container instances based on the number of incoming requests.

    Here’s a step-by-step process to create scalable API endpoints for LLMs on GCP Cloud Run using Pulumi:

    1. Containerization: Package your LLM application into a container image and push it to a registry like Google Container Registry (GCR).

    2. Cloud Run Service: Define a Cloud Run service in Pulumi that references the image and includes configuration for authentication, memory limits, etc.

    3. IAM Policy: Optionally, set Identity and Access Management (IAM) policies if you wish to restrict access to the API.

    Below is a Pulumi program in Python that sets up such a Cloud Run service:

    import pulumi import pulumi_gcp as gcp # Project and location are typically configured through `gcp:project` and `gcp:region` configuration settings. # Assuming gcp provider is configured for the correct project and region/zone already. project = gcp.config.project location = gcp.config.region # The name of your container image in Google Container Registry container_image = "gcr.io/my-project/my-llm-api:v1" # Define the Cloud Run service cloud_run_service = gcp.cloudrun.Service("my-llm-api-service", location=location, template=gcp.cloudrun.ServiceTemplateArgs( spec=gcp.cloudrun.ServiceSpecArgs( containers=[gcp.cloudrun.ServiceSpecContainerArgs( image=container_image, resources=gcp.cloudrun.ServiceSpecContainerResourcesArgs( # Setting a higher limit for CPU and memory depending on workload limits={"cpu": "2000m", "memory": "2Gi"} ), )], # You could include additional configuration such as environment variables here ), ), traffic=[gcp.cloudrun.ServiceTrafficArgs( percent=100, type="MANAGED", )] ) # Obtain the URL of the deployed Cloud Run service cloud_run_url = pulumi.Output.all(cloud_run_service.statuses).apply(lambda statuses: statuses[0].url) # IAM policy for the Cloud Run service to allow unauthenticated access # Remove or modify this section for allowing only authenticated accesses. iam_policy = gcp.cloudrun.IamPolicy("my-llm-api-iam-policy", location=location, project=project, service=cloud_run_service.name, binding=gcp.cloudrun.IamPolicyBindingArgs( role="roles/run.invoker", members=["allUsers"] # Allows unauthenticated calls ) ) # Export the Cloud Run service URL so it can be easily accessed after deployment pulumi.export('cloud_run_url', cloud_run_url)

    Breakdown of Resources:

    • pulumi_gcp.cloudrun.Service: This resource is used to define and manage a Cloud Run service. It includes configurations such as the container image, resource limits, and traffic settings.

    • pulumi_gcp.cloudrun.IamPolicy: If needed, you can set IAM policies for the service. In the above script, we allow unauthenticated users to invoke the service, but you can customize the policy according to your needs.

    • pulumi.export: This function is used to output the URL of the deployed Cloud Run service so you can easily access it.

    When you run the Pulumi program, it will use the Google Cloud SDK configuration. Make sure you have authenticated with Google Cloud (gcloud auth login) and set the correct project and region (gcloud config set project YOUR_PROJECT_ID and gcloud config set run/region YOUR_REGION).

    The provided program assumes that you've already containerized your LLM application and pushed the image to Google Container Registry (GCR). Please replace "gcr.io/my-project/my-llm-api:v1" with the actual path to your container image.

    After deploying your Cloud Run service with Pulumi, the application will automatically scale up or down based on the load, making it an excellent fit for APIs that receive variable traffic.