Featured projects
Thank you to everyone who participated in the PyTorch Docathon 2026! Once again, the community showed up with incredible energy and dedication to make PyTorch documentation better for developers everywhere.
The PyTorch Docathon ran from May 5th 2026 through May 19th 2026, bringing together more than 260+ registrants and 30+ active participants. Participants tackled issues across difficulty levels, resulting in over 150 merged pull requests that fixed various issues, added API documentation and contributed to the ExecuTorch documentation.
We want to give a special shout-out to our top contributors, whose dedication and expertise went above and beyond. Your work directly improves the experience for millions of PyTorch users worldwide. See the full list of contributors in the leaderboard.
Meet the top contributors: #
**First place:** [ymrohit](https://github.com/ymrohit)
**Second place: **[XAheli](https://github.com/XAheli), [PyDevC](https://github.com/PyDevC), [darknight054](https://github.com/darknight054)
**Third place:** [JonathanColetti](https://github.com/JonathanColetti), [Kadermiyanyedi](https://github.com/Kadermiyanyedi)
Honorable mentions: AswaniSahoo, Vasanthadithya-mundrathi, Nazim-fad, ozgecinko, kiszk, saurabhkthakur, spzala
As we wrap up this Docathon, we want to remind everyone that great documentation is an ongoing effort. Whether this was your first open source contribution or your hundredth, your work matters. Clear docs lower the barrier to entry and help the entire deep learning community move faster, and shortens the path from research to production in machine learning. And as AI development accelerates, documentation matters even more. LLMs and AI agents increasingly rely on public technical documentation to learn APIs, generate code, and troubleshoot workflows. High-quality PyTorch docs don’t just help humans, they help ensure AI-generated guidance is more accurate, up-to-date, and aligned with best practice.
We encourage you to keep contributing to PyTorch documentation and code.Thank you for being part of this, and we look forward to seeing you at the next one.
Team PyTorch