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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.

• More about PyTorch
• Installation
  • Binaries
  • From Source
  • Docker Image
  • Building the Documentation
  • Previous Versions
• Getting Started
• Communication
• Releases and Contributing
• The Team

System	2.7	3.5	3.6
Linux CPU			—
Linux GPU			—
Windows CPU / GPU	—		—
Linux (ppc64le) CPU		—
Linux (ppc64le) GPU		—

See also theci.pytorch.org HUD.

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

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:

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

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

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

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 tochange the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comesfrom several research papers on this topic, as well as current and past work such astorch-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.

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 useNumPy /SciPy /scikit-learn etc.You can write your new neural network layers in Python itself, using your favorite librariesand use packages such as Cython and Numba.Our goal is to not reinvent the wheel where appropriate.

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.

PyTorch has minimal framework overhead. We integrate acceleration librariessuch asIntel 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 thatyour deep learning models are maximally memory efficient.This enables you to train bigger deep learning models than before.

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

You can write new neural network layers in Python using the torch APIor 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 seea tutorial here andan example here.

Commands to install from binaries via Conda or pip wheels are on our website:https://pytorch.org

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

Once you haveAnaconda installed, here are the instructions.

If you want to compile with CUDA support, install

• NVIDIA CUDA 7.5 or above
• NVIDIA cuDNN v6.x or above

If you want to disable CUDA support, export environment variableNO_CUDA=1.Other potentially useful environment variables may be found insetup.py.

Common

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

On Linux

# Add LAPACK support for the GPU if neededconda install -c pytorch magma-cuda90# or [magma-cuda80 | magma-cuda92 | magma-cuda100 ] depending on your cuda version

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

On Linux

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname$(which conda))/../"}python setup.py install

On macOS

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname$(which conda))/../"}MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

On Windows

At least Visual Studio 2017 Update 3 (version 15.3.3 with the toolset 14.11) andNVTX are needed.

If the version of Visual Studio 2017 is higher than 15.4.5, installing of "VC++ 2017 version 15.4 v14.11 toolset" is strongly recommended.
If the version of Visual Studio 2017 is lesser than 15.3.3, please update Visual Studio 2017 to the latest version along with installing "VC++ 2017 version 15.4 v14.11 toolset".
There is no guarantee of the correct building with VC++ 2017 toolsets, others than version 15.4 v14.11.
"VC++ 2017 version 15.4 v14.11 toolset" might be installed onto already installed Visual Studio 2017 by running its installation once again and checking the corresponding checkbox under "Individual components"/"Compilers, build tools, and runtimes".

For building against CUDA 8.0 Visual Studio 2015 Update 3 (version 14.0), and thepatch are needed to be installed too.The details of the patch can be foundhere.

NVTX is a part of CUDA distributive, where it is called "Nsight Compute". For installing it onto already installed CUDA run CUDA installation once again and check the corresponding checkbox.Be sure that CUDA with Nsight Compute is installed after Visual Studio 2017.

cmdREM [Optional] The following two lines are needed for Python 2.7, but the support for it is very experimental.setMSSdk=1setFORCE_PY27_BUILD=1REM [Optional] As for CUDA 8, VS2015 Update 3 is required; use the following line.set"CUDAHOSTCXX=%VS140COMNTOOLS%..\..\VC\bin\amd64\cl.exe"setCMAKE_GENERATOR=Visual Studio152017 Win64setDISTUTILS_USE_SDK=1REM Run "Visual Studio 2017 Developer Command Prompt"for /f"usebackq tokens=*"%i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,16^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=14.11python setup.py install

Dockerfile is supplied to build images with cuda support and cudnn v7. You can pass-e PYTHON_VERSION=x.y flag to specify which Python version is to be used by Miniconda, or leave it unset to use the default. Build from pytorch repo directory as docker needs to copy git repo into docker filesystem while building the image.

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 youshould increase shared memory size either with--ipc=host or--shm-size command line options tonvidia-docker run.

To build documentation in various formats, you will needSphinx and thereadthedocs theme.

cd docs/pip install -r requirements.txt

You can then build the documentation by runningmake <format> from thedocs/ folder. Runmake to get a list of all available output formats.

Installation instructions and binaries for previous PyTorch versions may be foundonour website.

Three pointers to get you started:

• Tutorials: get you started with understanding and using PyTorch
• Examples: easy to understand pytorch code across all domains
• The API Reference

• forums: discuss implementations, research, etc.https://discuss.pytorch.org
• GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
• Slack: ThePyTorch Slack hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration etc. If you are a beginner looking for help, the primary medium isPyTorch Forums. If you need a slack invite, please fill this form:https://goo.gl/forms/PP1AGvNHpSaJP8to1
• newsletter: no-noise, one-way email newsletter with important announcements about pytorch. You can sign-up here:https://eepurl.com/cbG0rv

PyTorch has a 90 day release cycle (major releases). Please let us know if you encounter a bug byfiling 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.

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

PyTorch is currently maintained byAdam Paszke,Sam Gross,Soumith Chintala andGregory Chanan with major contributions coming from hundreds 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 tohughperkins/pytorch with the same name. Hugh is a valuable contributor in the Torch community and has helped with many things Torch and PyTorch.

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