1. Multi-tenant AI Platforms with Keycloak Integration on Kubernetes


    Creating a multi-tenant AI platform with Keycloak integration on Kubernetes involves setting up several components, including the Kubernetes cluster itself, the application platform, and the Keycloak service for authentication and authorization. Integrating these components ensures that each tenant's data and resources are isolated while providing a centralized user management system via Keycloak.

    The following Pulumi program in Python illustrates how you might set up such a platform. The program assumes you have already configured your Pulumi environment to interact with your desired Kubernetes provider and that the necessary Pulumi provider packages have been installed.

    We'll go through the following steps:

    1. Deploying a Kubernetes cluster (this step will depend on your cloud provider).
    2. Setting up Keycloak as an authentication service.
    3. Deploying a sample multi-tenant AI platform application which would integrate with Keycloak for user management.

    Here is a high-level overview of the Pulumi Python program:

    import pulumi import pulumi_kubernetes as k8s import pulumi_keycloak as keycloak # Step 1: Create a Kubernetes cluster # Note: This will vary depending on your cloud provider and existing infrastructure. # For example, if you are using AWS, you would use the `pulumi_aws` package to create an EKS cluster. # Placeholder for Kubernetes cluster creation code... # Step 2: Deploy Keycloak on Kubernetes # We will deploy Keycloak in a new namespace for simplicity and isolation. keycloak_namespace = k8s.core.v1.Namespace("keycloak-namespace", metadata={"name": "keycloak"}) keycloak_deployment = k8s.apps.v1.Deployment( "keycloak-deployment", metadata={ "namespace": keycloak_namespace.metadata["name"] }, spec={ "selector": {"matchLabels": {"app": "keycloak"}}, "replicas": 1, "template": { "metadata": {"labels": {"app": "keycloak"}}, "spec": { "containers": [{ "name": "keycloak", "image": "quay.io/keycloak/keycloak:latest", "env": [ # Add necessary environment variables for Keycloak configuration. ], "ports": [{"containerPort": 8080}], }] } } }) # Step 3: Deploy a sample multi-tenant AI platform application # This will vary widely depending on the actual application you want to deploy. # The example below is a placeholder for your actual application deployment code. # Placeholder for AI platform application deployment code... # Export any necessary outputs, such as the Keycloak URL, which might be needed by users. keycloak_service = k8s.core.v1.Service( "keycloak-service", metadata={ "namespace": keycloak_namespace.metadata["name"] }, spec={ "type": "LoadBalancer", "selector": {"app": "keycloak"}, "ports": [{"port": 8080}] }) # Using `pulumi.Output.all()` to combine outputs from different resources and create the final Keycloak URL. keycloak_url = pulumi.Output.all(keycloak_service.status["load_balancer"]["ingress"][0]["ip"], keycloak_service.spec["ports"][0]["port"]).apply( lambda args: f"http://{args[0]}:{args[1]}") pulumi.export('keycloak_url', keycloak_url)

    Let me detail the steps undertaken in the above program:

    • Step 1: The Kubernetes cluster creation is abstracted away, as this would depend on your cloud provider. You would use services like AWS EKS, Azure AKS, or Google GKE, which have corresponding Pulumi packages (pulumi_aws, pulumi_azure_native, pulumi_gcp) for provisioning.

    • Step 2: We deploy Keycloak in a new Kubernetes namespace for organization and security purposes. Here, a Deployment manages the Keycloak pods, working based on a Docker image provided by Keycloak. We also expose Keycloak via a Service of type LoadBalancer to make it accessible outside the cluster. You would need to tailor the environment variables according to your Keycloak configuration needs.

    • Step 3: This would be where you deploy the actual AI platform. Since the specifics of this are not provided, this section only contains a placeholder comment. You must insert the necessary deployments here—these would potentially be a series of deployments, each representing a component of your multi-tenant AI platform (e.g., data processing services, machine learning model servers), all configured to work with Keycloak for authentication and multi-tenancy.

    The last part of the program exports the URL for the deployed Keycloak, which you would use to access the Keycloak administration console and configure realms, clients, users, etc.

    Please note that this program is a high-level template. The actual implementation of your AI platform services, as well as specifics about how you integrate them with Keycloak, would require more detailed consideration based on your requirements.