1. On-Demand Machine Learning Model Serving with Cloud Run


    To set up an on-demand machine learning model serving application with Google Cloud Run, you would go through several steps. Cloud Run is a managed compute platform provided by Google Cloud that is built from Knative, letting you run stateless containers that are invocable via web requests or Pub/Sub events.

    For a Machine Learning (ML) model to be served on demand, it needs to be:

    1. Containerized: The ML model and its serving logic (e.g., code to handle web requests, perform inference, etc.) should be packaged into a container image. This is usually done via a Dockerfile that defines the environment, dependencies, serving code, and the trained model itself.

    2. Deployed to a registry: The container image then needs to be pushed to a container registry from which Cloud Run can deploy it. Google provides a Container Registry but also supports using Docker Hub or other third-party registries.

    3. Deployed as a Service on Cloud Run: Once the image is in a registry, you can deploy it to Cloud Run by defining a service that specifies resources (like memory and CPU), environment variables, concurrency settings, etc. Cloud Run will automatically scale the number of container instances up or down based on demand, down to zero when there's no traffic, which is ideal for on-demand purposes.

    To put this to work with Pulumi, you'll be making use of the Pulumi Google Cloud provider (pulumi_gcp) to define the infrastructure needed to achieve this setup. Below I'm including a program that might represent a portion of the Pulumi program you'd use to deploy an ML model to Cloud Run:

    • gcp.cloudrun.Service: This resource is used to create and manage a Cloud Run service. You need to provide the location (region), the image location in the registry, and any relevant configurations specific to your application.

    Below is a Python Pulumi program that illustrates how you would define these resources for deploying an ML Model on Cloud Run. Please ensure you already have a container image of your ML model service pushed to Google Container Registry or any other container registry service before running this Pulumi code:

    import pulumi import pulumi_gcp as gcp # Replace 'DOCKER_IMAGE_URL' with the path to your container image in your registry. # For example: "gcr.io/project-id/image-name:tag" docker_image_url = "DOCKER_IMAGE_URL" # Define a Cloud Run service for machine learning model serving ml_service = gcp.cloudrun.Service("ml-model-service", location="us-central1", # Update to the region you prefer template=gcp.cloudrun.ServiceTemplateArgs( spec=gcp.cloudrun.ServiceTemplateSpecArgs( containers=[gcp.cloudrun.ServiceTemplateSpecContainerArgs( image=docker_image_url, resources=gcp.cloudrun.ServiceTemplateSpecContainerResourcesArgs( limits={"memory": "512Mi"} ) )], service_account_name="your-service-account@project-id.iam.gserviceaccount.com" # Replace with your service account ) ) ) # Optional: Map the service url for easy access pulumi.export('service_url', ml_service.statuses[0].url)

    Make sure you replace DOCKER_IMAGE_URL with the URL to your image in the container registry, and your-service-account@project-id.iam.gserviceaccount.com with your appropriate service account.

    This Pulumi program sets up the Cloud Run service pointing to a container that serves your ML model. The resources limit section is where you might customize the amount of memory allocated to match the needs of your model. Additionally, service accounts can be used to control access permissions for your service, and you can set environment variables to configure your container at runtime.

    To run this Pulumi program, you will need to run pulumi up inside your project directory where this file is saved. If this is the first time you're running Pulumi, you'll first need to install Pulumi and set up your Google Cloud project and authentication.

    Remember to ensure your environment has the required permissions for Pulumi to create and manage resources in your Google Cloud project. After successfully deploying this program with pulumi up, your service's URL will be outputted, and you can use this to send requests to your ML model, which will be processed on-demand by your containerized service.

    Would you need direction on any specific parts of this process, such as containerizing an ML model or pushing to a container registry? Or do you have further questions on the infrastructure code above?