1. Serverless Inference Services for Large Language Models with Cloud Run


    To deploy a serverless inference service for a large language model using Google Cloud Run, you will need a containerized application that can handle inference requests. Cloud Run is a managed compute platform that automatically scales your stateless containers. With Cloud Run, you can run your containers in a fully managed environment or in your own Google Kubernetes Engine cluster.

    Here, we'll outline the steps to deploy a serverless inference service to Cloud Run:

    1. Prepare the Inference Code: Write the code that uses your large language model to perform inference. This code will be packaged into a Docker container.

    2. Containerize the Application: Create a Dockerfile that defines how to build a Docker image of your application. Build the Docker image and push it to Google Container Registry (GCR).

    3. Deploy to Cloud Run: Create a Cloud Run service that references the Docker image stored in GCR. Configure the service to meet your scaling and request handling requirements.

    4. Expose the Service: By default, Cloud Run services are assigned a .run.app domain. You can connect your custom domain by creating a Cloud Run domain mapping.

    Here's a high-level Pulumi program that accomplishes this:

    import pulumi import pulumi_gcp as gcp # Define your GCR Docker image name docker_image_name = "gcr.io/project-id/language-model-inference" # Create a Cloud Run service inference_service = gcp.cloudrun.Service("language-model-service", location="us-central1", # specify the GCP region for your service template={ "spec": { "containers": [ { "image": docker_image_name, # Define environment variables, ports, etc. } ] } }) # Set IAM policy to allow unauthenticated user to access the service iam_policy = gcp.cloudrun.IamMember("service-iam", service=inference_service.name, location=inference_service.location, role="roles/run.invoker", member="allUsers") # be cautious with public access; restrict as necessary # Export the URL of the service pulumi.export("inference_service_url", inference_service.statuses["url"])

    In this program:

    • We import the necessary Pulumi modules.
    • We specify the Docker image we want to deploy. Replace project-id and language-model-inference with your actual Google Cloud project ID and the name of your Docker image.
    • We create a cloudrun.Service that defines the desired state of our service, such as the region and the container image it should use.
    • We create a cloudrun.IamMember resource to set the IAM policy for the service to allow unauthenticated access. Caution: Allowing allUsers means anyone can access your service. In a production environment, you should restrict access as necessary.
    • We export the URL of the deployed service, which can be used to interact with the inference service.

    Remember to replace "gcr.io/project-id/language-model-inference" with the path to your actual Docker image in GCR. Before running this program, ensure your Docker image is built and pushed to GCR, and you've enabled the Cloud Run API in your Google Cloud project.

    To run this Pulumi program:

    1. Ensure that you have Pulumi installed and have authenticated with Google Cloud.
    2. Save the program in a file named __main__.py.
    3. Run pulumi up in the same directory as your program file to deploy your Cloud Run service.

    This Pulumi code provides a basic template to get started with deploying a serverless inference service for large language models on Google Cloud Run. Depending on your specific needs, you might need to add more configuration details to your Cloud Run service, such as memory limits, environment variables, or VPC connections.