51 lines
1.7 KiB
Docker
51 lines
1.7 KiB
Docker
# Llama 4 Scout / Maverick on Iluvatar BI-V150
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#
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# 一个镜像同时支持：
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# - Llama-4-Scout-17B-16E-Instruct（BF16，TP8，8 卡）
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# - Llama-4-Maverick-17B-128E-Instruct-FP8（FP8，TP8+PP2，16 卡）
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#
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# 模型权重不打进镜像，运行时挂载模型目录；容器启动后读取 config.json
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# 自动判断 Scout / Maverick，并选择对应的 TP / PP / 端口。
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#
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# 构建：
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# docker build -t llama4-bi150:2026-10-26 .
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#
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# 运行：
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# ./run.sh /mnt/disk0/models/LLM-Research/Llama-4-Scout-17B-16E-Instruct
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# ./run.sh /mnt/disk0/models/LLM-Research/Llama-4-Maverick-17B-128E-Instruct-FP8
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#
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# 补丁是针对下面这个精确基础镜像生成的；换基础镜像需要重新生成 diff。
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# registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v7
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# (vLLM 0.17.0+corex.20260612070235)
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FROM registry.iluvatar.com.cn:10443/customer/sz/vllm0.17.0-4.4.0-x86:v7
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ARG IMAGE_VERSION=2026-10-26
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LABEL org.opencontainers.image.title="llama4-bi150" \
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org.opencontainers.image.description="Llama 4 Scout / Maverick on Iluvatar BI-V150 with vLLM 0.17.0+corex" \
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org.opencontainers.image.version="${IMAGE_VERSION}"
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COPY patches /opt/llama4/patches
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COPY apply_patch.py /opt/llama4/apply_patch.py
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COPY detect_model.py /opt/llama4/detect_model.py
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COPY entrypoint.sh /usr/local/bin/llama4-entrypoint.sh
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RUN chmod +x /usr/local/bin/llama4-entrypoint.sh \
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&& chmod +x /opt/llama4/detect_model.py \
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&& python3 /opt/llama4/apply_patch.py
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ENV MODEL_PATH=/models/llama4 \
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AUTO_DETECT=1 \
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HOST=0.0.0.0 \
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MAX_MODEL_LEN=4096 \
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MAX_NUM_SEQS=1 \
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GPU_MEM_UTIL=0.90 \
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ENFORCE_EAGER=1 \
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PYTHONUNBUFFERED=1
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WORKDIR /opt/llama4
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EXPOSE 8091 8092
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ENTRYPOINT ["/usr/local/bin/llama4-entrypoint.sh"]
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