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PyTorch PyTorch Foundation is the deep learning community home for the open source PyTorch framework and ecosystem
Get Started - PyTorch For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core
End-to-end Machine Learning Framework – PyTorch PyTorch supports an end-to-end workflow from Python to deployment on iOS and Android It extends the PyTorch API to cover common preprocessing and integration tasks needed for incorporating ML in mobile applications
PyTorch – PyTorch PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment Built to offer maximum flexibility and speed, PyTorch supports dynamic computation graphs, enabling researchers and developers to iterate quickly and intuitively
PyTorch 2. x Introducing PyTorch 2 0, our first steps toward the next generation 2-series release of PyTorch Over the last few years we have innovated and iterated from PyTorch 1 0 to the most recent 1 13 and moved to the newly formed PyTorch Foundation, part of the Linux Foundation
PyTorch documentation — PyTorch 2. 9 documentation PyTorch documentation # PyTorch is an optimized tensor library for deep learning using GPUs and CPUs Features described in this documentation are classified by release status: Stable (API-Stable): These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation
Quickstart — PyTorch Tutorials 2. 9. 0+cu128 documentation PyTorch offers domain-specific libraries such as TorchText, TorchVision, and TorchAudio, all of which include datasets For this tutorial, we will be using a TorchVision dataset
Learn the Basics — PyTorch Tutorials 2. 9. 0+cu128 documentation Most machine learning workflows involve working with data, creating models, optimizing model parameters, and saving the trained models This tutorial introduces you to a complete ML workflow implemented in PyTorch, with links to learn more about each of these concepts