mirror of
https://github.com/meta-llama/llama-stack.git
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58 lines
No EOL
1.7 KiB
YAML
58 lines
No EOL
1.7 KiB
YAML
services:
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nim:
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image: nvcr.io/nim/meta/llama-3.1-8b-instruct:latest
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network_mode: "host"
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volumes:
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- nim-llm-cache:/opt/nim/.cache
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ports:
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- "8000:8000"
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shm_size: 16G
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environment:
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- CUDA_VISIBLE_DEVICES=0
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- NIM_HTTP_API_PORT=8000
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- NIM_TRITON_LOG_VERBOSE=1
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- NGC_API_KEY=${NIM_NGC_API_KEY:-${NGC_API_KEY:-ngcapikey}}
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command: []
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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# that's the closest analogue to --gpus; provide
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# an integer amount of devices or 'all'
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count: 1
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# Devices are reserved using a list of capabilities, making
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# capabilities the only required field. A device MUST
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# satisfy all the requested capabilities for a successful
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# reservation.
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capabilities: [gpu]
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runtime: nvidia
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healthcheck:
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test: ["CMD", "curl", "http://localhost:8000/v1/health/ready"]
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interval: 5s
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timeout: 5s
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retries: 30
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start_period: 120s
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llamastack:
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depends_on:
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- nim
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image: distribution-nvidia:dev
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network_mode: "host"
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volumes:
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- ~/.llama:/root/.llama
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- ./run.yaml:/root/llamastack-run-nvidia.yaml
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ports:
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- "5000:5000"
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environment:
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- INFERENCE_MODEL=${INFERENCE_MODEL:-Llama3.1-8B-Instruct}
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- NVIDIA_API_KEY=${NVIDIA_API_KEY:-}
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entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml-config /root/llamastack-run-nvidia.yaml"
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deploy:
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restart_policy:
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condition: on-failure
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delay: 3s
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max_attempts: 5
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window: 60s
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volumes:
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nim-llm-cache:
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driver: local |