1. Managed Authentication for ML Services on Cloud Run


    To create managed authentication for Machine Learning (ML) services on Google Cloud Run, you will need to deploy a Cloud Run service and configure IAM policies to manage access and authentication to that service. Cloud Run services are scalable and you only pay for the resources you use. You can bring your own Docker container image to deploy your ML service.

    In the following Pulumi program, we'll create a Cloud Run service with an ML model container. We'll use the Google Cloud Run Service resource for deploying the actual service, along with the Cloud Run IAM Member resource to manage access via IAM roles and permissions.

    The program will include:

    • A Docker image containing an ML model. (For this example, let's assume you have a ready-to-use Docker image hosted in Google Container Registry (GCR) or Docker Hub).
    • A Cloud Run service to run this image.
    • IAM policies to restrict access to the Cloud Run service.

    Here's a detailed Pulumi program that deploys a Cloud Run service and configures its IAM roles:

    import pulumi import pulumi_gcp as gcp # Configuration variables for the Cloud Run deployment project = gcp.config.project location = gcp.config.region docker_image = 'gcr.io/my-project/my-ml-image' # Your ML docker image here service_name = 'my-ml-service' # Deploy a Cloud Run service ml_service = gcp.cloudrun.Service( "ml-service", location=location, project=project, template=gcp.cloudrun.ServiceTemplateArgs( spec=gcp.cloudrun.ServiceSpecArgs( containers=[ gcp.cloudrun.ServiceTemplateSpecContainerArgs( image=docker_image, ), ], ), ), ) # The email of the IAM service account associated with the Cloud Run service service_account_email = f"{service_name}@{project}.iam.gserviceaccount.com" # Give the Cloud Run invoker role to the designated user (could be a user email, service account, etc.) invoker_iam = gcp.cloudrun.IamMember( "invoker-iam", service=ml_service.name, location=location, project=project, role="roles/run.invoker", member="user:<user-email@example.com>", # Replace with the actual user email ) # Export the URL of the ML service pulumi.export("service_url", ml_service.statuses.apply(lambda s: s[0].url if s else None))

    In this program:

    • We're assuming you have a Docker image that contains your ML service, which is ready to be deployed.
    • We create a new Cloud Run service with that Docker image.
    • We create an IAM Member resource tied to the service that grants the roles/run.invoker role to the specified user, which allows that user to invoke the service. Replace <user-email@example.com> with the email address of the user who should have access.
    • Finally, we export the URL of the deployed service, which you can use to access your ML service.

    This program will configure your ML service to be accessible by the specified user, managing authentication via Google's Identity and Access Management (IAM) system. If you need to open it up to additional users or even the public, you would modify the member's role or add more IAM members accordingly.

    Remember to replace the docker_image placeholder with the actual path to your ML model's Docker image and the member with the identity you want to grant access to. For a service account, you'd use something like serviceAccount:my-invoker@my-project.iam.gserviceaccount.com.

    Ensure you've authenticated with GCP and set the required configurations before running this Pulumi program. You can do so by configuring your environment with the command gcloud auth login and gcloud config set project [YOUR_PROJECT_ID]. The Pulumi CLI will use your Google Cloud SDK configurations to authenticate your requests.