76 lines
2.0 KiB
Docker
76 lines
2.0 KiB
Docker
# 1. 基础镜像：使用你验证过的 CUDA 12.4 开发版
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FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04
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# 环境变量
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ENV PYTHONDONTWRITEBYTECODE=1
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ENV PYTHONUNBUFFERED=1
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ENV DEBIAN_FRONTEND=noninteractive
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# 针对 RTX 50 系列的架构优化 (Blackwell = sm_100/sm_120，这里涵盖所有新架构)
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ENV TORCH_CUDA_ARCH_LIST="9.0;10.0+PTX"
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# 2. 安装系统工具 (增加了 ffmpeg 用于视频处理)
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RUN apt-get update && apt-get install -y \
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python3.10 \
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python3-pip \
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git \
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wget \
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ffmpeg \
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libgl1 \
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libglib2.0-0 \
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libsm6 \
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libxext6 \
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build-essential \
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ninja-build \
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&& rm -rf /var/lib/apt/lists/*
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# 建立 python 软链接，方便直接用 python 命令
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RUN ln -s /usr/bin/python3.10 /usr/bin/python
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WORKDIR /app
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# 3. 升级 pip (使用清华源加速)
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RUN python3 -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple --default-timeout=100
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# 4. 🔥 核心修复：安装适配 RTX 50 系列的 PyTorch (cu128)
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# 这是解决 "sm_120 is not compatible" 的关键一步
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RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 --default-timeout=100
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# 5. 安装 SAM3 依赖 (包含之前报错缺少的 decord, pycocotools)
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# 移除了 transformers 版本限制，使用最新版适配 SAM3
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RUN pip install \
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opencv-python-headless \
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matplotlib \
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jupyter \
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jupyterlab \
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ipympl \
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pyyaml \
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tqdm \
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hydra-core \
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iopath \
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pillow \
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networkx \
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scipy \
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pandas \
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timm \
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einops \
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transformers \
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tokenizers \
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decord \
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pycocotools \
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-i https://pypi.tuna.tsinghua.edu.cn/simple \
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--default-timeout=100
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# 6. 拉取 SAM3 代码
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RUN git clone https://github.com/facebookresearch/sam3.git sam3_code
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# 7. 安装 SAM3 包
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WORKDIR /app/sam3_code
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RUN pip install -e .
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# 8. 设置工作目录和入口
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WORKDIR /app
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EXPOSE 8888
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# 默认保持运行，方便进入终端调试
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CMD ["/bin/bash", "-c", "jupyter lab --ip=0.0.0.0 --port=8888 --allow-root --no-browser --NotebookApp.token='sam3'"]
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