[Doc]: Add deploying_with_k8s guide (#8451)

docs/source/index.rst

@@ -79,6 +79,7 @@ Documentation
 
    serving/openai_compatible_server
    serving/deploying_with_docker
+   serving/deploying_with_k8s
    serving/distributed_serving
    serving/metrics
    serving/env_vars

docs/source/serving/deploying_with_k8s.rst

@@ -0,0 +1,175 @@
+.. _deploying_with_k8s:
+
+Deploying with Kubernetes
+==========================
+
+Using Kubernetes to deploy vLLM is a scalable and efficient way to serve machine learning models. This guide will walk you through the process of deploying vLLM with Kubernetes, including the necessary prerequisites, steps for deployment, and testing.
+
+Prerequisites
+-------------
+Before you begin, ensure that you have the following:
+
+- A running Kubernetes cluster
+- NVIDIA Kubernetes Device Plugin (`k8s-device-plugin`): This can be found at `https://github.com/NVIDIA/k8s-device-plugin/`
+- Available GPU resources in your cluster
+
+Deployment Steps
+----------------
+
+1.  **Create a PVC , Secret and Deployment for vLLM**
+
+
+PVC is used to store the model cache and it is optional, you can use hostPath or other storage options
+
+.. code-block:: yaml
+
+  apiVersion: v1
+  kind: PersistentVolumeClaim
+  metadata:
+    name: mistral-7b
+    namespace: default
+  spec:
+    accessModes:
+    - ReadWriteOnce
+    resources:
+      requests:
+        storage: 50Gi
+    storageClassName: default
+    volumeMode: Filesystem
+
+Secret is optional and only required for accessing gated models, you can skip this step if you are not using gated models
+
+.. code-block:: yaml
+
+  apiVersion: v1
+  kind: Secret
+  metadata:
+    name: hf-token-secret
+    namespace: default
+  type: Opaque
+  data:
+    token: "REPLACE_WITH_TOKEN"
+
+
+Create a deployment file for vLLM to run the model server. The following example deploys the `Mistral-7B-Instruct-v0.3` model:
+
+.. code-block:: yaml
+
+  apiVersion: apps/v1
+  kind: Deployment
+  metadata:
+    name: mistral-7b
+    namespace: default
+    labels:
+      app: mistral-7b
+  spec:
+    replicas: 1
+    selector:
+      matchLabels:
+        app: mistral-7b
+    template:
+      metadata:
+        labels:
+          app: mistral-7b
+      spec:
+        volumes:
+        - name: cache-volume
+          persistentVolumeClaim:
+            claimName: mistral-7b
+        # vLLM needs to access the host's shared memory for tensor parallel inference.
+        - name: shm
+          emptyDir:
+            medium: Memory
+            sizeLimit: "2Gi"
+        containers:
+        - name: mistral-7b
+          image: vllm/vllm-openai:latest
+          command: ["/bin/sh", "-c"]
+          args: [
+            "vllm serve mistralai/Mistral-7B-Instruct-v0.3 --trust-remote-code --enable-chunked-prefill --max_num_batched_tokens 1024"
+          ]
+          env:
+          - name: HUGGING_FACE_HUB_TOKEN
+            valueFrom:
+              secretKeyRef:
+                name: hf-token-secret
+                key: token
+          ports:
+          - containerPort: 8000
+          resources:
+            limits:
+              cpu: "10"
+              memory: 20G
+              nvidia.com/gpu: "1"
+            requests:
+              cpu: "2"
+              memory: 6G
+              nvidia.com/gpu: "1"
+          volumeMounts:
+          - mountPath: /root/.cache/huggingface
+            name: cache-volume
+          - name: shm
+            mountPath: /dev/shm
+          livenessProbe:
+            httpGet:
+              path: /health
+              port: 8000
+            initialDelaySeconds: 60
+            periodSeconds: 10
+          readinessProbe:
+            httpGet:
+              path: /health
+              port: 8000
+            initialDelaySeconds: 60
+            periodSeconds: 5
+
+2. **Create a Kubernetes Service for vLLM**
+
+Next, create a Kubernetes Service file to expose the `mistral-7b` deployment:
+
+.. code-block:: yaml
+
+    apiVersion: v1
+    kind: Service
+    metadata:
+      name: mistral-7b
+      namespace: default
+    spec:
+      ports:
+      - name: http-mistral-7b
+        port: 80
+        protocol: TCP
+        targetPort: 8000
+      # The label selector should match the deployment labels & it is useful for prefix caching feature
+      selector:
+        app: mistral-7b
+      sessionAffinity: None
+      type: ClusterIP
+
+3. **Deploy and Test**
+
+Apply the deployment and service configurations using ``kubectl apply -f <filename>``:
+
+.. code-block:: console
+
+    kubectl apply -f deployment.yaml
+    kubectl apply -f service.yaml
+
+To test the deployment, run the following ``curl`` command:
+
+.. code-block:: console
+
+    curl http://mistral-7b.default.svc.cluster.local/v1/completions \
+      -H "Content-Type: application/json" \
+      -d '{
+            "model": "facebook/opt-125m",
+            "prompt": "San Francisco is a",
+            "max_tokens": 7,
+            "temperature": 0
+          }'
+
+If the service is correctly deployed, you should receive a response from the vLLM model.
+
+Conclusion
+----------
+Deploying vLLM with Kubernetes allows for efficient scaling and management of ML models leveraging GPU resources. By following the steps outlined above, you should be able to set up and test a vLLM deployment within your Kubernetes cluster. If you encounter any issues or have suggestions, please feel free to contribute to the documentation.