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, which is supported by KubeRay version v0.6.0 and later.

Step 3: Install a RayService#

# Download `ray-service.mobilenet.yaml`
curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.2.2/ray-operator/config/samples/ray-service.mobilenet.yaml

# Create a RayService
kubectl apply -f ray-service.mobilenet.yaml

  • The mobilenet.py file requires tensorflow as a dependency. Hence, the YAML file uses rayproject/ray-ml:2.5.0 instead of rayproject/ray:2.5.0.

  • python-multipart is required for the request parsing function starlette.requests.form(), so the YAML file includes python-multipart in the runtime environment.

Step 4: Forward the port for Ray Serve#

kubectl port-forward svc/rayservice-mobilenet-serve-svc 8000

Note that the Serve service is created after the Ray Serve applications are ready and running. This process may take approximately 1 minute after all Pods in the RayCluster are running.

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]}