1. Running Multi-tenant AI Services with ORY Hydra on Kubernetes


    To run multi-tenant AI services with ORY Hydra on Kubernetes, you need to set up a Kubernetes cluster, configure it to handle multiple tenants, and deploy ORY Hydra to handle OAuth 2.0 and OpenID Connect which is commonly used for user authorization and authentication in modern applications.

    For the purpose of this example, we will assume that you are deploying this on Azure Kubernetes Service (AKS) as it provides managed Kubernetes clusters that simplify the deployment and management of your containerized applications.

    Here's an outline of the steps we will cover in the Pulumi program:

    1. Set up an AKS cluster
    2. Configure the cluster with necessary role-based access control (RBAC) for multi-tenancy
    3. Deploy ORY Hydra on Kubernetes as a service within the cluster

    Let's go through the steps in detail:

    Step 1: Set Up an Azure Kubernetes Service (AKS) Cluster

    We are using the ManagedCluster resource from the azure-native.containerservice module to create a fully managed Kubernetes cluster on Azure. This resource helps us to specify a range of configurations including the node count, node size, and more.

    import pulumi from pulumi_azure_native import containerservice, resources # Create an Azure Resource Group resource_group = resources.ResourceGroup('rg') # Create an Azure AKS cluster managed_cluster = containerservice.ManagedCluster( "aksCluster", resource_group_name=resource_group.name, agent_pool_profiles=[{ "count": 2, "vm_size": "Standard_DS2_v2" }], dns_prefix="oryhydra-aks" ) # Export the kubeconfig kubeconfig = pulumi.Output.all(resource_group.name, managed_cluster.name).apply( lambda args: containerservice.list_managed_cluster_user_credentials(resource_group_name=args[0], resource_name=args[1]) ).apply( lambda creds: creds.kubeconfigs[0].value.decode('utf-8') ) pulumi.export('kubeconfig', kubeconfig)

    Step 2: Configure RBAC for Multi-Tenancy

    In a real-world multi-tenant scenario, you would have more complex RBAC setups based on the needs of each tenant. However, for this example, we'll just set up a simple RBAC role for demonstration purposes.

    # This step would typically involve setting up Kubernetes RBAC roles and # role bindings depending on your specific multi-tenancy requirements. # Please consider consulting the Kubernetes RBAC documentation or a # Kubernetes administrator for tailored setup.

    Step 3: Deploy ORY Hydra on Kubernetes

    This part of the code would deploy ORY Hydra to the AKS cluster you've set up. This typically involves creating Kubernetes deployments, services, and possibly ingress controllers. ORY Hydra's documentation provides a Helm chart that simplifies this deployment.

    from pulumi_kubernetes import Provider, helm # Create a Kubernetes provider instance for the AKS cluster k8s_provider = Provider( 'k8sProvider', kubeconfig=kubeconfig ) # Deploy ORY Hydra using its Helm chart hydra_chart = helm.v3.Chart( 'ory-hydra', helm.v3.ChartOpts( chart='hydra', version='1.10.2', fetch_opts=helm.v3.FetchOpts( repo='https://k8s.ory.sh/helm/charts' ) ), opts=pulumi.ResourceOptions(provider=k8s_provider) )

    Note: You might need to customize ORY Hydra according to your needs, such as setting up persistence, configuring the ingress to expose ORY Hydra outside of the cluster, and such.

    In summary, the code provided will:

    • Provision an Azure Kubernetes cluster where ORY Hydra will be deployed.
    • Set the stage for configuring Kubernetes RBAC for a multi-tenant setup.
    • Deploy ORY Hydra to the Kubernetes cluster using a Helm chart, which will manage user authentication and authorization for your AI services.

    This setup would form the backbone of running a secure and scalable multi-tenant AI service platform. Please note that implementing a multi-tenant architecture requires careful planning and a deep understanding of Kubernetes RBAC to isolate resources and maintain security across different tenants.