Open pyun-ram opened 1 year ago
Thanks for the nice code! Here is a Dockerfile to support CUDA-version pytorch-cpp. Hope it helps when you want to run the code with GPUs.
FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 LABEL maintainer="pyun@cse.ust.hk" # Fix the apt-get error from nvidia-docker RUN rm /etc/apt/sources.list.d/cuda.list \ && rm /etc/apt/sources.list.d/nvidia-ml.list \ && apt-key del 7fa2af80 \ && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \ && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub # Install basics RUN apt-get update -y \ && apt-get install -y apt-utils git curl ca-certificates tree htop wget libssl-dev unzip \ && rm -rf /var/lib/apt/lists/* # Install g++-8 gcc-8 RUN apt-get update && apt-get install -y gcc-8 g++-8 \ && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 60 --slave /usr/bin/g++ g++ /usr/bin/g++-8 \ && update-alternatives --config gcc \ && rm -rf /var/lib/apt/lists/* # Install cmake RUN apt-get purge -y cmake \ && mkdir /root/temp \ && cd /root/temp \ && wget https://github.com/Kitware/CMake/releases/download/v3.23.4/cmake-3.23.4.tar.gz \ && tar -xzvf cmake-3.23.4.tar.gz \ && cd cmake-3.23.4 \ && bash ./bootstrap \ && make \ && make install \ && cmake --version \ && rm -rf /root/temp \ && rm -rf /var/lib/apt/lists/* # Install libtorch RUN cd /root/ \ && wget https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.12.1%2Bcu102.zip -O libtorch.zip \ && unzip libtorch.zip # Install pytorch-cpp RUN cd /root \ && wget https://github.com/prabhuomkar/pytorch-cpp/archive/refs/tags/v1.12.tar.gz \ && tar -xzvf v1.12.tar.gz RUN cd /root/pytorch-cpp-1.12 \ && cmake -B build \ -D CMAKE_BUILD_TYPE=Release \ -D CMAKE_PREFIX_PATH=/root/libtorch/share/cmake/Torch \ -D CREATE_SCRIPTMODULES=ON \ && cmake --build build WORKDIR /root
@pyun-ram do you mind creating a PR with this feature? We would really appreciate that!
Sure. A PR has been raised. :)
Thanks for the nice code! Here is a Dockerfile to support CUDA-version pytorch-cpp. Hope it helps when you want to run the code with GPUs.