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c4d5d6bb91
* tgi docker compose * path * wait for tgi server to start before starting server * update provider-id * move scripts to distribution/ folder * add readme * readme
55 lines
1.8 KiB
YAML
55 lines
1.8 KiB
YAML
services:
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text-generation-inference:
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image: ghcr.io/huggingface/text-generation-inference:latest
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network_mode: "host"
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volumes:
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- $HOME/.cache/huggingface:/data
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ports:
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- "5009:5009"
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devices:
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- nvidia.com/gpu=all
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environment:
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- CUDA_VISIBLE_DEVICES=0
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- HF_HOME=/data
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- HF_DATASETS_CACHE=/data
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- HF_MODULES_CACHE=/data
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- HF_HUB_CACHE=/data
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command: ["--dtype", "bfloat16", "--usage-stats", "on", "--sharded", "false", "--model-id", "meta-llama/Llama-3.1-8B-Instruct", "--port", "5009", "--cuda-memory-fraction", "0.3"]
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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", "-f", "http://text-generation-inference:5009/health"]
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interval: 5s
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timeout: 5s
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retries: 30
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llamastack-local-cpu:
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depends_on:
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text-generation-inference:
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condition: service_healthy
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image: llamastack-local-cpu
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network_mode: "host"
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volumes:
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- ~/.llama:/root/.llama
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# Link to TGI run.yaml file
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- ./tgi-run.yaml:/root/llamastack-run-tgi.yaml
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ports:
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- "5000:5000"
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# Hack: wait for TGI server to start before starting docker
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entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-tgi.yaml"
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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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