PyTorch Foundation - PyTorch

Get Started: Install PyTorch Locally or Launch Instantly on Supported Cloud Platforms
As a member of the PyTorch Foundation, you’ll have access to resources that allow you to be stewards of stable, secure, and long-lasting codebases. You can collaborate on training, local and regional events, open-source developer tooling, academic research, and guides to help new users and contributors have a productive experience.
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.
Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.
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. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.
NOTE: Latest Stable PyTorch requires Python 3.10 or later.
Could not find the right platform for your hardware? See the PyTorch Additional Platforms page.
Get up and running with PyTorch quickly through popular cloud platforms and machine learning services.
Explore a rich ecosystem of libraries, tools, and more to support development.
Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.
PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.
skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.
Reduce inference costs by 71% and scale out using PyTorch, TorchServe, and AWS Inferentia.
Pushing the state of the art in NLP and Multi-task learning.
Using PyTorch’s flexibility to efficiently research new algorithmic approaches.
Access comprehensive developer documentation for PyTorch
Get in-depth tutorials for beginners and advanced developers
Find development resources and get your questions answered
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