llama-stack-mirror/docs/source/distributions/k8s/vllm-k8s.yaml.template
ehhuang 84fa83b788
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fix: update k8s templates (#2645)
# What does this PR do?
- fix env variables
- use gpu for vllm
- add eks/apply.py for aws
- add template to set hf secret

## Test Plan
bash apply.sh

Co-authored-by: Eric Huang <erichuang@fb.com>
2025-07-08 15:57:01 -07:00

70 lines
1.5 KiB
Text

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: vllm-models
spec:
accessModes:
- ReadWriteOnce
volumeMode: Filesystem
resources:
requests:
storage: 50Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-server
spec:
replicas: 1
selector:
matchLabels:
app.kubernetes.io/name: vllm
template:
metadata:
labels:
app.kubernetes.io/name: vllm
workload-type: inference
spec:
nodeSelector:
eks.amazonaws.com/nodegroup: gpu
containers:
- name: vllm
image: vllm/vllm-openai:latest
command: ["/bin/sh", "-c"]
args:
- "vllm serve ${INFERENCE_MODEL} --dtype float16 --enforce-eager --max-model-len 4096 --gpu-memory-utilization 0.6"
env:
- name: INFERENCE_MODEL
value: "${INFERENCE_MODEL}"
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-token-secret
key: token
ports:
- containerPort: 8000
resources:
limits:
nvidia.com/gpu: 1
requests:
nvidia.com/gpu: 1
volumeMounts:
- 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