1. Kubernetes Cluster Federation for Geo-Distributed AI Workloads


    Kubernetes Cluster Federation, often referred to as "Federation v2," is a method of connecting multiple Kubernetes clusters and coordinating workloads across them. It is designed to handle deployment of applications across different clusters optimizing for locality, low latency or high availability.

    Creating a Federation involves a few high-level steps:

    1. Setting up the Federated Clusters: This requires having multiple Kubernetes clusters up and running, each in a different geographical location to achieve geo-distribution.

    2. Deploying the Federation Control Plane: Usually, this involves installing a Federation control plane in one of the clusters, which will manage the federated resources.

    3. Joining Clusters to the Federation: Clusters must be registered with the Federation control plane so that they can be managed as a part of the Federation.

    4. Deploying Federated Resources: You define federated resources (e.g., Federated Deployment, Federated Service, etc.) that the control plane will then synchronize across the registered clusters based on the policy you define (which can include spreading instances evenly, concentrating in a specific region, and more).

    For instance, a geo-distributed AI workload might use Federation to deploy training jobs to clusters nearest the data sources to minimize latency, or to clusters with spare compute capacity.

    Using Pulumi, we can programmatically define and manage each of these resources. Although setting up a full Federation is beyond a single snippet of code, we can get you started with the creation of multiple Kubernetes clusters across different regions.

    Below, I will illustrate how to create two Google Kubernetes Engine (GKE) clusters in different regions using Pulumi Python. One could be used for training AI models, and the other could be used for serving the trained models.

    The following Pulumi program will:

    • Create two GKE clusters, one in us-west1 and the other in europe-west1.
    • These clusters will then be able to be federated by setting up multi-cluster communication.

    Please note that this example does not set up the Federation control plane or federated resources itself but creates the necessary Kubernetes clusters that you would federate.

    import pulumi import pulumi_gcp as gcp # Define a function to create a GKE cluster in a given location. def create_gke_cluster(name: str, location: str): # Create a GKE cluster cluster = gcp.container.Cluster(name, initial_node_count=1, node_config={ "machineType": "n1-standard-1", "oauthScopes": [ "https://www.googleapis.com/auth/compute", "https://www.googleapis.com/auth/devstorage.read_only", "https://www.googleapis.com/auth/logging.write", "https://www.googleapis.com/auth/monitoring" ], }, location=location ) return cluster # Create the GKE cluster for training AI models in the US West region. training_cluster = create_gke_cluster("training-cluster", "us-west1") # Create the GKE cluster for serving AI models in the Europe West region. serving_cluster = create_gke_cluster("serving-cluster", "europe-west1") # Export the clusters' details pulumi.export("training_cluster_name", training_cluster.name) pulumi.export("serving_cluster_name", serving_cluster.name) pulumi.export("training_cluster_endpoint", training_cluster.endpoint) pulumi.export("serving_cluster_endpoint", serving_cluster.endpoint)

    In the code above, we define a function create_gke_cluster that encapsulates the logic for creating a GKE cluster. We then call this function twice to create two clusters in the specified locations.

    To use this program:

    1. Ensure you have Pulumi and the Google Cloud SDK installed with gcloud configured for the correct account and project.

    2. Save the code in a file named __main__.py.

    3. Run pulumi up to deploy the clusters.

    4. Once you have the clusters up and running, you could proceed to install a Federation control plane and join these clusters into the Federation.

    Make sure you define your AI workload requirements so you can appropriately size your clusters, specify necessary permissions, and configure other settings such as network policies or storage options.