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：
#. 确保已安装PyTorch 1.4.0：
.. code-block:: none
$ python -c "import torch; print(torch.__version__)"
>>> 1.4.0
#. 确保正确设置了CUDA（可选）：
#. Check if PyTorch is installed with CUDA support:
.. code-block:: none
$ python -c "import torch; print(torch.cuda.is_available())"
>>> True
#. Add CUDA to ``$PATH`` and ``$CPATH`` (note that your actual CUDA path may vary from ``/usr/local/cuda``):
.. code-block:: none
$ export PATH=/usr/local/cuda/bin:$PATH
$ echo $PATH
>>> /usr/local/cuda/bin:...
$ export CPATH=/usr/local/cuda/include:$CPATH
$ echo $CPATH
>>> /usr/local/cuda/include:...
#. Add CUDA to ``$LD_LIBRARY_PATH`` on Linux and to ``$DYLD_LIBRARY_PATH`` on macOS (note that your actual CUDA path may vary from ``/usr/local/cuda``):
.. code-block:: none
$ 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:...
#. Verify that ``nvcc`` is accessible from terminal:
.. code-block:: none
$ nvcc --version
>>> 10.0
#. Ensure that PyTorch and system CUDA versions match:
.. code-block:: none
$ python -c "import torch; print(torch.version.cuda)"
>>> 10.0
$ nvcc --version
>>> 10.0
#. 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:
.. code-block:: none
$ 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
.. code-block:: python
def spawn(self, cmd):
spawn(cmd, dry_run=self.dry_run)
with
.. code-block:: python
import subprocess
def spawn(self, cmd):
subprocess.call(cmd)
in ``lib/python{xxx}/distutils/ccompiler.py``.
常见问题集
--------------------------
#. ``ImportError: ***: cannot open shared object file: No such file or directory``: Add CUDA to your ``$LD_LIBRARY_PATH`` (see `Issue#43 `_).
#. ``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 `_):
.. code-block:: none
$ conda install -y clang_osx-64 clangxx_osx-64 gfortran_osx-64
#. Unable to import ``*_cuda``: You need to ``import torch`` first before importing any of the extension packages (see `Issue#6 `_).
#. ``error: command '/usr/bin/nvcc' failed with exit status 2``: Ensure that at least CUDA >= 8 is installed (see `Issue#25a `_ and `Issue#106 `_).
#. ``return __and_...>::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.
#. ``file not recognized: file format not recognized``: Clean the repository and temporarily rename Anaconda's ``ld`` linker (see `Issue#16683 `_).
#. ``undefined symbol: __cudaPopCallConfiguration``: Ensure that your PyTorch CUDA version and system CUDA version match (see `Issue#19 `_):
.. code-block:: none
$ python -c "import torch; print(torch.version.cuda)"
$ nvcc --version
#. ``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 `_).
#. On macOS: ``'gcc' failed with exit status 1``: Install the respective packages by using the following environment variables (see `Issue#21 `_):
.. code-block:: none
$ MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
#. 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 `_):
.. code-block:: none
$ sudo ln -s /usr/local/cuda/lib /usr/local/cuda/lib64
#. 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 `_.