1. Continuous Delivery of AI Models with GKE and BackendConfig


    To set up continuous delivery of AI models using Google Kubernetes Engine (GKE) and BackendConfig, we will go through a basic Pulumi program which outlines the steps required to:

    1. Create a GKE cluster where the AI models will be deployed.
    2. Define a BackendConfig, a custom resource provided by the Kubernetes Ingress-GCE controller which allows users to provide configurations to the Google Cloud load balancer.

    The GKE cluster will serve as the environment where your AI models run as services. The BackendConfig resource will specify configurations that the load balancer serving the AI services should apply.

    Let's walk through the creation of a GKE cluster and setting up a BackendConfig:


    Before starting, make sure you have the following prerequisites met:

    • Pulumi is installed and configured with your GCP credentials.
    • You have a project setup in GCP where resources will be deployed.

    Step 1: Define the GKE Cluster

    First, we'll create a GKE cluster which is the central piece of infrastructure where your containers will be scheduled, managed, and run.

    Step 2: BackendConfig for Ingress Customization

    For the ingress customization, we'll define a BackendConfig. This is used to specify configurations like custom health checks, security settings, and session affinity.

    Now, let’s look at a Pulumi program written in Python that performs the above tasks:

    import pulumi import pulumi_gcp as gcp # Define the GKE cluster. gke_cluster = gcp.container.Cluster("ai-models-cluster", initial_node_count=1, node_version="latest", min_master_version="latest", node_config={ "machine_type": "n1-standard-1", "oauth_scopes": [ "https://www.googleapis.com/auth/cloud-platform", ], } ) # Define a Kubernetes provider instance that uses our GKE cluster. k8s_provider = gcp.Provider("k8s-provider", kubeconfig=gke_cluster.endpoint.apply( lambda endpoint: gke_cluster.master_auth.apply( lambda auth: f""" apiVersion: v1 clusters: - cluster: certificate-authority-data: {auth.cluster_ca_certificate} server: https://{endpoint} name: gke-cluster contexts: - context: cluster: gke-cluster user: gke-cluster name: gke-cluster current-context: gke-cluster kind: Config preferences: {{}} users: - name: gke-cluster user: client-certificate-data: {auth.client_certificate} client-key-data: {auth.client_key} token: {auth.token} """ ) )) # Define the BackendConfig. backend_config = gcp.compute.BackendService("backend-config", backends=[{ "group": pulumi.Output.all(gke_cluster.instance_group_urls.apply(lambda groups: groups[0])), }], health_checks=[health_check_self_managed.id], opts=pulumi.ResourceOptions(provider=k8s_provider) ) # Output the GKE cluster endpoint. pulumi.export('gke_cluster_endpoint', gke_cluster.endpoint)

    In the program above:

    • We define a gke_cluster which specifies the desired settings for the node pool and Kubernetes version.
    • A Kubernetes provider k8s_provider instance is created that uses the GKE cluster's endpoint and credentials obtained from the master_auth attribute.
    • A BackendService resource named backend-config is created. It refers to the backend services that the Google Cloud load balancer uses to distribute traffic.
    • We export the endpoint of the GKE cluster so that you can interact with your cluster after it’s up and running.

    This is a simplified example to get you started. In a real-world scenario, you might want to configure more specific settings for your cluster, such as autoscaling, networking, or advanced cluster configurations.

    Remember, setting up continuous delivery involves more steps, such as setting up your CI/CD pipeline to automatically deploy container images to your GKE cluster upon successful build tests. This could involve services like Google Cloud Build or Jenkins, and may use additional Pulumi resources related to your chosen CI/CD system.