Learn the Basics — PyTorch Tutorials 2. 12. 0+cu130 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
PyTorch 2. x Learn about PyTorch 2 x: faster performance, dynamic shapes, distributed training, and torch compile
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 Its Pythonic design and deep integration with native Python tools make it an accessible and powerful
YOLOv5 – PyTorch Load From PyTorch Hub This example loads a pretrained YOLOv5s model and passes an image for inference YOLOv5 accepts URL, Filename, PIL, OpenCV, Numpy and PyTorch inputs, and returns detections in torch, pandas, and JSON output formats See the YOLOv5 PyTorch Hub Tutorial for details
PyTorch Foundation: The Next Chapter, Together The PyTorch Foundation’s progress is not the product of any single organization or individual It is the result of thousands of community members: maintainers, contributors, reviewers, working group participants, event organizers, speakers, educators, and member company teams who consistently choose collaboration over fragmentation and long
PyTorch at NVIDIA GTC 2026: Join Us in San Jose! – PyTorch We’re excited to announce that PyTorch will have a strong presence at NVIDIA GTC 2026, from March 16-19, 2026 in San Jose! Whether you’re a seasoned PyTorch developer or just getting started, we invite you to join us for demos, talks, hands-on labs, and opportunities to connect with PyTorch core maintainers and community experts