1. Orchestrating AI Workloads with GCP Kubernetes and Container Registry


    Orchestrating AI workloads efficiently requires a robust container orchestration platform, and Google Kubernetes Engine (GKE) on Google Cloud Platform (GCP) is an excellent option for managing, deploying, and scaling containerized AI applications. Google Container Registry (GCR) allows you to store and manage your Docker container images securely and ensures that your container images are available to your Kubernetes clusters.

    Below, we'll write a Pulumi program in Python that showcases how to provision a GCP Kubernetes cluster using Pulumi's GCP provider, and how to create a Container Registry where you can push your AI workload images.

    In this program, we will:

    1. Import the necessary modules, which would generally include pulumi, pulumi_gcp for utilizing Google Cloud resources.
    2. Create a GCP Container Registry to store our Docker images.
    3. Provision a Kubernetes cluster by defining a GKECluster resource.
    4. Define the GCP project and the location (region) where the resources will be provisioned.

    Here's a Pulumi program that accomplishes this:

    import pulumi import pulumi_gcp as gcp # Configuration variables for the GCP project and location/region. # Ideal to set these via Pulumi configuration or environment variables. # gcp.config.project and gcp.config.zone can be set in the Pulumi configuration file (Pulumi.<stack-name>.yaml), # otherwise, you'll need to mention them explicitly in each resource. project = 'your-gcp-project-id' location = 'us-central1' # Create a Google Container Registry to store container images. # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/container/registry/ container_registry = gcp.container.Registry("ai-container-registry", project=project, location=location) # Provision a GCP Kubernetes cluster in the specified project and location. # In this resource, we're specifying the size (number of nodes), machine type, and the GCP zone where the cluster should be provisioned. # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/container/cluster/ kubernetes_cluster = gcp.container.Cluster("ai-kubernetes-cluster", initial_node_count=3, node_version="latest", min_master_version="latest", location=location, project=project, node_config=gcp.container.ClusterNodeConfigArgs( machine_type="n1-standard-1", oauth_scopes=[ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/service.management.readonly", "https://www.googleapis.com/auth/servicecontrol", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring", ], )) # Export the cluster name and the Container Registry URL for easy access. pulumi.export("kubernetes_cluster_name", kubernetes_cluster.name) pulumi.export("container_registry_url", container_registry.id.apply(lambda _: f"gcr.io/{project}/{container_registry.name}"))

    In the program above:

    • We create a GCP Container Registry named ai-container-registry, which will give us a URL where we can upload and manage our Docker images. Notice how we export the URL at the end, ensuring you can easily reference it for operations like docker push.

    • We define a Kubernetes cluster named ai-kubernetes-cluster. This cluster will have 3 nodes of type n1-standard-1, and it will have the necessary OAuth scopes to pull container images from GCR and use GCP's services such as compute, storage, and logging.

    To run this code, you'll need Pulumi installed and configured for GCP access. Deploying this program would require executing pulumi up, which prompts Pulumi to provision the resources as defined in the code.

    After the successful deployment, your Kubernetes cluster and Container Registry will be ready to use, and you can begin orchestrating your AI workloads. You would use kubectl to interact with your Kubernetes cluster, and docker alongside gcloud CLI tools to build and upload your Docker images to the Container Registry.

    Please replace your-gcp-project-id with your actual GCP project ID and us-central1 with your preferred GCP region. It is also highly recommended to customize the cluster configuration, like the node count and machine type, according to your AI workload requirements for production environments.