1. ML Model Inference Endpoint Management with Ingress


    In the context of Kubernetes and cloud infrastructure, ML model inference typically involves serving a machine learning model as an API. Clients can then make requests to this API to receive predictions based on the input data they provide. To achieve this, we'll create a Kubernetes deployment that serves the model using a containerized application, like TensorFlow Serving, PyTorch Serve, or a custom solution. Then, we'll use a Kubernetes Service to expose this deployment within the cluster, and an Ingress resource to manage external access to the endpoint.

    Here's an overview of the Pulumi resources used in this example:

    • Deployment: This represents a set of multiple, identical pods with no unique identities. In this context, it runs the container that serves our ML model.
    • Service: An abstract way to expose an application running on a set of Pods as a network service. With Kubernetes, you don't need to modify your application to use an unfamiliar service discovery mechanism - Kubernetes gives Pods their own IP addresses and a single DNS name for a set of Pods and can load-balance across them.
    • Ingress: An API object that manages external access to the services in a cluster, typically HTTP. Ingress can provide load balancing, SSL termination, and name-based virtual hosting.

    Below is a Pulumi program written in Python that sets up these resources for managing an ML model inference endpoint with Ingress in Kubernetes:

    import pulumi import pulumi_kubernetes as k8s # Create a Kubernetes Deployment to serve your ML model ml_model_deployment = k8s.apps.v1.Deployment( resource_name='ml-model-deployment', spec={ 'selector': { 'matchLabels': { 'app': 'ml-model' } }, 'replicas': 1, # Number of replicas you'd like, e.g., for availability and scaling 'template': { 'metadata': { 'labels': { 'app': 'ml-model' } }, 'spec': { 'containers': [{ 'name': 'model-container', 'image': 'YOUR_MODEL_SERVING_IMAGE', # Replace with your container image # You may need to define ports, env vars, volumes, resource requests/limits, etc. 'ports': [{'containerPort': 8501}], # Update the port if necessary }], }, }, } ) # Create a Kubernetes Service to point to the ML model pods ml_model_service = k8s.core.v1.Service( resource_name='ml-model-service', spec={ 'selector': { 'app': 'ml-model' }, 'ports': [{ 'port': 8501, # The port your container is listening on 'targetPort': 8501 # The targetPort must match the container port }] } ) # Create an Ingress to route external HTTP traffic to the ML model Service ml_model_ingress = k8s.networking.v1beta1.Ingress( resource_name='ml-model-ingress', spec={ 'rules': [{ 'http': { 'paths': [{ 'path': '/predict', # The path to route to the service, e.g., "/predict" 'backend': { 'serviceName': ml_model_service.metadata['name'], 'servicePort': 8501 }, }] } }] } ) # Export the Ingress IP to access the ML model inference endpoint from outside pulumi.export('ingress_ip', ml_model_ingress.status['load_balancer']['ingress'][0]['ip'])

    Make sure to replace 'YOUR_MODEL_SERVING_IMAGE' with the appropriate Docker image for your ML model serving, and update the ports if necessary. The path /predict and port 8501 are commonly used, but they should be updated according to your specific configurations.

    This script sets up a single replica of your ML model serving application in a Deployment, a Service to allow network access to your application within the cluster, and an Ingress to manage external HTTP traffic to your ML model Service. The Ingress resource in this script is very basic and may need additional configuration for production use, such as setting up TLS for secure HTTP connections.