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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.

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
  • From Source
  • Docker Image
  • Building the Documentation
  • Previous Versions
• Getting Started
• Communication

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

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

• Stable binaries:
  • Python 2.7: https://nvidia.box.com/v/torch-stable-cp27-jetson-jp42
  • Python 3.6: https://nvidia.box.com/v/torch-stable-cp36-jetson-jp42
• Rolling weekly binaries:
  • Python 2.7: https://nvidia.box.com/v/torch-weekly-cp27-jetson-jp42
  • Python 3.6: https://nvidia.box.com/v/torch-weekly-cp36-jetson-jp42

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

If you are installing from source, you will need 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

• NVIDIA CUDA 9 or above
• NVIDIA cuDNN v7 or above

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 are available here

Common (only install typing for Python <3.5)

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

On Linux

# Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda90 # or [magma-cuda92 | magma-cuda100 | magma-cuda101 ] depending on your cuda version

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

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

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

On Windows

At least Visual Studio 2017 Update 3 (version 15.3.3 with the toolset 14.11) and NVTX 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".

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. Be sure that CUDA with Nsight Compute is installed after Visual Studio 2017.

Currently VS 2017, VS 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.
If Ninja is selected as the generator, the latest MSVC which is newer than VS 2015 (14.0) will get selected as the underlying toolchain if you have Python > 3.5, otherwise VS 2015 will be selected so you'll have to activate the environment. If you use CMake <= 3.14.2 and has VS 2019 installed, then even if you specify VS 2017 as the generator, VS 2019 will get selected as the generator.

CUDA and MSVC have strong version dependencies, so even if you use VS 2017 / 2019, you will get build errors like nvcc fatal : Host compiler targets unsupported OS. For this kind of problem, please install the corresponding VS toolchain in the table below and then you can either specify the toolset during activation (recommended) or set CUDAHOSTCXX to override the cuda host compiler (not recommended if there are big version differences).

cmd
:: [Optional] Only add the next two lines if you need Python 2.7. If you use Python 3, ignore these two lines.
set MSSdk=1
set FORCE_PY27_BUILD=1

:: [Optional] If you want to build with VS 2019 generator, please change the value in the next line to `Visual Studio 16 2019`.
:: 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 15 2017

:: 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 2017 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.
:: It's an essential step if you use Python 3.5.
set CMAKE_GENERATOR_TOOLSET_VERSION=14.11
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\2017\Enterprise\VC\Tools\MSVC\14.11.25503\bin\HostX64\x64\cl.exe

python setup.py install

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

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.

NOTE: Must be built with a docker version > 18.06

The Dockerfile is supplied to build images with cuda support and cudnn v7. 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

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.

Installation instructions and binaries for previous PyTorch versions may be found on our 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: The PyTorch 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 is PyTorch 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

More dataSet to see: publicDataSet.rst

More totorials to see: https://github.com/Lornatang/PyTorch-Tutorials