(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](https://github.com/ray-project/serve_config_examples). ## Step 1: Create a Kubernetes cluster with Kind ```sh kind create cluster --image=kindest/node:v1.23.0 ``` ## Step 2: Install KubeRay operator Follow [this document](kuberay-operator-deploy) 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 ```sh # Download `ray-service.mobilenet.yaml` curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.0.0-rc.0/ray-operator/config/samples/ray-service.mobilenet.yaml # Create a RayService kubectl apply -f ray-service.mobilenet.yaml ``` * The [mobilenet.py](https://github.com/ray-project/serve_config_examples/blob/master/mobilenet/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 ```sh 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](https://github.com/ray-project/serve_config_examples/blob/master/mobilenet/mobilenet_req.py) * Step 5.3: Send a request to the `ImageClassifier`. ```sh python mobilenet_req.py # sample output: {"prediction":["n02099601","golden_retriever",0.17944198846817017]} ```