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