91 lines
2.5 KiB
YAML
91 lines
2.5 KiB
YAML
apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: openclaw-brain-vllm
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namespace: customer1
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labels:
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app: openclaw-brain
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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: openclaw-brain
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template:
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metadata:
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labels:
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app: openclaw-brain
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spec:
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nodeSelector:
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cloud.google.com/gke-accelerator: "nvidia-a100-80gb"
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tolerations:
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- key: "nvidia.com/gpu-a100-80gb"
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operator: "Equal"
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value: "present"
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effect: "NoSchedule"
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containers:
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- name: vllm-brain
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image: vllm/vllm-openai:latest
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command: ["python3", "-m", "vllm.entrypoints.openai.api_server"]
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env:
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- name: HF_TOKEN
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value: ""
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- name: VLLM_TOKENIZER_MODE
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value: "auto"
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args:
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- --model=Jiunsong/supergemma4-26b-abliterated-multimodal
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- --host=0.0.0.0
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- --port=8000
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- --tensor-parallel-size=1
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- --gpu-memory-utilization=0.78 # ← Critical: lower than default 0.90
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- --max-model-len=16384 # Start conservative (you can raise to 32768 later)
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- --max-num-batched-tokens=8192
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- --max-num-seqs=4 # Lower for single-user / low-concurrency
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- --enforce-eager # Disables CUDA graphs → big memory saver at startup
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- --disable-custom-all-reduce
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- --trust-remote-code
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- --dtype=auto
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##- --limit-mm-per-prompt='{"image"=1, "video"=0}' # Optional: limit vision if not heavily using images yet
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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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memory: "80Gi"
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cpu: "16"
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requests:
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nvidia.com/gpu: 1
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memory: "60Gi"
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cpu: "8"
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volumeMounts:
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- name: model-cache
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mountPath: /root/.cache/huggingface
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volumes:
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- name: model-cache
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persistentVolumeClaim:
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claimName: vllm-model-cache-pvc
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---
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apiVersion: v1
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kind: PersistentVolumeClaim
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metadata:
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name: vllm-model-cache-pvc
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namespace: customer1
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spec:
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accessModes:
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- ReadWriteOnce
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resources:
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requests:
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storage: 100Gi
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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: openclaw-brain-service
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namespace: customer1
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spec:
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selector:
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app: openclaw-brain
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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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