PyG 安装

我们已经将 PyTorch Geometric 的许多功能外包给其他软件包，需要预先安装。 These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions introduced in PyTorch 0.4.0.

Note

我们不建议以root用户身份在系统python上进行安装。 请设置 Anaconda/Miniconda 环境或创建 Docker image 映像。

安装步骤

请按照以下步骤来安装：

1. 确保已安装PyTorch 1.4.0：

   $ python -c "import torch; print(torch.__version__)"
   >>> 1.4.0
   

2. 确保正确设置了CUDA（可选）：

   1. Check if PyTorch is installed with CUDA support:

      $ python -c "import torch; print(torch.cuda.is_available())"
      >>> True
      

   2. Add CUDA to $PATH and $CPATH (note that your actual CUDA path may vary from /usr/local/cuda):

      $ export PATH=/usr/local/cuda/bin:$PATH
      $ echo $PATH
      >>> /usr/local/cuda/bin:...
      
      $ export CPATH=/usr/local/cuda/include:$CPATH
      $ echo $CPATH
      >>> /usr/local/cuda/include:...
      

   3. Add CUDA to $LD_LIBRARY_PATH on Linux and to $DYLD_LIBRARY_PATH on macOS (note that your actual CUDA path may vary from /usr/local/cuda):

      $ export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
      $ echo $LD_LIBRARY_PATH
      >>> /usr/local/cuda/lib64:...
      
      $ export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH
      $ echo $DYLD_LIBRARY_PATH
      >>> /usr/local/cuda/lib:...
      

   4. Verify that nvcc is accessible from terminal:

      $ nvcc --version
      >>> 10.0
      

   5. Ensure that PyTorch and system CUDA versions match:

      $ python -c "import torch; print(torch.version.cuda)"
      >>> 10.0
      
      $ nvcc --version
      >>> 10.0
      

3. 安装所有需要的软件包 with ${CUDA} replaced by either cpu, cu92, cu100 or cu101 depending on your PyTorch installation:

   $ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
   $ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
   $ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
   $ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
   $ python setup.py install or pip install torch-geometric
   

In rare cases, CUDA or Python path problems can prevent a successful installation. pip may even signal a successful installation, but runtime errors complain about missing modules, .e.g., No module named 'torch_*.*_cuda', or execution simply crashes with Segmentation fault (core dumped). We collected a lot of common installation errors in the Frequently Asked Questions subsection. In case the FAQ does not help you in solving your problem, please create an issue. You should additionally verify that your CUDA is set up correctly by following the official installation guide, and that the official extension example runs on your machine.

C++/CUDA Extensions on macOS

In order to compile CUDA extensions on macOS with Python <3.7, you need to replace the call

def spawn(self, cmd):
    spawn(cmd, dry_run=self.dry_run)

with

import subprocess

def spawn(self, cmd):
    subprocess.call(cmd)

in lib/python{xxx}/distutils/ccompiler.py.

常见问题集

1. ImportError: ***: cannot open shared object file: No such file or directory: Add CUDA to your $LD_LIBRARY_PATH (see Issue#43).

2. undefined symbol:, e.g. _ZN2at6detail20DynamicCUDAInterface10set_deviceE: Clear the pip cache and reinstall the respective package (see Issue#7). On macOS, it may help to install clang compilers via conda (see Issue#18):

   $ conda install -y clang_osx-64 clangxx_osx-64 gfortran_osx-64
   

3. Unable to import *_cuda: You need to import torch first before importing any of the extension packages (see Issue#6).

4. error: command '/usr/bin/nvcc' failed with exit status 2: Ensure that at least CUDA >= 8 is installed (see Issue#25a and Issue#106).

5. return __and_<is_constructible<_Elements, _UElements&&>...>::value: Ensure that your gcc version is at least 4.9 (and below 6) (see Issue#25b). You will also need to reinstall PyTorch because gcc versions must be consistent across all PyTorch packages.

6. file not recognized: file format not recognized: Clean the repository and temporarily rename Anaconda’s ld linker (see Issue#16683).

7. undefined symbol: __cudaPopCallConfiguration: Ensure that your PyTorch CUDA version and system CUDA version match (see Issue#19):

   $ python -c "import torch; print(torch.version.cuda)"
   $ nvcc --version
   

8. undefined symbol: _ZN3c105ErrorC1ENS_14SourceLocationERKSs: The std::string abi does not match between building PyTorch and its extensions. This is fixable by building extensions with -D_GLIBCXX_USE_CXX11_ABI=1 or building PyTorch from source (see this PyTorch thread).

9. On macOS: 'gcc' failed with exit status 1: Install the respective packages by using the following environment variables (see Issue#21):

   $ MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
   

10. On macOS: ld: warning: directory not found for option '-L/usr/local/cuda/lib64' and ld: library not found for -lcudart: Symlink cuda/lib to cuda/lib64 (see Issue#116):

    $ sudo ln -s /usr/local/cuda/lib /usr/local/cuda/lib64
    

11. On macOS: The version of the host compiler ('Apple clang') is not supported: Downgrade your command line tools (see this StackOverflow thread) with the respective version annotated in the CUDA Installation Guide for Mac (Section 1.1) for your specific CUDA version. You can download previous command line tool versions here.