(kuberay-mobilenet-rayservice-example)=

Serve a MobileNet image classifier on Kubernetes

Note: The Python files for the Ray Serve application and its client are in the repository ray-project/serve_config_examples.

Step 1: Create a Kubernetes cluster with Kind

kind create cluster --image=kindest/node:v1.26.0

Step 2: Install KubeRay operator

Follow this document to install the latest stable KubeRay operator from the Helm repository. Note that the YAML file in this example uses serveConfigV2. You need KubeRay version v0.6.0 or later to use this feature.

Step 3: Install a RayService

# Create a RayService
kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.1/ray-operator/config/samples/ray-service.mobilenet.yaml

The mobilenet.py file needs tensorflow as a dependency. Hence, the YAML file uses rayproject/ray-ml image instead of rayproject/ray image.
The request parsing function starlette.requests.form() needs python-multipart, so the YAML file includes python-multipart in the runtime environment.

Step 4: Forward the port for Ray Serve

# Wait for the RayService to be ready to serve requests
kubectl describe rayservice/rayservice-mobilenet
#  Conditions:
#   Last Transition Time:  2025-02-13T02:29:26Z
#   Message:               Number of serve endpoints is greater than 0
#   Observed Generation:   1
#   Reason:                NonZeroServeEndpoints
#   Status:                True
#   Type:                  Ready

# Forward the port for Ray Serve service
kubectl port-forward svc/rayservice-mobilenet-serve-svc 8000

Step 5: Send a request to the ImageClassifier

Step 5.1: Prepare an image file.
Step 5.2: Update image_path in mobilenet_req.py

Step 5.3: Send a request to the ImageClassifier.

python mobilenet_req.py
# sample output: {"prediction":["n02099601","golden_retriever",0.17944198846817017]}