If you are interested in contributing to PyTorch, your contributions will fall into two categories:
- You want to propose a new Feature and implement it
- post about your intended feature, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
- You want to implement a feature or bug-fix for an outstanding issue
- Look at the outstanding issues here: https://github.com/pytorch/pytorch/issues
- Especially look at the Low Priority and Medium Priority issues
- Pick an issue and comment on the task that you want to work on this feature
- If you need more context on a particular issue, please ask and we shall provide.
Once you finish implementing a feature or bugfix, please send a Pull Request to https://github.com/pytorch/pytorch
If you are not familiar with creating a Pull Request, here are some guides:
- http://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request
- https://help.github.com/articles/creating-a-pull-request/
To locally develop with PyTorch, here are some tips:
- Uninstall all existing pytorch installs
conda uninstall pytorch
pip uninstall torch
pip uninstall torch # run this command twice
- Locally clone a copy of PyTorch from source:
git clone https://github.com/pytorch/pytorch
cd pytorch
- Install PyTorch in
build develop
mode:
A full set of instructions on installing PyTorch from Source are here: https://github.com/pytorch/pytorch#from-source
The change you have to make is to replace
python setup.py install
with
python setup.py build develop
This is especially useful if you are only changing Python files.
This mode will symlink the python files from the current local source tree into the python install.
Hence, if you modify a python file, you do not need to reinstall pytorch again and again.
For example:
- Install local pytorch in
build develop
mode - modify your python file
torch/__init__.py
(for example) - test functionality
- modify your python file
torch/__init__.py
- test functionality
- modify your python file
torch/__init__.py
- test functionality
You do not need to repeatedly install after modifying python files.
One downside to using python setup.py develop
is that your development
version of pytorch will be installed globally on your account (e.g., if
you run import torch
anywhere else, the development version will be
used.
If you want to manage multiple builds of PyTorch, you can make use of conda environments to maintain separate Python package environments, each of which can be tied to a specific build of PyTorch. To set one up:
conda create -n pytorch-myfeature
source activate pytorch-myfeature
# if you run python now, torch will NOT be installed
python setup.py build develop
If you are working on the C++ code, there are a few important things that you will want to keep in mind:
- How to rebuild only the code you are working on, and
- How to make rebuilds in the absence of changes go faster.
python setup.py build
will build everything, but since our build system is
not very optimized for incremental rebuilds, this will actually be very slow.
Far better is to only request rebuilds of the parts of the project you are
working on:
-
Working on
torch/csrc
? Runpython setup.py develop
to rebuild (NB: nobuild
here!) -
Working on
torch/lib/TH
, did not make any cmake changes, and just want to see if it compiles? Run(cd torch/lib/build/TH && make install -j$(getconf _NPROCESSORS_ONLN))
. This applies for any other subdirectory oftorch/lib
. Warning: Changes you make here will not be visible from Python. See below. -
Working on
torch/lib
and want to run your changes / rerun cmake? Runpython setup.py build_deps
. Note that this will rerun cmake for every subdirectory in TH; if you are only working on one project, consider editingtorch/lib/build_all.sh
and commenting out thebuild
lines of libraries you are not working on.
On the initial build, you can also speed things up with the environment
variables DEBUG
and NO_CUDA
.
DEBUG=1
will enable debug builds (-g -O0)NO_CUDA=1
will disable compiling CUDA (in case you are developing on something not CUDA related), to save compile time.
For example:
NO_CUDA=1 DEBUG=1 python setup.py build develop
Make sure you continue to pass these flags on subsequent builds.
Python setuptools
is pretty dumb, and always rebuilds every C file in a
project. Using ccache in a situation like this is a real time-saver. However, by
default, ccache does not properly support CUDA stuff, so here are the
instructions for installing a custom ccache
fork that has CUDA support:
# install and export ccache
if ! ls ~/ccache/bin/ccache
then
sudo apt-get update
sudo apt-get install -y automake autoconf
sudo apt-get install -y asciidoc
mkdir -p ~/ccache
pushd /tmp
rm -rf ccache
git clone https://github.com/colesbury/ccache -b ccbin
pushd ccache
./autogen.sh
./configure
make install prefix=~/ccache
popd
popd
mkdir -p ~/ccache/lib
mkdir -p ~/ccache/cuda
ln -s ~/ccache/bin/ccache ~/ccache/lib/cc
ln -s ~/ccache/bin/ccache ~/ccache/lib/c++
ln -s ~/ccache/bin/ccache ~/ccache/lib/gcc
ln -s ~/ccache/bin/ccache ~/ccache/lib/g++
ln -s ~/ccache/bin/ccache ~/ccache/cuda/nvcc
~/ccache/bin/ccache -M 25Gi
fi
export PATH=~/ccache/lib:$PATH
export CUDA_NVCC_EXECUTABLE=~/ccache/cuda/nvcc
Hope this helps, and thanks for considering to contribute.