1. Containerized Model Deployment with GCP Container Registry


    To deploy a containerized model with Google Cloud Platform (GCP) using Pulumi, you would need to perform a series of steps that typically involve:

    1. Containerizing your model: Packaging your machine learning model along with any necessary code and dependencies into a Docker container.

    2. Pushing the container image to a registry: Uploading your Docker container image to a container image registry—GCP Container Registry in this case.

    3. Deploying the image to a service: Deploying the image from the registry to a compute service like Google Kubernetes Engine (GKE) or Cloud Run where it can be run and managed.

    The following Pulumi program in Python illustrates how you can perform steps 2 and 3, assuming that you have already containerized your model (your-model-image:tag) and have it locally on your machine:

    import pulumi import pulumi_gcp as gcp # Create a new GCP project and enable the required services. project = gcp.organizations.Project('model-deployment-project', name='model-deployment-project') gcp.services.enable('containerregistry.googleapis.com', project=project.project_id) # Pulumi creates a default GCP registry in the format gcr.io/<PROJECT-ID>/<IMAGE-NAME> image_name = 'model-deployment-image' # Use your model's image and tag for the container image. container_image = gcp.container.RegistryImage('model-deployment-image', local_path=pulumi.FileAsset('path/to/your/model-container.tar'), image_name=f'gcr.io/{project.project_id}/{image_name}') # You can deploy the image to GKE or Cloud Run, depending on your requirements. # For deployment to Cloud Run, uncomment the following code. cloud_run_service = gcp.cloudrun.Service('model-deployment-service', location='us-central1', template=gcp.cloudrun.ServiceTemplateArgs( spec=gcp.cloudrun.ServiceTemplateSpecArgs( containers=[gcp.cloudrun.ServiceTemplateSpecContainerArgs( image=container_image.image_name )], service_account_name=gcp.service_account.Account('service-account', account_id='model-deploy-account', display_name='Model Deployment Service Account').email ) )) # Output the URL of the deployed model. pulumi.export('deployed_model_url', cloud_run_service.statuses[0].url)

    In this program:

    • We've enabled the containerregistry.googleapis.com service which is required to use the GCP Container Registry.
    • We've specified a RegistryImage named model-deployment-image that is built from path/to/your/model-container.tar. This path should be replaced with the path to the tarball of your containerized image.
    • For the deployment, we've chosen to use Cloud Run (cloudrun.Service), which is a managed compute platform that automatically scales your containerized applications.
    • We've also created a service account (service_account.Account) which will be attributed to the Cloud Run service for proper permissions.
    • The final URL where the model is being served will be exported as a stack output.

    Please replace the local_path in RegistryImage with the actual path to your containerized model's tarball. Also, ensure you have the necessary permissions and that your project ID is correctly referred to within the program.

    You'll need to have Pulumi and gcloud CLI tools installed and configured with appropriate credentials for this program to work. Once saved to a file (e.g., main.py), you can run pulumi up within the same directory to deploy your infrastructure.