62 lines
2.2 KiB
Docker
Executable File
62 lines
2.2 KiB
Docker
Executable File
# 使用 devel 镜像，确保有 nvcc + CUDA Toolkit
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FROM nvidia/cuda:12.8.0-devel-ubuntu22.04
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# 安装基本系统依赖（apt 清理缓存节省空间）
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RUN apt-get update && apt-get install -y --no-install-recommends \
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wget \
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git \
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build-essential \
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cmake \
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ninja-build \
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libglib2.0-0 \
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libgl1 \
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python3.10 \
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python3.10-dev \
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python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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# 安装 miniconda（推荐用官方最新版）
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RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda.sh && \
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bash /tmp/miniconda.sh -b -p /opt/conda && \
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rm /tmp/miniconda.sh && \
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/opt/conda/bin/conda clean --all -y
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# 把 conda 加到 PATH
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ENV PATH="/opt/conda/bin:${PATH}"
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# 接受 ToS（关键修复点）
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RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \
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conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
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#
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## 创建环境
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#RUN conda create -n trellis python=3.10 -y && \
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# echo "conda activate trellis" >> ~/.bashrc
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#
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#SHELL ["/bin/bash", "-c"]
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#
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## 激活环境后安装 PyTorch 2.8 nightly + CUDA 12.8
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#RUN conda activate trellis && \
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# conda install pytorch torchvision torchaudio pytorch-cuda=12.8 -c pytorch-nightly -c nvidia -y
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#
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## 安装 pytorch3d（优先用 fvcore 频道，如果没有则从 git 源码编译）
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#RUN conda activate trellis && \
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# conda install pytorch3d -c fvcore -c pytorch -c nvidia -y || \
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# pip install --no-cache-dir "git+https://github.com/facebookresearch/pytorch3d.git@stable"
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#
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## 如果你有 trellis.tar.gz 打包的环境，可以继续 COPY 并 unpack（可选）
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## COPY trellis.tar.gz /opt/
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## RUN mkdir /opt/env && tar -xzf /opt/trellis.tar.gz -C /opt/env && /opt/env/bin/conda-unpack
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#
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## 安装 kaolin（你的原 Dockerfile 有这个）
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#RUN conda activate trellis && \
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# pip install kaolin==0.18.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.8.0_cu128.html
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# 设置 PATH 和工作目录
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ENV PATH="/opt/conda/envs/trellis/bin:${PATH}"
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WORKDIR /workspace
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# 复制你的代码（如果需要）
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COPY . /workspace
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# 默认命令：保持容器运行，或换成你的启动脚本
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CMD ["tail", "-f", "/dev/null"] |