[Doc] Update LWS docs

docs/source/deployment/frameworks/lws.md

@@ -7,5 +7,192 @@ A major use case is for multi-host/multi-node distributed inference.
 
 vLLM can be deployed with [LWS](https://github.com/kubernetes-sigs/lws) on Kubernetes for distributed model serving.
 
-Please see [this guide](https://github.com/kubernetes-sigs/lws/tree/main/docs/examples/vllm) for more details on
-deploying vLLM on Kubernetes using LWS.
+## Prerequisites
+
+* At least two Kubernetes nodes, each with 8 GPUs, are required.
+* Install LWS by following the instructions found [here](https://lws.sigs.k8s.io/docs/installation/).
+
+## Deploy and Serve
+
+Deploy the following yaml file `lws.yaml`
+
+```yaml
+apiVersion: leaderworkerset.x-k8s.io/v1
+kind: LeaderWorkerSet
+metadata:
+  name: vllm
+spec:
+  replicas: 2
+  leaderWorkerTemplate:
+    size: 2
+    restartPolicy: RecreateGroupOnPodRestart
+    leaderTemplate:
+      metadata:
+        labels:
+          role: leader
+      spec:
+        containers:
+          - name: vllm-leader
+            image: docker.io/vllm/vllm-openai:latest
+            env:
+              - name: HUGGING_FACE_HUB_TOKEN
+                value: <your-hf-token>
+            command:
+              - sh
+              - -c
+              - "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh leader --ray_cluster_size=$(LWS_GROUP_SIZE); 
+                 python3 -m vllm.entrypoints.openai.api_server --port 8080 --model meta-llama/Meta-Llama-3.1-405B-Instruct --tensor-parallel-size 8 --pipeline_parallel_size 2"
+            resources:
+              limits:
+                nvidia.com/gpu: "8"
+                memory: 1124Gi
+                ephemeral-storage: 800Gi
+              requests:
+                ephemeral-storage: 800Gi
+                cpu: 125
+            ports:
+              - containerPort: 8080
+            readinessProbe:
+              tcpSocket:
+                port: 8080
+              initialDelaySeconds: 15
+              periodSeconds: 10
+            volumeMounts:
+              - mountPath: /dev/shm
+                name: dshm
+        volumes:
+        - name: dshm
+          emptyDir:
+            medium: Memory
+            sizeLimit: 15Gi
+    workerTemplate:
+      spec:
+        containers:
+          - name: vllm-worker
+            image: docker.io/vllm/vllm-openai:latest
+            command:
+              - sh
+              - -c
+              - "bash /vllm-workspace/examples/online_serving/multi-node-serving.sh worker --ray_address=$(LWS_LEADER_ADDRESS)"
+            resources:
+              limits:
+                nvidia.com/gpu: "8"
+                memory: 1124Gi
+                ephemeral-storage: 800Gi
+              requests:
+                ephemeral-storage: 800Gi
+                cpu: 125
+            env:
+              - name: HUGGING_FACE_HUB_TOKEN
+                value: <your-hf-token>
+            volumeMounts:
+              - mountPath: /dev/shm
+                name: dshm   
+        volumes:
+        - name: dshm
+          emptyDir:
+            medium: Memory
+            sizeLimit: 15Gi
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: vllm-leader
+spec:
+  ports:
+    - name: http
+      port: 8080
+      protocol: TCP
+      targetPort: 8080
+  selector:
+    leaderworkerset.sigs.k8s.io/name: vllm
+    role: leader
+  type: ClusterIP
+```
+
+```bash
+kubectl apply -f lws.yaml
+```
+
+Verify the status of the pods:
+
+```bash
+kubectl get pods
+```
+
+Should get an output similar to this:
+
+```bash
+NAME       READY   STATUS    RESTARTS   AGE
+vllm-0     1/1     Running   0          2s
+vllm-0-1   1/1     Running   0          2s
+vllm-1     1/1     Running   0          2s
+vllm-1-1   1/1     Running   0          2s
+```
+
+Verify that the distributed tensor-parallel inference works:
+
+```bash
+kubectl logs vllm-0 |grep -i "Loading model weights took" 
+```
+
+Should get something similar to this:
+
+```text
+INFO 05-08 03:20:24 model_runner.py:173] Loading model weights took 0.1189 GB
+(RayWorkerWrapper pid=169, ip=10.20.0.197) INFO 05-08 03:20:28 model_runner.py:173] Loading model weights took 0.1189 GB
+```
+
+## Access ClusterIP service
+
+```bash
+# Listen on port 8080 locally, forwarding to the targetPort of the service's port 8080 in a pod selected by the service
+kubectl port-forward svc/vllm-leader 8080:8080
+```
+
+The output should be similar to the following:
+
+```text
+Forwarding from 127.0.0.1:8080 -> 8080
+Forwarding from [::1]:8080 -> 8080
+```
+
+## Serve the model
+
+Open another terminal and send a request
+
+```text
+curl http://localhost:8080/v1/completions \
+-H "Content-Type: application/json" \
+-d '{
+    "model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
+    "prompt": "San Francisco is a",
+    "max_tokens": 7,
+    "temperature": 0
+}'
+```
+
+The output should be similar to the following
+
+```text
+{
+  "id": "cmpl-1bb34faba88b43f9862cfbfb2200949d",
+  "object": "text_completion",
+  "created": 1715138766,
+  "model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
+  "choices": [
+    {
+      "index": 0,
+      "text": " top destination for foodies, with",
+      "logprobs": null,
+      "finish_reason": "length",
+      "stop_reason": null
+    }
+  ],
+  "usage": {
+    "prompt_tokens": 5,
+    "total_tokens": 12,
+    "completion_tokens": 7
+  }
+}
+```