MobileNet example

docs/guidance/mobilenet-rayservice.md

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+# RayService: MobileNet example
+
+> **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](../../helm-chart/kuberay-operator/README.md) to install the latest stable KubeRay operator via Helm repository.
+
+## Step 3: Install a RayService
+
+```sh
+# path: ray-operator/config/samples/
+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 of Serve
+
+```sh
+kubectl port-forward svc/rayservice-mobilenet-serve-svc 8000
+```
+
+Note that the Serve service will be created after the 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]}
+  ```

ray-operator/config/samples/ray-service.mobilenet.yaml

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+apiVersion: ray.io/v1alpha1
+kind: RayService
+metadata:
+  name: rayservice-mobilenet
+spec:
+  serviceUnhealthySecondThreshold: 300 # Config for the health check threshold for service. Default value is 60.
+  deploymentUnhealthySecondThreshold: 300 # Config for the health check threshold for deployments. Default value is 60.
+  serveConfig:
+    importPath: mobilenet.mobilenet:app
+    runtimeEnv: |
+      working_dir: "https://github.com/ray-project/serve_config_examples/archive/b393e77bbd6aba0881e3d94c05f968f05a387b96.zip"
+      pip: ["python-multipart==0.0.6"]
+  rayClusterConfig:
+    rayVersion: '2.5.0' # should match the Ray version in the image of the containers
+    ######################headGroupSpecs#################################
+    # Ray head pod template.
+    headGroupSpec:
+      # The `rayStartParams` are used to configure the `ray start` command.
+      # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
+      # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
+      rayStartParams:
+        dashboard-host: '0.0.0.0'
+      #pod template
+      template:
+        spec:
+          containers:
+            - name: ray-head
+              image: rayproject/ray-ml:2.5.0
+              resources:
+                limits:
+                  cpu: 2
+                  memory: 8Gi
+                requests:
+                  cpu: 2
+                  memory: 8Gi
+              ports:
+                - containerPort: 6379
+                  name: gcs-server
+                - containerPort: 8265 # Ray dashboard
+                  name: dashboard
+                - containerPort: 10001
+                  name: client
+                - containerPort: 8000
+                  name: serve
+    workerGroupSpecs:
+      # the pod replicas in this group typed worker
+      - replicas: 1
+        minReplicas: 1
+        maxReplicas: 5
+        # logical group name, for this called small-group, also can be functional
+        groupName: worker
+        # The `rayStartParams` are used to configure the `ray start` command.
+        # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
+        # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
+        rayStartParams: {}
+        #pod template
+        template:
+          spec:
+            containers:
+              - name: ray-worker # must consist of lower case alphanumeric characters or '-', and must start and end with an alphanumeric character (e.g. 'my-name',  or '123-abc'
+                image: rayproject/ray-ml:2.5.0
+                lifecycle:
+                  preStop:
+                    exec:
+                      command: ["/bin/sh","-c","ray stop"]
+                resources:
+                  limits:
+                    cpu: "2"
+                    memory: "8Gi"
+                  requests:
+                    cpu: "2"
+                    memory: "8Gi"