PyTorch

Federated Learning Without the Refactoring Overhead The most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale. The latest version of NVIDIA FLARE addresses this reality with a Federated Learning (FL) computing runtime that moves the training logic to the data, while raw data stays put. See examples of how to leverage PyTorch in a federated learning system. Read the full post: https://lnkd.in/gbm7qwg3

NVIDIA shared a full playlist of its sessions from PyTorch Conference Europe. NVIDIA engineers and developers covered a wide range of topics, including: - Symmetric Memory + NCCL Device APIs - Custom Kernels in torch.compile - Agentic AI & LLM Evaluation frameworks, and more. The clip below is from Besmira Nushi's keynote, "The Unbearable Lightness of (Agentic) Evaluations," which explores how agentic evaluation frameworks expose gaps between what models say and what they actually do. Find the full playlist here: https://lnkd.in/gXFgvAS8

PyTorch 2.12 includes major updates across compilation, distributed systems, export, graph capture, and accelerator support. Highlights include a new device-agnostic torch.accelerator.Graph API, up to 100x faster batched eigenvalue decomposition on CUDA, support for microscaling quantization formats in torch.export.save, and expanded CUDA, ROCm, XPU, MPS, and Arm platform support. Join us on Wednesday, May 20 at 10:00 AM PT for a live Q&A with panelists Andrey Talman, Alban Desmaison, and Joseph Spisak, moderated by Chris Gottbrath. The panel will provide a brief overview of the release and answer your questions live. Topics include: -Device-Agnostic Accelerator Graph Capture -ProcessGroup Support in Custom Ops -torch.export.save Support for Microscaling Quantization Formats -Fused Adagrad Optimizer Support -FlightRecorder Updates -Multi-GPU and Multi-Node Profiling Improvements -Updated Backend Selection for torch.linalg.eigh on CUDA -Expanded CUDA, ROCm, XPU, MPS, and Arm Platform Support Register today: https://lnkd.in/gK4D63M2 Panelists: Andrey Talman is a Software Engineer at Meta, primarily focused on open source releases for PyTorch and its ecosystem libraries. He works on release management, continuous integration, and process improvements, ensuring high-quality and timely delivery of PyTorch and related projects. Alban Desmaison is a Research Engineer at Meta and the Lead Core Maintainer of PyTorch. Joe Spisak is Vice President of Product and Head of Open Source at Reflection AI. He is a PyTorch core maintainer, serves on the PyTorch Foundation Governing Board, and previously worked at Meta. Moderator: Chris Gottbrath is a Group Technical Program Manager supporting PyTorch at Meta and Chair of the PyTorch Foundation Marketing Committee.

The PyTorch Docathon 2026 has officially wrapped up, and the results are incredible! Over the past two weeks (May 5–19), our open source community showed up with massive energy and dedication to make PyTorch documentation better for developers everywhere. Here is a snapshot of what we accomplished together: Ecosystem Reach: 260+ registrants and 30+ active participants collaborated globally. Impactful Contributions: 150+ merged pull requests successfully landed across all difficulty levels. Technical Depth: Community members resolved critical issues, added API documentation, and expanded core ExecuTorch documentation. Why does high-quality documentation matter more than ever in the AI era? Clear documentation lowers the barrier to entry for human developers and directly fuels the modern AI ecosystem. LLMs and AI agents increasingly rely on public technical documentation to learn APIs, generate code, and troubleshoot complex workflows. High-quality PyTorch docs help ensure AI-generated guidance is more accurate, up-to-date, and aligned with best practices. 🏆 Celebrating Our Community Leaders: A massive shout-out to the top-performing contributors who led the way and whose work improves the experience for millions of PyTorch users worldwide: First Place: ymrohit  Second Place: XAheli, PyDevC, and darknight054  Third Place: JonathanColetti and Kadermiyanyedi  Honorable Mentions: AswaniSahoo, Vasanthadithya-mundrathi, Nazim-fad, ozgecinko, kiszk, saurabhkthakur, and spzala. Thank you to everyone who brought code, reviews, and the community momentum!

Why attend PyTorch Conference North America? 🚀 On October 20-21 in San Jose, CA, you’ll gain direct access to the maintainers and researchers shaping the world's most popular AI framework. Stay ahead of the curve with Core PyTorch updates. Learn production-ready techniques for training and inference. Connect with a global community of innovators. Secure your early bird pass by July 31 and save $400! 🔗 Register here: https://bit.ly/4sh3DSw #PyTorchCon #PyTorch #PyTorchFoundation #FutureOfAI #AI #GenAI #MachineLearning #ML #DeepLearning #OpenSource #OpenSourceSoftware #OpenSourceDevelopment #OpenSourceCommunity #OSS #LinuxFoundation #events #linux

Developing a Real-Time Hailstorm Forecasting Model With the goal to provide critical weather observation and nowcasting capabilities for severe weather prediction, Colorado State University used PyTorch-based NVIDIA PhysicsNeMo and trained a specialized version of the NVIDIA Earth-2 StormScope Nowcasting model, specifically for operational severe hailstorm prediction. 🔗 Read the full case study: https://lnkd.in/gDhzEQ5E

vLLM and PyTorch worked together to fix a long-standing aarch64 install headache — as of PyTorch 2.11.0, pip install torch on GB200 / GB300 / GH200 just works. What changed: PyTorch 2.11.0 now publishes CUDA-enabled aarch64 wheels to the default PyPI index. No more custom --index-url flags. No more transitive dependencies silently swapping your GPU build for the CPU wheel. New users on Grace Hopper and Grace Blackwell systems can follow the standard install instructions and have vLLM work the first time. In our latest blog, Kaichao You (co-founder Inferact, Lead Maintainer vLLM) shares the full story: 🐛 A 2024 hackathon bug bringing up vLLM on GH200 🔧 vLLM's in-tree workarounds (use_existing_torch.py and [tool.uv] build-isolation passthrough) 🤝 From GitHub issue to PyTorch Foundation TAC discussion 🚀 The fix landing in PyTorch 2.11.0, driven by NVIDIA and PyTorch core. A great example of cross-project collaboration under the PyTorch Foundation umbrella — and a reminder that boring infrastructure wins compound. Read the full story: https://lnkd.in/gGc8mRm8 ✍ Alban Desmaison (Meta), Nikita Shulga (Meta), Andrey Talman (Meta), Piotr Bialecki (NVIDIA)

Don't miss DeepSpeed.ai virtual office hours on May 26 at 12:00 PM America/New_York to get recent key updates, including AutoSP (sequence parallel), AutoEP (expert parallel), and AutoTP (tensor parallel) & ask questions with Masahiro Tanaka, member of DeepSpeed TSC.