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.

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    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.jit a compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code
    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 and other utility functions for convenience

    Usually, 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 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. No wrapper code 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

    NVIDIA Jetson Platforms

    Python wheels for NVIDIA's Jetson Nano, Jetson TX2, and Jetson AGX Xavier are available via the following URLs:

    They require JetPack 4.2 and above, and @dusty-nv maintains them

    From Source

    If you are installing from source, you will need Python 3.6.2 or later and a C++14 compiler. Also, we highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions 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 USE_CUDA=0. Other potentially useful environment variables may be found in setup.py.

    If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are available here

    If you want to compile with ROCm support, install

    • AMD ROCm 4.0 and above installation
    • ROCm is currently supported only for Linux system.

    If you want to disable ROCm support, export environment variable USE_ROCM=0. Other potentially useful environment variables may be found in setup.py.

    Install Dependencies

    Common

    conda install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses

    On Linux

    # CUDA only: Add LAPACK support for the GPU if needed
    conda install -c pytorch magma-cuda110  # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo

    On MacOS

    # Add these packages if torch.distributed is needed
    conda install pkg-config libuv

    On Windows

    # Add these packages if torch.distributed is needed.
    # Distributed package support on Windows is a prototype feature and is subject to changes.
    conda install -c conda-forge libuv=1.39

    Get the PyTorch Source

    git clone --recursive https://github.com/pytorch/pytorch
    cd pytorch
    # if you are updating an existing checkout
    git submodule sync
    git submodule update --init --recursive --jobs 0

    Install PyTorch

    On Linux

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

    Note that if you are compiling for ROCm, you must run this command first:

    python tools/amd_build/build_amd.py

    Note that if you are using Anaconda, 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 1

    This is caused by ld from Conda environment shadowing the system ld. You should use a newer version of Python that fixes this issue. The recommended Python version is 3.6.10+, 3.7.6+ and 3.8.1+.

    On macOS

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

    Each CUDA version only supports one particular XCode version. The following combinations have been reported to work with PyTorch.

    CUDA version XCode version
    10.0 XCode 9.4
    10.1 XCode 10.1

    On Windows

    Choose Correct Visual Studio Version.

    Sometimes there are regressions in new versions of Visual Studio, so it's best to use the same Visual Studio Version 16.8.5 as Pytorch CI's. You can use Visual Studio Enterprise, Professional or Community though PyTorch CI uses Visual Studio BuildTools.

    If you want to build legacy python code, please refer to Building on legacy code and CUDA

    Build with CPU

    It's fairly easy to build with CPU.

    Note 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 tweaking CMAKE_INCLUDE_PATH and LIB. The instruction here is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.

    Build with CUDA

    NVTX is needed to build Pytorch with CUDA. NVTX is a part of CUDA distributive, where it is called "Nsight Compute". To install it onto 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. If ninja.exe is detected in PATH, 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 as Magma, oneDNN, a.k.a MKLDNN or DNNL, and Sccache are often needed. Please refer to the installation-helper to install them.

    You can refer to the build_pytorch.bat script for some other environment variables configurations

    cmd
    
    :: [Optional] If you want to build with the VS 2017 generator for old CUDA and PyTorch, please change the value in the next line to `Visual Studio 15 2017`.
    :: Note: This value is useless if Ninja is detected. However, you can force that by using `set USE_NINJA=OFF`.
    set CMAKE_GENERATOR=Visual Studio 16 2019
    
    :: 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^,16^) -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 install
    
    Adjust 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 Linux

    export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
    python setup.py build --cmake-only
    ccmake build  # or cmake-gui build

    On macOS

    export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
    MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only
    ccmake build  # or cmake-gui build

    Docker Image

    Using pre-built images

    You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+

    docker run --gpus all --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 image yourself

    NOTE: Must be built with a docker version > 18.06

    The Dockerfile is supplied to build images with CUDA 11.1 support and cuDNN v8. You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it unset to use the default.

    make -f docker.Makefile
    # images are tagged as docker.io/${your_docker_username}/pytorch

    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.

    If you get a katex error run npm install katex. If it persists, try npm install -g katex

    Previous Versions

    Installation instructions and binaries for previous PyTorch versions may be found on Our Website.

    Getting Started

    Three-pointers to get you started:

    Resources

    Communication

    Releases and Contributing

    PyTorch has a 90-day release cycle (major releases). 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.

    To learn more about making a contribution to Pytorch, please see our Contribution page.

    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 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 to hughperkins/pytorch with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.

    License

    PyTorch has a BSD-style license, as found in the LICENSE file.

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/pytorch/pytorch

    发行版本 32

    Small bug fix release

    全部发行版

    贡献者 310

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    开发语言

    • C++ 52.1 %
    • Python 36.0 %
    • Cuda 5.2 %
    • C 3.2 %
    • CMake 1.2 %