Package: torch
PyTorch is a Python package that provides two high-level features:Tensor computation (like NumPy) with strong GPU accelerationDeep neural networks built on a tape-based autograd systemYou can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.Our trunk health (Continuous Integration signals) can be found athud.pytorch.org.More About PyTorchA GPU-Ready Tensor LibraryDynamic Neural Networks: Tape-Based AutogradPython FirstImperative ExperiencesFast and LeanExtensions Without PainInstallationBinariesNVIDIA Jetson PlatformsFrom SourcePrerequisitesInstall DependenciesGet the PyTorch SourceInstall PyTorchAdjust Build Options (Optional)Docker ImageUsing pre-built imagesBuilding the image yourselfBuilding the DocumentationPrevious VersionsGetting StartedResourcesCommunicationReleases and ContributingThe TeamLicenseMore About PyTorchLearn the basics of PyTorchAt a granular level, PyTorch is a library that consists of the following components:ComponentDescriptiontorchA Tensor library like NumPy, with strong GPU supporttorch.autogradA tape-based automatic differentiation library that supports all differentiable Tensor operations in torchtorch.jitA compilation stack (TorchScript) to create serializable and optimizable models from PyTorch codetorch.nnA neural networks library deeply integrated with autograd designed for maximum flexibilitytorch.multiprocessingPython multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild trainingtorch.utilsDataLoader and other utility functions for convenienceUsually, PyTorch is used 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 LibraryIf 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 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, mathematical operations, linear algebra, reductions.
And they are fast!Dynamic Neural Networks: Tape-Based AutogradPyTorch 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 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.Python FirstPyTorch 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-learnetc.
You can write your new neural network layers in Python itself, using your favorite libraries
and use packages such asCythonandNumba.
Our goal is to not reinvent the wheel where appropriate.Imperative ExperiencesPyTorch 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 LeanPyTorch has minimal framework overhead. We integrate acceleration libraries
such asIntel MKLand NVIDIA (cuDNN,NCCL) to maximize speed.
At the core, its CPU and GPU Tensor and neural network backends
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 PainWriting 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 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.
No wrapper code needs to be written. You can seea tutorial hereandan example here.InstallationBinariesCommands to install binaries via Conda or pip wheels are on our website:https://pytorch.org/get-started/locally/NVIDIA Jetson PlatformsPython wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are providedhereand the L4T container is publishedhereThey require JetPack 4.2 and above, and@dusty-nvand@ptrblckare maintaining them.From SourcePrerequisitesIf you are installing from source, you will need:Python 3.8 or later (for Linux, Python 3.8.1+ is needed)A compiler that fully supports C++17, such as clang or gcc (especially for aarch64, gcc 9.4.0 or newer is required)We highly recommend installing anAnacondaenvironment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.If you want to compile with CUDA support,select a supported version of CUDA from our support matrix, then install the following:NVIDIA CUDANVIDIA cuDNNv7 or aboveCompilercompatible with CUDANote: You could refer to thecuDNN Support Matrixfor cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardwareIf you want to disable CUDA support, export the environment variableUSE_CUDA=0.
Other potentially useful environment variables may be found insetup.py.If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano areavailable hereIf you want to compile with ROCm support, installAMD ROCm4.0 and above installationROCm is currently supported only for Linux systems.If you want to disable ROCm support, export the environment variableUSE_ROCM=0.
Other potentially useful environment variables may be found insetup.py.Install DependenciesCommoncondainstallcmakeninja# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section belowpipinstall-rrequirements.txtOn Linuxcondainstallmklmkl-include# CUDA only: Add LAPACK support for the GPU if neededcondainstall-cpytorchmagma-cuda110# or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo# (optional) If using torch.compile with inductor/triton, install the matching version of triton# Run from the pytorch directory after cloningmaketritonOn MacOS# Add this package on intel x86 processor machines onlycondainstallmklmkl-include# Add these packages if torch.distributed is neededcondainstallpkg-configlibuvOn Windowscondainstallmklmkl-include# Add these packages if torch.distributed is needed.# Distributed package support on Windows is a prototype feature and is subject to changes.condainstall-cconda-forgelibuv=1.39Get the PyTorch Sourcegitclone--recursivehttps://github.com/pytorch/pytorchcdpytorch# if you are updating an existing checkoutgitsubmodulesync
gitsubmoduleupdate--init--recursiveInstall PyTorchOn LinuxIf you would like to compile PyTorch withnew C++ ABIenabled, then first run this command:export_GLIBCXX_USE_CXX11_ABI=1If you're compiling for AMD ROCm then first run this command:# Only run this if you're compiling for ROCmpythontools/amd_build/build_amd.pyInstall PyTorchexportCMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname$(whichconda))/../"}pythonsetup.pydevelopAside:If you are usingAnaconda, you may experience an error caused by the linker:build/temp.linux-x86_64-3.7/torch/csrc/stub.o: file not recognized: file format not recognized
collect2: error: ld returned 1 exit status
error: command 'g++' failed with exit status 1This is caused byldfrom the Conda environment shadowing the systemld. You should use a newer version of Python that fixes this issue. The recommended Python version is 3.8.1+.On macOSpython3setup.pydevelopOn WindowsChoose Correct Visual Studio Version.PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise,
Professional, or Community Editions. You can also install the build tools fromhttps://visualstudio.microsoft.com/visual-cpp-build-tools/. The build toolsdo notcome with Visual Studio Code by default.If you want to build legacy python code, please refer toBuilding on legacy code and CUDACPU-only buildsIn this mode PyTorch computations will run on your CPU, not your GPUconda activate
python setup.py developNote on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweakingCMAKE_INCLUDE_PATHandLIB. The instructionhereis an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.CUDA based buildIn this mode PyTorch computations will leverage your GPU via CUDA for faster number crunchingNVTXis needed to build Pytorch with CUDA.
NVTX is a part of CUDA distributive, where it is called "Nsight Compute". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox.
Make sure that CUDA with Nsight Compute is installed after Visual Studio.Currently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. Ifninja.exeis detected inPATH, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.Additional libraries such asMagma,oneDNN, a.k.a. MKLDNN or DNNL, andSccacheare often needed. Please refer to theinstallation-helperto install them.You can refer to thebuild_pytorch.batscript for some other environment variables configurationscmd

:: Set the environment variables after you have downloaded and unzipped the mkl package,
:: else CMake would throw an error as `Could NOT find OpenMP`.
set CMAKE_INCLUDE_PATH={Your directory}\mkl\include
set LIB={Your directory}\mkl\lib;%LIB%

:: Read the content in the previous section carefully before you proceed.
:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.
:: "Visual Studio 2019 Developer Command Prompt" will be run automatically.
:: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.
set CMAKE_GENERATOR_TOOLSET_VERSION=14.27
set DISTUTILS_USE_SDK=1
for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%

:: [Optional] If you want to override the CUDA host compiler
set CUDAHOSTCXX=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX64\x64\cl.exe

python setup.py developAdjust Build Options (Optional)You can adjust the configuration of cmake variables optionally (without building first), by doing
the following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done
with such a step.On LinuxexportCMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname$(whichconda))/../"}pythonsetup.pybuild--cmake-only
ccmakebuild# or cmake-gui buildOn macOSexportCMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname$(whichconda))/../"}MACOSX_DEPLOYMENT_TARGET=10.9CC=clangCXX=clang++pythonsetup.pybuild--cmake-only
ccmakebuild# or cmake-gui buildDocker ImageUsing pre-built imagesYou can also pull a pre-built docker image from Docker Hub and run with docker v19.03+dockerrun--gpusall--rm-ti--ipc=hostpytorch/pytorch:latestPlease 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=hostor--shm-sizecommand line options tonvidia-docker run.Building the image yourselfNOTE:Must be built with a docker version > 18.06TheDockerfileis supplied to build images with CUDA 11.1 support and cuDNN v8.
You can passPYTHON_VERSION=x.ymake variable to specify which Python version is to be used by Miniconda, or leave it
unset to use the default.make-fdocker.Makefile# images are tagged as docker.io/${your_docker_username}/pytorchYou can also pass theCMAKE_VARS="..."environment variable to specify additional CMake variables to be passed to CMake during the build.
Seesetup.pyfor the list of available variables.CMAKE_VARS="BUILD_CAFFE2=ON BUILD_CAFFE2_OPS=ON"make-fdocker.MakefileBuilding the DocumentationTo build documentation in various formats, you will needSphinxand the
readthedocs theme.cddocs/
pipinstall-rrequirements.txtYou can then build the documentation by runningmake <format>from thedocs/folder. Runmaketo get a list of all available output formats.If you get a katex error runnpm install katex.  If it persists, trynpm install -g katexNote: if you installednodejswith a different package manager (e.g.,conda) thennpmwill probably install a version ofkatexthat is not
compatible with your version ofnodejsand doc builds will fail.
A combination of versions that is known to work isnode@6.13.1andkatex@0.13.18. To install the latter withnpmyou can runnpm install -g katex@0.13.18Previous VersionsInstallation instructions and binaries for previous PyTorch versions may be found
onour website.Getting StartedThree-pointers to get you started:Tutorials: get you started with understanding and using PyTorchExamples: easy to understand PyTorch code across all domainsThe API ReferenceGlossaryResourcesPyTorch.orgPyTorch TutorialsPyTorch ExamplesPyTorch ModelsIntro to Deep Learning with PyTorch from UdacityIntro to Machine Learning with PyTorch from UdacityDeep Neural Networks with PyTorch from CourseraPyTorch TwitterPyTorch BlogPyTorch YouTubeCommunicationForums: Discuss implementations, research, etc.https://discuss.pytorch.orgGitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.Slack: ThePyTorch Slackhosts 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/PP1AGvNHpSaJP8to1Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here:https://eepurl.com/cbG0rvFacebook Page: Important announcements about PyTorch.https://www.facebook.com/pytorchFor brand guidelines, please visit our website atpytorch.orgReleases and ContributingTypically, PyTorch has three minor releases a year. 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.To learn more about making a contribution to Pytorch, please see ourContribution page. For more information about PyTorch releases, seeRelease page.The TeamPyTorch is a community-driven project with several skillful engineers and researchers contributing to it.PyTorch is currently maintained bySoumith Chintala,Gregory Chanan,Dmytro Dzhulgakov,Edward Yang, andNikita Shulgawith 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 Koepf, 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/pytorchwith the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.LicensePyTorch has a BSD-style license, as found in theLICENSEfile.