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8be41a4e80
For a simple RAG pipeline add a embeding service Signed-off-by: Zvonko Kaiser <zkaiser@nvidia.com>
89 lines
2.1 KiB
YAML
89 lines
2.1 KiB
YAML
# Copyright (c) 2025 NVIDIA Corporation
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#
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# SPDX-License-Identifier: Apache-2.0
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#
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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-instruct
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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: ${POD_NAME_INSTRUCT}
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labels:
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app: ${POD_NAME_INSTRUCT}
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spec:
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restartPolicy: Never
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runtimeClassName: "${RUNTIME_CLASS_NAME}"
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imagePullSecrets:
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- name: ngc-secret-instruct
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securityContext:
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runAsUser: 0
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runAsGroup: 0
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fsGroup: 0
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containers:
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- name: ${POD_NAME_INSTRUCT}
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image: nvcr.io/nim/meta/llama3-8b-instruct:1.0.0
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# Ports exposed by the container:
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ports:
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- containerPort: 8000
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name: http-openai
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livenessProbe:
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httpGet:
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path: /v1/health/live
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port: http-openai
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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: http-openai
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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: http-openai
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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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# Environment variable for NGC_API_KEY. In production, use a Secret.
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env:
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- name: NGC_API_KEY
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value: "${NGC_API_KEY}"
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# GPU resource request/limit (for NVIDIA GPU)
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resources:
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requests:
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cpu: "16"
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memory: "32Gi"
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limits:
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nvidia.com/pgpu: "1"
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cpu: "16"
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memory: "32Gi"
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# Mount the local .cache directory into the container
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volumeMounts:
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- name: nim-cache
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mountPath: /opt/nim/.cache
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# Host path volume for the local .cache directory.
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# Adjust 'path' to match your $LOCAL_NIM_CACHE location.
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volumes:
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- name: nim-cache
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hostPath:
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path: "${LOCAL_NIM_CACHE}"
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type: DirectoryOrCreate
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