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6: Model Deployment - Deploy on OpenShift Cluster

Objective

In this lab, we will:

  • deploy the containerized model API onto OpenShift cluster
  • create necessary Kubernetes resources (i.e deployments, services, and routes)

ensuring the model is accessible and scalable in a production-like environment.

Guide

Step 1 - Go to the OpenShift Console

Find your project (e.g. user1) in the openshift console. 💡 Note: Please make sure that you are in your given project.

Step 2 - Create a Deployment Using the Built Image

From the left panel go to Workloads -> Deployment and create a new deployment.

💡 Note One: Please set the link to the image from the last task. It could also be found in the created ImageStream.

💡 Note Two: Make sure that you set the correct values for the environment variables (e.g. MLFLOW_TRACKING_URI, MODEL_NAME, MODEL_VERSION) and replace the placeholders.

💡 Note Three: MODEL_VERSION is already set to 1, but if you have more that 1 version and you want to deploy that, please change the value for variable MODEL_VERSION accordingly.

💡 Note Four: MLFLOW_TRACKING_URI is the same server you have set in step 3 & 4.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: bike-model-api
  labels:
    app: bike-model-api
spec:
  replicas: 1
  selector:
    matchLabels:
      app: bike-model-api
  template:
    metadata:
      labels:
        app: bike-model-api
    spec:
      containers:
        - name: bike-model-container
          image: IMAGE_URL_FROM_IMAGE_STREAM  # It can be found on the imagestream page in OpenShift
          ports:
            - containerPort: 8000
          env:
            - name: MLFLOW_TRACKING_URI
              value: "MLFLOW_REMOTE_TRACKING_SERVER"
            - name: MODEL_NAME
              value: "MODEL_NAME" # f"BikeSharingModel_{PARTICIPANT_FIRSTNAME}"
            - name: MODEL_VERSION
              value: "1"  # Or leave it empty for latest Production version
          resources:
            requests:
              cpu: "250m"
              memory: "256Mi"
            limits:
              cpu: "500m"
              memory: "512Mi"

Step 3 - Service: Expose the API Endpoint internally

Create a service to expose your API internally for applications on the same cluster:

From the left panel go to Network -> Service and create a new service. 

apiVersion: v1
kind: Service
metadata:
  name: bike-model-api-svc
spec:
  selector:
    app: bike-model-api
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8000
  type: ClusterIP

Now, the model is reachable only from inside the cluster!

Step 4 - Route (Optional): Expose the API Endpoint externally

Deploy this resource ONLY and ONLY if you want to make your model accessible outside the OpenShift cluster.

From the left panel go to Network -> Route and create a new route.

apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: bike-model-api-route
spec:
  port:
    targetPort: 8000 
  to:
    kind: Service
    name: bike-model-api-svc
  tls:
    termination: edge  # or passthrough, depending on your needs
    insecureEdgeTerminationPolicy: Redirect  # Redirect HTTP to HTTPS

✅ Now with a model to deploy, we see how to prepare the deployment files and dependencies in a container, in the next exercise Model Deployment - Testing Model Endpoint-API.