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[doc] Fold long code blocks to improve readability (#19926)

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Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>
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Reid
2025-06-23 13:24:23 +08:00
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docs/deployment/k8s.md
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@@ -29,85 +29,89 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
First, create a Kubernetes PVC and Secret for downloading and storing Hugging Face model:
```bash
cat <<EOF |kubectl apply -f -
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: vllm-models
spec:
accessModes:
- ReadWriteOnce
volumeMode: Filesystem
resources:
requests:
storage: 50Gi
---
apiVersion: v1
kind: Secret
metadata:
name: hf-token-secret
type: Opaque
data:
token: $(HF_TOKEN)
EOF
```
??? Config
```bash
cat <<EOF |kubectl apply -f -
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: vllm-models
spec:
accessModes:
- ReadWriteOnce
volumeMode: Filesystem
resources:
requests:
storage: 50Gi
---
apiVersion: v1
kind: Secret
metadata:
name: hf-token-secret
type: Opaque
data:
token: $(HF_TOKEN)
EOF
```
Next, start the vLLM server as a Kubernetes Deployment and Service:
```bash
cat <<EOF |kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-server
spec:
replicas: 1
selector:
matchLabels:
app.kubernetes.io/name: vllm
template:
??? Config
```bash
cat <<EOF |kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app.kubernetes.io/name: vllm
name: vllm-server
spec:
containers:
- name: vllm
image: vllm/vllm-openai:latest
command: ["/bin/sh", "-c"]
args: [
"vllm serve meta-llama/Llama-3.2-1B-Instruct"
]
env:
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
ports:
- containerPort: 8000
volumeMounts:
replicas: 1
selector:
matchLabels:
app.kubernetes.io/name: vllm
template:
metadata:
labels:
app.kubernetes.io/name: vllm
spec:
containers:
- name: vllm
image: vllm/vllm-openai:latest
command: ["/bin/sh", "-c"]
args: [
"vllm serve meta-llama/Llama-3.2-1B-Instruct"
]
env:
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
ports:
- containerPort: 8000
volumeMounts:
- name: llama-storage
mountPath: /root/.cache/huggingface
volumes:
- name: llama-storage
mountPath: /root/.cache/huggingface
volumes:
- name: llama-storage
persistentVolumeClaim:
claimName: vllm-models
---
apiVersion: v1
kind: Service
metadata:
name: vllm-server
spec:
selector:
app.kubernetes.io/name: vllm
ports:
- protocol: TCP
port: 8000
targetPort: 8000
type: ClusterIP
EOF
```
persistentVolumeClaim:
claimName: vllm-models
---
apiVersion: v1
kind: Service
metadata:
name: vllm-server
spec:
selector:
app.kubernetes.io/name: vllm
ports:
- protocol: TCP
port: 8000
targetPort: 8000
type: ClusterIP
EOF
```
We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
@@ -128,6 +132,9 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
PVC is used to store the model cache and it is optional, you can use hostPath or other storage options
<details>
<summary>Yaml</summary>
```yaml
apiVersion: v1
kind: PersistentVolumeClaim
@@ -144,6 +151,8 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
volumeMode: Filesystem
```
</details>
Secret is optional and only required for accessing gated models, you can skip this step if you are not using gated models
```yaml
@@ -156,13 +165,16 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
stringData:
token: "REPLACE_WITH_TOKEN"
```
Next to create the deployment file for vLLM to run the model server. The following example deploys the `Mistral-7B-Instruct-v0.3` model.
Here are two examples for using NVIDIA GPU and AMD GPU.
NVIDIA GPU:
<details>
<summary>Yaml</summary>
```yaml
apiVersion: apps/v1
kind: Deployment
@@ -233,10 +245,15 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
periodSeconds: 5
```
</details>
AMD GPU:
You can refer to the `deployment.yaml` below if using AMD ROCm GPU like MI300X.
<details>
<summary>Yaml</summary>
```yaml
apiVersion: apps/v1
kind: Deployment
@@ -305,12 +322,17 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
mountPath: /dev/shm
```
</details>
You can get the full example with steps and sample yaml files from <https://github.com/ROCm/k8s-device-plugin/tree/master/example/vllm-serve>.
2. Create a Kubernetes Service for vLLM
Next, create a Kubernetes Service file to expose the `mistral-7b` deployment:
<details>
<summary>Yaml</summary>
```yaml
apiVersion: v1
kind: Service
@@ -330,6 +352,8 @@ INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
type: ClusterIP
```
</details>
3. Deploy and Test
Apply the deployment and service configurations using `kubectl apply -f <filename>`: