From
       Research To
Production

An open source machine learning framework that accelerates the path from research prototyping to production deployment.

Install

Key Features &
Capabilities

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Production Ready

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.

Distributed Training

Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.

Robust Ecosystem

A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

Cloud Support

PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.

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Install PyTorch

Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you have met the prerequisites below (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.

PyTorch Build
Your OS
Package
Language
Compute Platform
Run this Command:
PyTorch Build
Stable (1.13.0)
Preview (Nightly)
Your OS
Linux
Mac
Windows
Package
Conda
Pip
LibTorch
Source
Language
Python
C++ / Java
Compute Platform
CUDA 11.6
CUDA 11.7
ROCm 5.2
CPU
Run this Command:
conda install pytorch torchvision -c pytorch

NOTE: PyTorch LTS has been deprecated. For more information, see this blog.

Previous versions of PyTorch

Quick Start With
Cloud Partners

Get up and running with PyTorch quickly through popular cloud platforms and machine learning services.

Amazon Web Services
Google Cloud Platform

Microsoft Azure

Ecosystem

Feature Projects
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Explore a rich ecosystem of libraries, tools, and more to support development.

Captum

Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.

PyTorch Geometric

PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.

skorch

skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.

Community

Join the PyTorch developer community to contribute, learn, and get your questions answered.

PyTorchDiscuss

Browse and join discussions on deep learning with PyTorch.

Slack

Discuss advanced topics. Request access: https://bit.ly/ptslack

中文文档

Docs and tutorials in Chinese, translated by the community.

파이토치(PyTorch)

Tutorials in Korean, translated by the community.

日本語(PyTorch)

Tutorials in Japanese, translated by the community.

Maintainers

Learn about the PyTorch core and module maintainers.

ContributionGuide

Learn how you can contribute to PyTorch code and documentation.

DesignPhilosophy

PyTorch design principles for contributors and maintainers.

Governance

Learn about the PyTorch governance hierarchy.

Contributors

Stay up to date with the codebase and discover RFCs, PRs and more.

Companies & Universities
Using PyTorch

Reduce inference costs by 71% and drive scale out using PyTorch, TorchServe, and AWS Inferentia.

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Pushing the state of the art in NLP and Multi-task learning.

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Using PyTorch’s flexibility to efficiently research new algorithmic approaches.

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Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources

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