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gpu: Add NIM embedding service
This is the second part of the RAG pipeline for embedding Signed-off-by: Zvonko Kaiser <zkaiser@nvidia.com>
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commit
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@ -0,0 +1,91 @@
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---
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apiVersion: v1
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kind: Secret
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metadata:
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name: ngc-secret-embedqa
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namespace: default
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type: kubernetes.io/dockerconfigjson
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data:
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.dockerconfigjson: ${DOCKER_CONFIG_JSON}
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---
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apiVersion: v1
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kind: Pod
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metadata:
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name: nvidia-nim-llama-32-nv-embedqa-1b-v2
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labels:
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app: nvidia-nim-llama-32-nv-embedqa-1b-v2
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spec:
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runtimeClassName: "${RUNTIME_CLASS_NAME}"
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serviceAccountName: default
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imagePullSecrets:
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- name: ngc-secret-embedqa
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securityContext:
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fsGroup: 0
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runAsGroup: 0
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runAsUser: 0
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containers:
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- name: nvidia-nim-llama-32-nv-embedqa-1b-v2
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image: nvcr.io/nim/nvidia/llama-3.2-nv-embedqa-1b-v2:1.5.0
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imagePullPolicy: IfNotPresent
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env:
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- name: NIM_CACHE_PATH
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value: "/opt/nim/.cache"
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- name: NGC_API_KEY
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value: "${NGC_API_KEY}"
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- name: NIM_HTTP_API_PORT
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value: "8000"
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- name: NIM_JSONL_LOGGING
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value: "1"
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- name: NIM_LOG_LEVEL
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value: "INFO"
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ports:
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- containerPort: 8000
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name: http
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livenessProbe:
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httpGet:
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path: /v1/health/live
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port: 8000
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initialDelaySeconds: 15
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periodSeconds: 10
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timeoutSeconds: 1
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successThreshold: 1
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failureThreshold: 3
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readinessProbe:
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httpGet:
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path: /v1/health/ready
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port: 8000
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initialDelaySeconds: 15
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periodSeconds: 10
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timeoutSeconds: 1
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successThreshold: 1
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failureThreshold: 3
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startupProbe:
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httpGet:
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path: /v1/health/ready
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port: 8000
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initialDelaySeconds: 40
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periodSeconds: 10
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timeoutSeconds: 1
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successThreshold: 1
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failureThreshold: 180
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resources:
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limits:
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nvidia.com/pgpu: 1
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cpu: "16"
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memory: "64Gi"
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volumeMounts:
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- name: nim-cache
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mountPath: /opt/nim/.cache
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volumes:
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- name: nim-cache
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hostPath:
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path: "/opr/nim/.cache"
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type: DirectoryOrCreate
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