xiejiahao
05f1c3932f
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- detect_model.py picks TP/port/served name/eager/util from config.json - validate TP against the model config (dense global KV heads, MoE k % 32) - one apply_patch.py covers both variants, idempotent - Dockerfile stays generic: no ENV/WORKDIR/EXPOSE/ENTRYPOINT - run.sh/verify.sh follow the llama4-bi150 layout
58 lines
1.6 KiB
YAML
58 lines
1.6 KiB
YAML
services:
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gemma4:
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build:
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context: .
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dockerfile: Dockerfile
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image: gemma4-bi150:${TAG:-2026-10-27}
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container_name: gemma4
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# 镜像本身不设置启动命令，启动方式全部由这里决定。
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entrypoint: ["/usr/local/bin/gemma4-entrypoint.sh"]
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working_dir: /opt/gemma4
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expose:
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- "8094"
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- "8095"
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# vLLM 多卡 NCCL / 端口直通
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network_mode: host
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ipc: host
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# Iluvatar 运行时需要 privileged + /dev 绑定。
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# 只给 --device=/dev/iluvatar* 会在 worker init 时报
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# cudaGetDeviceCount() ... Error 1001
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privileged: true
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pid: host
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volumes:
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- /dev:/dev
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- ${MODEL_HOST_DIR:-/mnt/disk0/models/google/gemma-4-31B-it}:/models/gemma4:ro
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environment:
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MODEL_PATH: /models/gemma4
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AUTO_DETECT: "${AUTO_DETECT:-1}"
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HOST: "${HOST:-0.0.0.0}"
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# 默认给 4 张（Gemma-4-31B 的 TP4）；GEV 只需要 2 张，
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# entrypoint 按检测出的 TP 使用前 TP 张。
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CUDA_VISIBLE_DEVICES: "${GPUS:-15,14,13,12}"
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# 留空时由 entrypoint 根据 config.json 自动判断。
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# 需要手动覆盖时直接设置同名环境变量即可。
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TP: "${TP:-}"
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PORT: "${PORT:-}"
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SERVED_NAME: "${SERVED_NAME:-}"
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MAX_NUM_SEQS: "${MAX_NUM_SEQS:-}"
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GPU_MEM_UTIL: "${GPU_MEM_UTIL:-}"
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ENFORCE_EAGER: "${ENFORCE_EAGER:-}"
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MAX_MODEL_LEN: "${MAX_MODEL_LEN:-8192}"
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MAX_IMAGES: "${MAX_IMAGES:-4}"
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PYTHONUNBUFFERED: "1"
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EXTRA_ARGS: "${EXTRA_ARGS:-}"
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ulimits:
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memlock: -1
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stack: 67108864
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restart: unless-stopped
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