43 lines
1.5 KiB
Docker
43 lines
1.5 KiB
Docker
FROM ghcr.io/astral-sh/uv:latest AS uv_bin
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FROM nvidia/cuda:12.4.1-base-ubuntu22.04
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ENV UV_LINK_MODE=copy \
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UV_COMPILE_BYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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UV_PROJECT_ENVIRONMENT=/app/.venv \
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TORCH_CUDA_ARCH_LIST="8.6" \
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CUDA_VISIBLE_DEVICES=0 \
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UV_TORCH_BACKEND=cu128
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COPY --from=uv_bin /uv /uvx /bin/
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#RUN apt-get update && apt-get install -y --no-install-recommends \
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# ca-certificates curl gnupg && \
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# sed -i 's|http://archive.ubuntu.com/ubuntu/|http://mirrors.cloud.tencent.com/ubuntu/|g' /etc/apt/sources.list && \
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# sed -i 's|http://security.ubuntu.com/ubuntu/|http://mirrors.cloud.tencent.com/ubuntu/|g' /etc/apt/sources.list && \
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# apt-get update && \
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# apt-get install -y --no-install-recommends \
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# git \
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# ffmpeg libsm6 libxext6 \
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# build-essential g++ \
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# && apt-get clean && rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# 3. 安装依赖 (不加 --system，让 uv 创建受管的虚拟环境)
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# 这里会根据 pyproject.toml 自动下载并安装 Python 3.11
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COPY pyproject.toml uv.lock ./
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#RUN uv sync --frozen --no-dev --no-install-project --python 3.9
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# 4. 拷贝项目文件并安装项目本身
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COPY . .
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#RUN uv sync --frozen --no-dev --python 3.9
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# 5. 【最关键】将虚拟环境的 bin 目录提到最前面
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# 注意：uv sync 创建的 python 就在这个目录下
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ENV PATH="/app/.venv/bin:$PATH"
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EXPOSE 8000
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# 验证路径并运行
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# 此时运行 python 实际上是运行 /app/.venv/bin/python
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CMD ["sleep", "infinity"] |