pytorch/pytorch 源码下载_部署教程

项目概述

Tensors and Dynamic neural networks in Python with strong GPU acceleration

项目地址

https://github.com/pytorch/pytorch

项目页面预览

pytorch/pytorch preview

关键指标

Stars：96635
主要语言：Python
License：Other
最近更新：2026-01-15T16:02:25Z
默认分支：main

本站高速下载（国内可用）

点击下载（本站镜像）
– SHA256：2973613a2592004d5d0ef04e7d2e48a7ec4af793aaf8ef3c15b0a4b87eabee43

安装部署要点（README 精选）

Installation

Install Dependencies

Common

# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section above
pip install --group dev

On Linux

pip install mkl-static mkl-include
# CUDA only: Add LAPACK support for the GPU if needed
# magma installation: run with active conda environment. specify CUDA version to install
.ci/docker/common/install_magma_conda.sh 12.4

# (optional) If using torch.compile with inductor/triton, install the matching version of triton
# Run from the pytorch directory after cloning
# For Intel GPU support, please explicitly `export USE_XPU=1` before running command.
make triton

On MacOS

# Add this package on intel x86 processor machines only
pip install mkl-static mkl-include
# Add these packages if torch.distributed is needed
conda install pkg-config libuv

On Windows

pip install mkl-static mkl-include
# 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=1.51

Install PyTorch

On Linux

If you’re compiling for AMD ROCm then first run this command:

# Only run this if you're compiling for ROCm
python tools/amd_build/build_amd.py

Install PyTorch

# the CMake prefix for conda environment
export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
python -m pip install --no-build-isolation -v -e .

# the CMake prefix for non-conda environment, e.g. Python venv
# call following after activating the venv
export CMAKE_PREFIX_PATH="${VIRTUAL_ENV}:${CMAKE_PREFIX_PATH}"

On macOS

python -m pip install --no-build-isolation -v -e .

On Windows

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

CPU-only builds

In this mode PyTorch computations will run on your CPU, not your GPU.

python -m pip install --no-build-isolation -v -e .

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.

CUDA based build

In this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching

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 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. 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

:: 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 -m pip install --no-build-isolation -v -e .

Intel GPU builds

In this mode PyTorch with Intel GPU support will be built.

Please make sure the common prerequisites as well as the prerequisites for Intel GPU are properly installed and the environment variables are configured prior to starting the build. For build tool support, Visual Studio 2022 is required.

Then PyTorch can be built with the command:

:: CMD Commands:
:: Set the CMAKE_PREFIX_PATH to help find corresponding packages
:: %CONDA_PREFIX% only works after `conda activate custom_env`

if defined CMAKE_PREFIX_PATH (
    set "CMAKE_PREFIX_PATH=%CONDA_PREFIX%\Library;%CMAKE_PREFIX_PATH%"
) else (
    set "CMAKE_PREFIX_PATH=%CONDA_PREFIX%\Library"
)

python -m pip install --no-build-isolation -v -e .

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 $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
CMAKE_ONLY=1 python setup.py build
ccmake build  # or cmake-gui build

On macOS

export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
MACOSX_DEPLOYMENT_TARGET=11.0 CMAKE_ONLY=1 python setup.py build
ccmake build  # or cmake-gui build

Docker Image

常用命令（从 README 提取）

$ source <CONDA_INSTALL_DIR>/bin/activate
$ conda create -y -n <CONDA_NAME>
$ conda activate <CONDA_NAME>

$ source <CONDA_INSTALL_DIR>\Scripts\activate.bat
$ conda create -y -n <CONDA_NAME>
$ conda activate <CONDA_NAME>
$ call "C:\Program Files\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvarsall.bat" x64

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