Tensors and Dynamic neural networks in Python with strong GPU acceleration
Switch branches/tags
Clone or download
smessmer and facebook-github-bot Move typeid to c10/util
Summary: Pull Request resolved: #13688

Reviewed By: ezyang

Differential Revision: D12912240

fbshipit-source-id: 1632172003682f62cea9b8c52596c3c0d8504b23
Latest commit 109dd5b Nov 14, 2018
Permalink
Failed to load latest commit information.
.circleci Add caffe2 clang7 build CI job Nov 14, 2018
.github Update issue templates Sep 26, 2018
.jenkins Fix Windows build and test in CI (#11716) Nov 14, 2018
aten Move typeid to c10/util Nov 14, 2018
binaries Do not fill in new data in every iteration if the input data only has… Nov 2, 2018
c10 Move typeid to c10/util Nov 14, 2018
caffe2 Move typeid to c10/util Nov 14, 2018
cmake Fix a bug that causes nvcc to emit an unknown option error (#13904) Nov 13, 2018
conda Remove FULL_CAFFE2 flag (#11321) Sep 7, 2018
docker Switch to packaged Thrust on Ubuntu, enable CentOS 7.5 as a CI target ( Nov 12, 2018
docs Unpin Sphinx. (#13831) Nov 13, 2018
modules Rename ndim() -> dim() - 6/6 Nov 7, 2018
scripts Re-enable experimental ops build (#12821) Oct 29, 2018
submodules 'Re-sync with internal repository' (#12652) Oct 15, 2018
test FileStore auto deletes file and FileStore::add bug fix (#13708) Nov 14, 2018
third_party Update nccl submodule to latest (#13921) Nov 13, 2018
tools Fix Windows build and test in CI (#11716) Nov 14, 2018
torch FileStore auto deletes file and FileStore::add bug fix (#13708) Nov 14, 2018
.clang-format Updates to .clang-format (#7683) May 18, 2018
.clang-tidy Add modernize-* checks to clang-tidy (#13196) Nov 3, 2018
.dockerignore Add .dockerignore. (#3333) Oct 28, 2017
.gitattributes add .gitattributes for EOL conversion. (#9813) Aug 1, 2018
.gitignore Add some more files to gitignore. (#13924) Nov 14, 2018
.gitmodules Remove catch from caffe2/.gitmodules Nov 7, 2018
.travis.aten.yml [build] Have PyTorch depend on minimal libcaffe2.so instead of libATe… May 24, 2018
.travis.yml Fix clang-tidy 404 in Travis Oct 23, 2018
CITATION Recommend citation (implements #4126) (#5955) Mar 23, 2018
CMakeLists.txt build with mkl-dnn by default (#13303) Nov 8, 2018
CODEOWNERS remove onnx CODEOWNERS entries (#12941) Oct 22, 2018
CONTRIBUTING.md Fix clang-tidy for Python2 (#13735) Nov 9, 2018
LICENSE Move copyright lines back to NOTICE file, fixes #6911 (#8310) Jun 12, 2018
Makefile Fix python support problems caused by building script errors. Apr 25, 2017
NOTICE Move copyright lines back to NOTICE file, fixes #6911 (#8310) Jun 12, 2018
README.md Add Linux ppc64le CPU/GPU CI build status Nov 2, 2018
mypy-README.md Change output_declarations in function_wrapper.py to be a NamedTuple (#… Feb 23, 2018
mypy-files.txt Change output_declarations in function_wrapper.py to be a NamedTuple (#… Feb 23, 2018
mypy.ini Change output_declarations in function_wrapper.py to be a NamedTuple (#… Feb 23, 2018
requirements.txt Remove protobuf require and use requirements.txt (#10771) Aug 24, 2018
setup.py Update setup.py to support Nvidia TX2 (#13939) Nov 14, 2018
tox.ini ensure flake8 ignores non-conforming python files generated by build Nov 7, 2018
ubsan.supp Suppress the vptr warning in ubsan (#9909) Jul 27, 2018

README.md

PyTorch Logo


PyTorch is a Python package that provides two high-level features:

  • Tensor computation (like NumPy) with strong GPU acceleration
  • Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

We are in an early-release beta. Expect some adventures and rough edges.

System 2.7 3.5 3.6
Linux CPU Build Status Build Status
Linux GPU Build Status Build Status
Windows GPU Build Status
Linux (ppc64le) CPU Build Status Build Status
Linux (ppc64le) GPU Build Status Build Status

See also the ci.pytorch.org HUD.

More about PyTorch

At a granular level, PyTorch is a library that consists of the following components:

Component Description
torch a Tensor library like NumPy, with strong GPU support
torch.autograd a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch
torch.nn a neural networks library deeply integrated with autograd designed for maximum flexibility
torch.multiprocessing Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training
torch.utils DataLoader, Trainer and other utility functions for convenience
torch.legacy(.nn/.optim) legacy code that has been ported over from torch for backward compatibility reasons

Usually one uses PyTorch either as:

  • a replacement for NumPy to use the power of GPUs.
  • a deep learning research platform that provides maximum flexibility and speed

Elaborating further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a ndarray).

Tensor illustration

PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerates the computation by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, math operations, linear algebra, reductions. And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.

Dynamic graph

Python First

PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use NumPy / SciPy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.

Imperative Experiences

PyTorch is designed to be intuitive, linear in thought and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.

Fast and Lean

PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends (TH, THC, THNN, THCUNN) are mature and have been tested for years.

Hence, PyTorch is quite fast – whether you run small or large neural networks.

The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.

Extensions without Pain

Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward and with minimal abstractions.

You can write new neural network layers in Python using the torch API or your favorite NumPy-based libraries such as SciPy.

If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate. There is no wrapper code that needs to be written. You can see a tutorial here and an example here.

Installation

Binaries

Commands to install from binaries via Conda or pip wheels are on our website:

https://pytorch.org

From Source

If you are installing from source, we highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get a controlled compiler version regardless of your Linux distro.

Once you have Anaconda installed, here are the instructions.

If you want to compile with CUDA support, install

If you want to disable CUDA support, export environment variable NO_CUDA=1. Other potentially useful environment variables may be found in setup.py.

If you want to build on Windows, Visual Studio 2017 14.11 toolset and NVTX are also needed. Especially, for CUDA 8 build on Windows, there will be an additional requirement for VS 2015 Update 3 and a patch for it. The details of the patch can be found out here.

Install optional dependencies

On Linux

export CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" # [anaconda root directory]

# Install basic dependencies
conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
conda install -c mingfeima mkldnn

# Add LAPACK support for the GPU
conda install -c pytorch magma-cuda92 # or [magma-cuda80 | magma-cuda91] depending on your cuda version

On macOS

export CMAKE_PREFIX_PATH=[anaconda root directory]
conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing

On Windows

conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing

Get the PyTorch source

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch

Install PyTorch

On Linux

python setup.py install

On macOS

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

On Windows

set "VS150COMNTOOLS=C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build"
set CMAKE_GENERATOR=Visual Studio 15 2017 Win64
set DISTUTILS_USE_SDK=1
REM The following two lines are needed for Python 2.7, but the support for it is very experimental.
set MSSdk=1
set FORCE_PY27_BUILD=1
REM As for CUDA 8, VS2015 Update 3 is also required to build PyTorch. Use the following line.
set "CUDAHOSTCXX=%VS140COMNTOOLS%\..\..\VC\bin\amd64\cl.exe"

call "%VS150COMNTOOLS%\vcvarsall.bat" x64 -vcvars_ver=14.11
python setup.py install

Docker image

Dockerfile is supplied to build images with cuda support and cudnn v7. You can pass -e PYTHON_VERSION=x.y flag to specificy which python to be used by Miniconda, or leave it unset to use the default. Build as usual

docker build -t pytorch -f docker/pytorch/Dockerfile .

You can also pull a pre-built docker image from Docker Hub and run with nvidia-docker, but this is not currently maintained and will pull PyTorch 0.2.

nvidia-docker run --rm -ti --ipc=host pytorch/pytorch:latest

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

Building the Documentation

To build documentation in various formats, you will need Sphinx and the readthedocs theme.

cd docs/
pip install -r requirements.txt

You can then build the documentation by running make <format> from the docs/ folder. Run make to get a list of all available output formats.

Previous Versions

Installation instructions and binaries for previous PyTorch versions may be found on our website.

Getting Started

Three pointers to get you started:

Communication

  • forums: discuss implementations, research, etc. https://discuss.pytorch.org
  • GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
  • Slack: general chat, online discussions, collaboration etc. https://pytorch.slack.com/ . Our slack channel is invite-only to promote a healthy balance between power-users and beginners. If you need a slack invite, ping us at slack@pytorch.org
  • newsletter: no-noise, one-way email newsletter with important announcements about pytorch. You can sign-up here: https://eepurl.com/cbG0rv

Releases and Contributing

PyTorch has a 90 day release cycle (major releases). Its current state is Beta, we expect no obvious bugs. Please let us know if you encounter a bug by filing an issue.

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.

The Team

PyTorch is a community driven project with several skillful engineers and researchers contributing to it.

PyTorch is currently maintained by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan with major contributions coming from 10s of talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Kopf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.

Note: this project is unrelated to hughperkins/pytorch with the same name. Hugh is a valuable contributor in the Torch community and has helped with many things Torch and PyTorch.

License

PyTorch is BSD-style licensed, as found in the LICENSE file.