1. Federated Learning Workflows Coordinated by GKE Hub


    To orchestrate Federated Learning workflows using Google Kubernetes Engine (GKE) Hub, you'll need to set up various GKE Hub features which allow clusters to communicate with each other and coordinate operations. This requires setting up a couple of things:

    1. GKE Hub Membership: This registers your Kubernetes clusters with the GKE Hub, which allows you to use other GKE Hub features.
    2. Fleet: Represents a group of clusters that are treated as a unit for the purpose of multi-cluster management.
    3. Features: These enhance your cluster with extra functionality. Some standard features are 'configmanagement' for multi-cluster config syncing, or 'servicemesh' for adding a service mesh across multiple clusters.

    Below is a Pulumi program in Python to create these resources using the Google Cloud provider. This program assumes you have already configured the Pulumi CLI with the appropriate credentials and default Google Cloud project and location settings.

    Make sure to install the required Pulumi Google Cloud provider package before running this program:

    $ pip install pulumi-google-native

    Here is the Pulumi Program followed by an explanation:

    import pulumi import pulumi_google_native.gkehub.v1alpha1 as gkehub # Create a Fleet resource representing a group of clusters. fleet = gkehub.Fleet("my-fleet", project=pulumi.config.Require("gcp:project"), # Your Google Cloud Project ID location=pulumi.config.Require("gcp:location"), # Location for the resource display_name="FleetDisplayName" # Display name for the fleet ) # Register a GKE cluster to the Hub as a Membership. membership = gkehub.Membership("my-membership", project=pulumi.config.Require("gcp:project"), location=pulumi.config.Require("gcp:location"), endpoint=gkehub.MembershipEndpointArgs( gke_cluster=gkehub.MembershipEndpointGkeClusterArgs( resource_link="//container.googleapis.com/projects/my-project/locations/us-central1-a/clusters/my-cluster" ), ), external_id="external-id-for-my-membership", # Unique identifier for the membership display_name=pulumi.Input("MyMembership"), # Display name for the membership description=pulumi.Input("My GKE Cluster Membership") ) # Create a Feature for multi-cluster Ingress. feature = gkehub.Feature("my-feature", project=pulumi.config.Require("gcp:project"), location=pulumi.config.Require("gcp:location"), spec=gkehub.FeatureSpecArgs( multiclusteringress=gkehub.FeatureSpecMulticlusteringressArgs( # Use "PREMIUM" for the paid tier or "STANDARD" for the standard tier config_membership=pulumi.Input("projects/my-project/locations/global/memberships/my-membership"), ) ) ) # Export the Fleet and Membership unique IDs pulumi.export("fleet_id", fleet.id) pulumi.export("membership_id", membership.id)


    • We first declare a Fleet resource named "my-fleet". It is a logical grouping of Kubernetes clusters for multi-cluster management using GKE Hub. We specify the project ID and the location along with a display name for the fleet.

    • Next, we create a Membership resource to register a GKE cluster with the GKE Hub. This membership is tied to a specific cluster which we reference by its GCP resource link. We also provide an external_id which is a unique identifier for this membership, and a display name and description.

    • We then create a Feature resource to enable multi-cluster ingress, which lets you set up a single external IP address to route traffic to multiple clusters. We attach the feature to the membership we created earlier and set the configuration for the multi-cluster ingress.

    • The pulumi.export statements at the end output the unique ID of the Fleet and Membership resources created. This might be useful for referencing in other parts of your infrastructure or CI/CD pipelines.

    This program gives you a starting point for coordinating Federated Learning workflows across multiple clusters, as GKE Hub's features can be used to set up consistent networking, configuration, and access policies across a fleet of clusters. You would then deploy your Federated Learning applications on the individual clusters and use the features enabled by the GKE Hub to coordinate the workflows.