1. Scaling ML Model Serving with Kubernetes Deployments


    To scale machine learning (ML) model serving with Kubernetes, you typically want to deploy your model as a service within a Kubernetes cluster. The model serving service will be packaged in a container, and Kubernetes Deployments will manage the replicas of this container to scale up or down based on demand.

    Here's an overview of the steps we'll take in the code:

    1. Define the Container: The container should have the machine learning model and the server (e.g., a Flask app) that responds to the inference requests.
    2. Create a Kubernetes Deployment: The deployment will manage our containers across multiple replicas for redundancy and scalability.
    3. Expose the Deployment: We will use a Kubernetes Service to expose our model serving API to be accessible over the network.

    It's important to note that you need to have Docker images of your applications, pushed to a container registry that your Kubernetes cluster can access.

    Below is the Pulumi program written in Python that sets up the necessary resources. I'll explain each part in detail.

    import pulumi import pulumi_kubernetes as k8s # Configurations for our ML serving service app_name = "ml-model-serving" image = "your-docker-image" # Replace with your machine learning model's Docker image replica_count = 3 # Start with 3 replicas, you can adjust this based on your needs container_port = 8080 # The port that your model server listens on service_port = 80 # The port that the Kubernetes Service will expose service_type = "LoadBalancer" # Use "LoadBalancer" for cloud environments or "ClusterIP" for internal-only # Create a Kubernetes Deployment for our ML model serving ml_deployment = k8s.apps.v1.Deployment( app_name, spec=k8s.apps.v1.DeploymentSpecArgs( replicas=replica_count, selector=k8s.meta.v1.LabelSelectorArgs( match_labels={"app": app_name} ), template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs( labels={"app": app_name} ), spec=k8s.core.v1.PodSpecArgs( containers=[k8s.core.v1.ContainerArgs( name=app_name, image=image, ports=[k8s.core.v1.ContainerPortArgs( container_port=container_port )] )] ) ) )) # Expose the Deployment as a Service to receive traffic ml_service = k8s.core.v1.Service( app_name, spec=k8s.core.v1.ServiceSpecArgs( selector={"app": app_name}, ports=[k8s.core.v1.ServicePortArgs( port=service_port, target_port=container_port )], type=service_type )) # Export the URL of the Service to access the ML model serving API pulumi.export('ml_model_serving_url', ml_service.status.load_balancer.ingress[0].hostname)

    This program does the following:

    • The ml_deployment resource creates a Deployment in Kubernetes to manage multiple replicas of the ML model serving container. It allows Kubernetes to handle the scaling and redundancy of the model serving service by adjusting replica_count.
    • The ml_service resource exposes the Deployment. We map the service port (80 by default) to the container port (8080 as defined) and create the service of type LoadBalancer. This makes the service accessible over the internet if your Kubernetes cluster supports LoadBalancers.
    • Finally, we export the URL endpoint for the service. It allows us to easily find where we can send requests to perform inference using the deployed ML model.

    Make sure to replace "your-docker-image" with the appropriate Docker image for your ML model.

    Before running this code with Pulumi, ensure you have configured your Kubernetes provider and that you have the necessary permissions to deploy resources to the cluster.