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nims.yaml
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nims.yaml
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services:
nemollm-inference:
container_name: nemollm-inference-microservice
image: nvcr.io/nim/meta/llama-3.1-70b-instruct:1.1
volumes:
# Use current path for model directory if nothing is specified
- ${MODEL_DIRECTORY:-.}:/opt/nim/.cache
user: "${USERID:-1000:1000}"
ports:
- "8000:8000"
expose:
- "8000"
environment:
NGC_API_KEY: ${NGC_API_KEY}
shm_size: 20gb
deploy:
resources:
reservations:
devices:
- driver: nvidia
# count: ${INFERENCE_GPU_COUNT:-all}
device_ids: ['${LLM_MS_GPU_ID:-0,1,2,3}']
capabilities: [gpu]
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/v1/health/ready"]
interval: 10s
timeout: 20s
retries: 100
profiles: ["local-nim"]
nemollm-embedding:
container_name: nemo-retriever-embedding-microservice
image: nvcr.io/nim/nvidia/nv-embedqa-e5-v5:1.0.1
volumes:
- ${MODEL_DIRECTORY:-.}:/opt/nim/.cache
ports:
- "9080:8000"
expose:
- "8000"
environment:
NGC_API_KEY: ${NGC_API_KEY}
user: "${USERID:-1000:1000}"
shm_size: 16GB
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ['${EMBEDDING_MS_GPU_ID:-4}']
capabilities: [gpu]
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/v1/health/ready"]
interval: 30s
timeout: 20s
retries: 3
start_period: 10m
profiles: ["local-nim"]
ranking-ms:
container_name: nemo-retriever-ranking-microservice
image: nvcr.io/nim/nvidia/nv-rerankqa-mistral-4b-v3:1.0.1
volumes:
- ${MODEL_DIRECTORY:-.}:/opt/nim/.cache
ports:
- "1976:8000"
expose:
- "8000"
environment:
NGC_API_KEY: ${NGC_API_KEY}
user: "${USERID:-1000:1000}"
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 10s
timeout: 20s
retries: 100
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ['${RANKING_MS_GPU_ID:-5}']
capabilities: [gpu]
profiles: ["local-nim"]
networks:
default:
name: nvidia-rag