1. Traefik for Secure Ingress Control in AI Model Serving


    Traefik is a modern HTTP reverse proxy and load balancer that makes deploying microservices easy. To deploy Traefik for secure ingress control in a Kubernetes cluster, you’d typically use an Ingress or IngressRoute resource, along with the necessary configurations for secure communications (like TLS).

    In the context of AI model serving, a common approach is to deploy your model within a Kubernetes cluster and use Traefik as the ingress controller to manage incoming requests to your model's API. The ingress controller would be responsible for directing traffic to the correct service and ensuring secure HTTPS connections.

    For this task, I'll demonstrate how to use Pulumi to deploy an Ingress resource on a Kubernetes cluster that integrates with Traefik for secure HTTPS traffic. The following program will set up:

    1. A basic deployment of a sample application that we'll use to simulate your AI model API.
    2. A Service to expose the deployment within the Kubernetes cluster.
    3. An Ingress resource that specifies the ingress rules and uses Traefik to manage traffic to the service.

    To secure ingress traffic, we would be using Ingress with TLS support, assuming you have Traefik installed on your Kubernetes cluster. Here's a Pulumi program that illustrates this:

    import pulumi import pulumi_kubernetes as k8s # Configuring the Kubernetes provider (assuming you have set up your kubeconfig) # No explicit provider configuration is required if the `KUBECONFIG` environment # variable is set or your config is at the default `~/.kube/config` location. # Assuming that a namespace for your serving application exists. # You can create one using Pulumi if needed. namespace_name = 'ai-serving-namespace' # Using a Deployment to simulate your AI Model serving application. app_labels = {"app": "ai-model-serving"} deployment = k8s.apps.v1.Deployment( "ai-model-serving-deployment", metadata=k8s.meta.v1.ObjectMetaArgs( namespace=namespace_name, labels=app_labels, ), spec=k8s.apps.v1.DeploymentSpecArgs( replicas=1, selector=k8s.meta.v1.LabelSelectorArgs(match_labels=app_labels), template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs(labels=app_labels), spec=k8s.core.v1.PodSpecArgs( containers=[k8s.core.v1.ContainerArgs( name="ai-model-serving", image="your_ai_model_serving_image", # Replace with your actual image ports=[k8s.core.v1.ContainerPortArgs(container_port=80)], )], ), ), )) # Creating a Service to expose the AI Model serving deployment within the cluster. service = k8s.core.v1.Service( "ai-model-serving-service", metadata=k8s.meta.v1.ObjectMetaArgs( namespace=namespace_name, ), spec=k8s.core.v1.ServiceSpecArgs( selector=app_labels, ports=[k8s.core.v1.ServicePortArgs( port=80, # Port for the service target_port=80, # Container port to send traffic to )], )) # Creating an Ingress resource that uses Traefik to manage traffic, along with the necessary TLS configuration. ingress = k8s.networking.v1.Ingress( "ai-model-serving-ingress", metadata=k8s.meta.v1.ObjectMetaArgs( namespace=