1. Real-time AI Inference Serving with Kubernetes


    To set up real-time AI inference serving with Kubernetes, we'll create a Kubernetes-based infrastructure including a Deployment and a Service. The Deployment will host our AI model in a containerized application, and the Service will provide a stable endpoint that clients can connect to for inference requests.

    Here's how we'll approach this:

    1. Set up a Kubernetes Deployment that will run our AI inference application. This will contain a specification for the container image that includes our model and serving code.
    2. Configure a Kubernetes Service to expose the Deployment to the outside world. This service will balance the load and provide an entry point to our application.
    3. Use annotations and labels to make the deployment and service manageable and discoverable.

    The following program demonstrates how to set up such an infrastructure with Pulumi using Python.

    The program includes:

    • Importing necessary Pulumi modules for Kubernetes
    • Defining a Deployment that runs a hypothetical AI inference container image
    • Defining a Service to expose the Deployment
    • Exporting the endpoint for accessing the AI inference service

    Let's assume you're using an AI model container image that listens on port 8080 for inference requests.

    Here is the detailed Pulumi Python program:

    import pulumi import pulumi_kubernetes as kubernetes # Define the Kubernetes Deployment for the AI inference server. ai_inference_deployment = kubernetes.apps.v1.Deployment("aiInferenceDeployment", spec=kubernetes.apps.v1.DeploymentSpecArgs( replicas=2, # We'll start with 2 replicas for high availability selector=kubernetes.meta.v1.LabelSelectorArgs( match_labels={"app": "ai-inference"}, # This label will be used to match against the service ), template=kubernetes.core.v1.PodTemplateSpecArgs( metadata=kubernetes.meta.v1.ObjectMetaArgs( labels={"app": "ai-inference"}, ), spec=kubernetes.core.v1.PodSpecArgs( containers=[ kubernetes.core.v1.ContainerArgs( name="inference-container", image="your-repo/your-ai-model:v1.0.0", # Replace with your actual container image ports=[kubernetes.core.v1.ContainerPortArgs( container_port=8080, # The port that your inference service listens on )], ), ], ), ), )) # Define the Kubernetes Service to expose the AI inference Deployment. ai_inference_service = kubernetes.core.v1.Service("aiInferenceService", spec=kubernetes.core.v1.ServiceSpecArgs( selector={"app": "ai-inference"}, # Match against pods with this label type="LoadBalancer", # Use a LoadBalancer to expose the service externally ports=[kubernetes.core.v1.ServicePortArgs( port=80, # The service will be accessible over port 80 target_port=8080, # Target port on the container to forward to )], )) # Export the service's endpoint for accessing the AI inference application. # This will typically be a LoadBalancer IP or a public DNS name. pulumi.export("ai_inference_endpoint", ai_inference_service.status.apply(lambda status: status.load_balancer.ingress[0].ip))

    This program sets up a Kubernetes deployment and service aimed at serving an AI inference application in real-time. The program starts by importing the required Pulumi Kubernetes module. It then defines a deployment with two replicas, ensuring high availability. The deployment is tagged with labels that are used for the service to identify the appropriate pods to manage traffic.

    The deployment references a container image that you would have to replace with the one containing your AI model. It also specifies the port (8080 in this case) that the application inside the container is listening to.

    Next, we define a Kubernetes service called aiInferenceService. It is of type LoadBalancer, which means it will be assigned an external IP address or hostname that can be used to access the service from outside the Kubernetes cluster. The service's port (80) is what users will connect to, and this traffic will be routed to the target port (8080) on the deployed pods.

    Lastly, we export the endpoint of the AI inference service. This endpoint is the IP address assigned to the LoadBalancer service that external clients will use to interact with the inference service.

    Please replace "your-repo/your-ai-model:v1.0.0" with the actual container image for your AI model that is ready to serve inference requests. Furthermore, if your service runs on a different port, adjust the container_port as necessary.

    After deploying this Pulumi program with pulumi up, clients can send inference requests to the IP address exported as ai_inference_endpoint. The LoadBalancer will route the requests to one of the pods running the AI model, allowing for real-time inference.