Video by PyTorch via YouTube
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 Joe Spisak, moderated by Chris Gottbrath. The panel will provide a brief overview of the release and answer your questions live. Register today!
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.
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.
