Installation

Docker

We recommend using docker images if docker or other container runtimes e.g. singularity is available on your devices.

We maintain a prebuilt image at GithubPackages:

ghcr.io/thinklab-sjtu/thinkmatch:torch1.6.0-cuda10.1-cudnn7-pyg1.6.3-pygmtools0.2.0

It can be used by docker or other container runtimes that support docker images e.g. singularity. If you are using docker, run the following command to pull the image:

docker pull ghcr.io/thinklab-sjtu/thinkmatch:torch1.6.0-cuda10.1-cudnn7-pyg1.6.3-pygmtools0.2.0

Other docker images with different torch/cuda/pyg combinations are also provided to fit the needs of various GPU devices. Please check from the Internet which CUDA version best suits your GPU.

ghcr.io/thinklab-sjtu/thinkmatch:torch1.6.0-cuda10.1-cudnn7-pyg1.6.3-pygmtools0.2.0 # recommended for GTX10 and RTX20 GPUs
ghcr.io/thinklab-sjtu/thinkmatch:torch1.7.1-cuda11.0-cudnn8-pyg1.6.3-pygmtools0.2.0
ghcr.io/thinklab-sjtu/thinkmatch:torch1.10.0-cuda11.3-cudnn8-pyg2.0.3-pygmtools0.2.0 # recommended for RTX30 GPUs

Note

It is recommended to use the torch1.6.0-cuda10.1 image if it is compatible with your devices, because our code are developed and tested on this image. If you are encountering any issues on other images, we may not guarantee to quickly resolve them because we do not have the same module as yours.

Note

If you find the existing image does not suit your need and you want a new image, please raise an issue and provide the torch/cuda/cudnn/pyg/pygmtools versions you required.

For more information about the docker images, please check out ThinkMatch-runtime

Manual configuration

This repository is developed and tested with Ubuntu 16.04, Python 3.7, Pytorch 1.6, cuda10.1, cudnn7 and torch-geometric 1.6.3. If docker is not available, we provide detailed steps to install the requirements by apt and pip.

  1. Install and configure Pytorch 1.6 (with GPU support). Please follow the official guidelines.

  2. Install ninja-build:
    apt-get install ninja-build
    
  3. Install python packages:
    pip install tensorboardX scipy easydict pyyaml xlrd xlwt pynvml pygmtools
    
  4. Install building tools for LPMP:
    apt-get install -y findutils libhdf5-serial-dev git wget libssl-dev
    
    wget https://github.com/Kitware/CMake/releases/download/v3.19.1/cmake-3.19.1.tar.gz && tar zxvf cmake-3.19.1.tar.gz
    cd cmake-3.19.1 && ./bootstrap && make && make install
    
  5. Install and build LPMP:
    python -m pip install git+https://git@github.com/rogerwwww/lpmp.git
    

    You may need gcc-9 to successfully build LPMP. Here we provide an example installing and configuring gcc-9:

    apt-get update
    apt-get install -y software-properties-common
    add-apt-repository ppa:ubuntu-toolchain-r/test
    
    apt-get install -y gcc-9 g++-9
    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-9
    
  6. Install torch-geometric:
    export CUDA=cu101
    export TORCH=1.6.0
    /opt/conda/bin/pip install torch-scatter==2.0.5 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
    /opt/conda/bin/pip install torch-sparse==0.6.8 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
    /opt/conda/bin/pip install torch-cluster==1.5.8 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
    /opt/conda/bin/pip install torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
    /opt/conda/bin/pip install torch-geometric==1.6.3
    
  7. If you have configured gcc-9 to build LPMP, be sure to switch back to gcc-7 because ThinkMatch is based on gcc-7. Here is also an example:
    update-alternatives --remove gcc /usr/bin/gcc-9
    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 60 --slave /usr/bin/g++ g++ /usr/bin/g++-7