Serve a text summarizer on Kubernetes#

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

Step 1: Create a Kubernetes cluster with GPUs#

Follow aws-eks-gpu-cluster.md or gcp-gke-gpu-cluster.md to create a Kubernetes cluster with 1 CPU node and 1 GPU node.

Step 2: Install KubeRay operator#

Follow this document to install the latest stable KubeRay operator via Helm repository. Please note that the YAML file in this example uses serveConfigV2, which is supported starting from KubeRay v0.6.0.

Step 3: Install a RayService#

# Step 3.1: Download `ray-service.text-summarizer.yaml`
curl -LO https://raw.githubusercontent.com/ray-project/kuberay/v1.2.2/ray-operator/config/samples/ray-service.text-summarizer.yaml

# Step 3.2: Create a RayService
kubectl apply -f ray-service.text-summarizer.yaml

This RayService configuration contains some important settings:

The tolerations for workers allow them to be scheduled on nodes without any taints or on nodes with specific taints. However, workers will only be scheduled on GPU nodes because we set nvidia.com/gpu: 1 in the Pod’s resource configurations.
```
# Please add the following taints to the GPU node.
tolerations:
    - key: "ray.io/node-type"
    operator: "Equal"
    value: "worker"
    effect: "NoSchedule"
```

Step 4: Forward the port of Serve#

First get the service name from this command.

kubectl get services

Then, port forward to the serve.

kubectl port-forward svc/text-summarizer-serve-svc 8000

Note that the RayService’s Kubernetes 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 text_summarizer model#

# Step 5.1: Download `text_summarizer_req.py`
curl -LO https://raw.githubusercontent.com/ray-project/serve_config_examples/master/text_summarizer/text_summarizer_req.py

# Step 5.2: Send a request to the Summarizer model.
python text_summarizer_req.py
# Check printed to console

Step 6: Delete your service#

# path: ray-operator/config/samples/
kubectl delete -f ray-service.text-summarizer.yaml

Step 7: Uninstall your kuberay operator#

Follow this document to uninstall the latest stable KubeRay operator via Helm repository.