316 lines
14 KiB
Docker
316 lines
14 KiB
Docker
###############################################################################
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# Stage 0 ─ builder-torch：编译 PyTorch 2.7.1 (+cu126)
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###############################################################################
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ARG CUDA_VERSION=12.6.1
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FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS builder-torch
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ENV USE_CUDA=1 \
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USE_DISTRIBUTED=1 \
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USE_MPI=1 \
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USE_GLOO=1 \
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USE_NCCL=1 \
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USE_SYSTEM_NCCL=1 \
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BUILD_TEST=0
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ARG MAX_JOBS=90
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ENV DEBIAN_FRONTEND=noninteractive \
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PYTHONUNBUFFERED=1 LANG=C.UTF-8 LC_ALL=C.UTF-8 \
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TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9;9.0"
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3 python3-dev python3-pip python3-distutils git cmake ninja-build \
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libopenblas-dev libopenmpi-dev \
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libnccl2=2.22.3-1+cuda12.6 \
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libnccl-dev=2.22.3-1+cuda12.6 \
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libjpeg-dev libpng-dev ca-certificates && \
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python3 -m pip install --no-cache-dir --upgrade pip wheel setuptools sympy pyyaml typing-extensions numpy
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RUN python3 -m pip install --no-cache-dir numpy requests packaging build
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WORKDIR /opt
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RUN git clone --recursive -b v2.7.1 https://github.com/pytorch/pytorch.git
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WORKDIR /opt/pytorch
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ENV MAX_JOBS=${MAX_JOBS}
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RUN echo "Building PyTorch with USE_DISTRIBUTED=$USE_DISTRIBUTED" && \
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python3 setup.py bdist_wheel
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###############################################################################
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# Stage 1 ─ builder-extras：用自编 Torch 装 TV / flashinfer / sglang，并收集轮子
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###############################################################################
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ARG CUDA_VERSION=12.6.1
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FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS builder-extras
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ENV TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;8.9"
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ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1 LANG=C.UTF-8 LC_ALL=C.UTF-8
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3 python3-pip python3-distutils python3.10-dev git build-essential \
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cmake ninja-build libjpeg-dev libpng-dev ca-certificates \
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libopenmpi-dev libopenblas-dev\
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libnccl2=2.22.3-1+cuda12.6 \
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libnccl-dev=2.22.3-1+cuda12.6 \
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curl xz-utils \
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&& python3 -m pip install --no-cache-dir --upgrade pip wheel setuptools
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# ── 安装自编 torch 轮子 ──────────────────────────────────────────────────────
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COPY --from=builder-torch /opt/pytorch/dist /tmp/torch_dist
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RUN set -e && \
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echo "==> Files in /tmp/torch_dist:" && ls -lh /tmp/torch_dist && \
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find /tmp/torch_dist -name 'torch-*.whl' -print | xargs -r python3 -m pip install --no-cache-dir --no-deps
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# ── 编译 torchvision 0.22.1 (依赖本地 torch) ────────────────────────────────
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WORKDIR /opt
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RUN git clone -b v0.22.1 https://github.com/pytorch/vision.git
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WORKDIR /opt/vision
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RUN python3 setup.py bdist_wheel
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# ── 编译 flashinfer (主分支支持 torch 2.7 / cu126) ─────────────────────────
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WORKDIR /opt
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RUN git clone --recursive -b v0.3.1 https://github.com/flashinfer-ai/flashinfer.git
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WORKDIR /opt/flashinfer
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# 覆盖你的目标算力：3090=8.6，4090=8.9，H100=9.0a；可按需增/减
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ENV FLASHINFER_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9"
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# 先做 AOT 预编译，再直接打 wheel（不隔离，使用同一份自编 torch）
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RUN python3 -m pip install --no-cache-dir numpy requests build "cuda-python>=12.0,<13" "nvidia-nvshmem-cu12" ninja pynvml filelock && \
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python3 -m flashinfer.aot && \
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python3 -m build --no-isolation --wheel && \
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ls -lh dist/
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COPY ./sglang /sgl/sglang
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# # ── 🔄 下载 sgl-kernel（与 sglang 同步）───────────────────────────────────────
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# RUN pip download --only-binary=:all: --no-deps sgl-kernel==0.3.9.post2 -d /tmp/sgl_kernel_wheels
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ENV PATH=/usr/local/cuda/bin:${PATH}
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# ── 用你本地源码编 sgl-kernel==0.3.9.post2（与自编 torch 完全 ABI 对齐） ──────
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WORKDIR /sgl/sglang/sgl-kernel
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# 覆盖安装 ptxas 12.8（保留 nvcc 12.6），并打印版本确认
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RUN bash -lc '\
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set -euo pipefail; \
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NVCC_ARCHIVE_VERSION=12.8.93; \
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T=cuda_nvcc-linux-x86_64-${NVCC_ARCHIVE_VERSION}-archive; \
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curl -fL --http1.1 -O https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/linux-x86_64/${T}.tar.xz && \
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tar -xf ${T}.tar.xz && \
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install -m 0755 ${T}/bin/ptxas /usr/local/cuda/bin/ptxas && \
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/usr/local/cuda/bin/ptxas --version \
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'
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# 限制构建并行；避免 ptxas 多线程崩溃
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ENV CMAKE_BUILD_PARALLEL_LEVEL=8
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ENV SGL_KERNEL_COMPILE_THREADS=1
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RUN bash -lc 'ls -la; test -f pyproject.toml -o -f setup.py || (echo "❌ no pyproject.toml/setup.py here; try sgl-kernel/python" && exit 1)'
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# 构建 sgl-kernel（保持 FA3；去掉无效的关 90a 标志）
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RUN python3 -m pip install --no-cache-dir "cmake>=3.27,<4.0" scikit-build-core==0.11.6 pybind11[global] packaging && \
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bash -lc '\
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export CMAKE_PREFIX_PATH="$(python3 -c "import torch; print(torch.utils.cmake_prefix_path)")" && \
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export TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9" && \
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export CUDAARCHS="80;86;89" && \
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export CMAKE_CUDA_ARCHITECTURES="$CUDAARCHS" && \
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# 这里保留常规参数；如果项目支持，也把内核编译线程设为 1（未知项将被忽略，不会报错）
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export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=$CUDAARCHS -DSGL_KERNEL_COMPILE_THREADS=8 -Wno-dev" && \
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python3 -m pip wheel . --no-deps --no-build-isolation -w /tmp/sgl_kernel_wheels \
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'
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# ★ 构建期 constraints：把自编的 torch / sgl-kernel / flashinfer 都锁到本地 wheel
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RUN bash -lc '\
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set -euo pipefail; \
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TWHL=$(ls /tmp/torch_dist/torch-*.whl | head -n1); \
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SKWHL=$(ls /tmp/sgl_kernel_wheels/sgl_kernel-*.whl | head -n1); \
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FWHL=$(ls /opt/flashinfer/dist/flashinfer_python-*.whl 2>/dev/null | head -n1 || true); \
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: > /tmp/local_constraints_build.txt; \
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echo "torch @ file://$TWHL" >> /tmp/local_constraints_build.txt; \
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echo "sgl-kernel @ file://$SKWHL" >> /tmp/local_constraints_build.txt; \
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if [ -n "$FWHL" ]; then \
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echo "flashinfer-python @ file://$FWHL" >> /tmp/local_constraints_build.txt; \
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fi; \
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echo ">>> build-time constraints:"; cat /tmp/local_constraints_build.txt \
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'
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RUN python3 -m pip install --no-cache-dir --no-deps /tmp/sgl_kernel_wheels/sgl_kernel-*.whl
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# ── 下载 vllm 预编译 wheel，避免编译 flash-attn ───────────────────────────────
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WORKDIR /opt
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RUN pip download --only-binary=:all: --no-deps vllm==0.9.1 -d /tmp/vllm_wheels
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# ── 编译你本地 sglang 源码并打 wheel ───────────────────────────────────────
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WORKDIR /sgl/sglang/python
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RUN python3 -m pip install --no-build-isolation -c /tmp/local_constraints_build.txt ".[srt,openai]" && \
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python3 -m pip wheel --no-build-isolation -c /tmp/local_constraints_build.txt ".[srt,openai]" -w /tmp/sg_wheels
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# ── 收集所有 wheel 到 /wheels ──────────────────────────────────────────────
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RUN mkdir -p /wheels && \
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cp /tmp/torch_dist/torch*.whl /wheels/ && \
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cp /opt/vision/dist/torchvision-*.whl /wheels/ && \
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cp /opt/flashinfer/dist/flashinfer_python-*.whl /wheels/ && \
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cp /tmp/vllm_wheels/vllm-*.whl /wheels/ && \
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cp /tmp/sg_wheels/sglang-*.whl /wheels/ && \
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cp /tmp/sgl_kernel_wheels/sgl_kernel-*.whl /wheels/ && \
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pip wheel filelock typing-extensions sympy fsspec jinja2 networkx -w /wheels
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# ── ✅ 再打包 runtime 阶段必需依赖 ────────────────────────────────────────────
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RUN pip wheel \
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pydantic orjson psutil pyzmq pynvml \
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transformers==4.56.0 uvicorn fastapi IPython aiohttp \
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setproctitle uvloop sentencepiece triton pillow cachetools msgspec blake3 cloudpickle compressed-tensors einops openai py-cpuinfo dill partial_json_parser python-multipart torchao \
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-w /wheels
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# 产出 openai-harmony 的离线 wheel
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RUN pip wheel --no-deps openai-harmony==0.0.4 -w /wheels
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# ── ✅ 打包 gradio UI 所需依赖 ────────────────────────────────────────────────
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RUN pip wheel "gradio==5.38.2" requests -w /wheels
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# 把运行时所需依赖也打包进入wheel ────────────────────────────────────────────────
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RUN pip wheel pybase64==1.3.2 -w /wheels
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# 导出轮子的独立阶段
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FROM scratch AS wheelhouse
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COPY --from=builder-extras /wheels /
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# 从宿主机目录 _wheelhouse/ 安装轮子的 runtime
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ARG CUDA_VERSION=12.6.1
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FROM nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu22.04 AS runtime-prebuilt
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ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1 LANG=C.UTF-8 LC_ALL=C.UTF-8
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RUN apt-get update && apt-get install -y --no-install-recommends \
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gcc g++ build-essential ninja-build cuda-compiler-12-6 \
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libcupti-dev cuda-cupti-12-6 \
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python3 python3-dev python3-pip python3-distutils curl ca-certificates \
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libopenblas-dev libgomp1 libnuma1 libopenmpi-dev openmpi-bin libnuma-dev libpng16-16 libjpeg8 \
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libnccl2=2.22.3-1+cuda12.6 && \
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rm -rf /var/lib/apt/lists/* && \
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python3 -m pip install --no-cache-dir --upgrade pip
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RUN ldconfig -p | grep -i cupti || (echo "no cupti"; exit 1)
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RUN ldconfig
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# ★ 从宿主机构建上下文复制本地轮子（目录名固定：_wheelhouse/）
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COPY _wheelhouse/ /tmp/wheels/
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# 安装顺序与 runtime-autobuild 完全一致（优先 torch，再装其它）
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RUN ls -lh /tmp/wheels || true && \
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# rm -f /tmp/wheels/torch-2.7.1a0+*.whl && \
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rm -f /tmp/wheels/huggingface_hub-0.34.4*.whl || true && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/torch*.whl && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/vllm-*.whl || true && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/sgl_kernel-*.whl || true && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/gradio-5.38.2*.whl || true && \
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python3 -m pip install --no-cache-dir --no-deps $(find /tmp/wheels -maxdepth 1 -type f -name '*.whl' ! -name 'gradio-*' -printf "/tmp/wheels/%f ") && \
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python3 -c "import gradio, sys; print('✅ Gradio version =', gradio.__version__)" && \
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rm -rf /tmp/wheels
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RUN python3 -m pip install --no-deps xgrammar==0.1.24
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RUN echo "/usr/local/cuda/extras/CUPTI/lib64" > /etc/ld.so.conf.d/cupti.conf && ldconfig
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# 保险起见，再加一行环境变量（有些基础镜像不把 extras 加入 ld.so.conf）：
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ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:${LD_LIBRARY_PATH}
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###############################################################################
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# Stage 2 ─ runtime：极简运行镜像，仅离线安装 wheel
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###############################################################################
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ARG CUDA_VERSION=12.6.1
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FROM nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu22.04 AS runtime-autobuild
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ENV DEBIAN_FRONTEND=noninteractive PYTHONUNBUFFERED=1 LANG=C.UTF-8 LC_ALL=C.UTF-8
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RUN apt-get update && apt-get install -y --no-install-recommends gcc g++ build-essential ninja-build cuda-compiler-12-6\
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python3 python3-dev python3-pip python3-distutils curl ca-certificates \
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libcupti-dev cuda-cupti-12-6 \
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libopenblas-dev libgomp1 libnuma1 libopenmpi-dev openmpi-bin libnuma-dev libpng16-16 libjpeg8 libnccl2=2.22.3-1+cuda12.6 && \
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rm -rf /var/lib/apt/lists/* && \
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python3 -m pip install --no-cache-dir --upgrade pip
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# 检查 cupti 动态库
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RUN ldconfig -p | grep -i cupti || (echo "no cupti"; exit 1)
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# 👇建议在后面补上
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RUN ldconfig
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COPY _wheelhouse/ /tmp/wheels/
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# ✅ 优先装你自编的 torch，避免被 PyPI 上的覆盖
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RUN ls -lh /tmp/wheels && \
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# rm -f /tmp/wheels/torch-2.7.1a0+*.whl && \
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rm -f /tmp/wheels/huggingface_hub-0.34.4*.whl && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/torch*.whl && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/vllm-*.whl && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/sgl_kernel-*.whl && \
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python3 -m pip install --no-cache-dir --no-deps /tmp/wheels/gradio-5.38.2*.whl && \
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# python3 -m pip install --no-cache-dir --no-deps $(ls /tmp/wheels | grep -v '^gradio-' | sed 's|^|/tmp/wheels/|') && \
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python3 -m pip install --no-cache-dir --no-deps $(find /tmp/wheels -maxdepth 1 -type f -name '*.whl' ! -name 'gradio-*') && \
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python3 -c "import gradio, sys; print('✅ Gradio version =', gradio.__version__)" && \
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rm -rf /tmp/wheels
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# ✅ 安装 Prometheus client
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RUN python3 -m pip install --no-cache-dir prometheus_client
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RUN python3 -m pip install --no-deps xgrammar==0.1.24
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RUN echo "/usr/local/cuda/extras/CUPTI/lib64" > /etc/ld.so.conf.d/cupti.conf && ldconfig
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# 保险起见，再加一行环境变量（有些基础镜像不把 extras 加入 ld.so.conf）：
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ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:${LD_LIBRARY_PATH}
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# ✅ 设置多进程 metrics 收集目录（用于 MultiProcessCollector）
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ENV PROMETHEUS_MULTIPROC_DIR=/tmp/prometheus
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# ✅ 确保目录存在
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RUN mkdir -p /tmp/prometheus
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# ---- 拷贝预调优的 MoE Triton kernel config ----------------------------
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COPY moe_kernels /usr/local/lib/python3.10/dist-packages/sglang/srt/layers/moe/fused_moe_triton/configs
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# ✅ 添加 Tini（推荐）
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ENV TINI_VERSION=v0.19.0
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ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /tini
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RUN chmod +x /tini
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ENTRYPOINT ["/tini", "--"]
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# ---- 拷贝模型（路径可换） ----
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# COPY ./Alibaba/Qwen3-30B-A3B /root/.cradle/Alibaba/Qwen3-30B-A3B
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HEALTHCHECK --interval=30s --timeout=2s --start-period=300s --retries=5 CMD curl -fs http://localhost:30000/health || exit 1
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# ---- 暴露端口 ----
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EXPOSE 30000 30001
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# 安装 supervisor
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RUN apt-get update && apt-get install -y supervisor && \
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mkdir -p /etc/supervisor/conf.d
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# 拷贝 supervisord 配置文件和 UI 脚本
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COPY ./meta_ui.py /app/meta_ui.py
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COPY ./supervisord.conf /etc/supervisor/supervisord.conf
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# 作为容器主进程运行 supervisor
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CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/supervisord.conf"]
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