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UID:10000202-1783584000-1786035600@pytorch.org
SUMMARY:AMD AI DevMaster Hackathon
DESCRIPTION:The virtual AMD AI DevMaster Hackathon invites developers\, researchers\, students\, AI practitioners\, and open source contributors to build applications across three tracks: Multimodal AI for content creation tools\, Agentic AI for private AI agents and local deployment\, and Physical AI for robotics simulation and application design. \nAs the officially recommended framework for the AMD AI DevMaster Hackathon\, PyTorch enables developers to build AI applications on ROCm-enabled AMD GPUs. Participants can compete individually or in teams of up to three for a share of the $30\,000 prize pool\, and eligible participants may receive access to AMD Radeon GPU resources. \nRegistration opened July 10\, and project submissions will be accepted from July 15 through August 6. \nJuly 10–August 6\, 2026VirtualHosted by George Wang and Daniel Huang \nRegister and view details
URL:https://pytorch.org/event/amd-ai-devmaster-hackathon/
CATEGORIES:Community-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2026/07/HNNfEo0XgAAvAff.png
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DTSTART;TZID=America/Los_Angeles:20260722T080000
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SUMMARY:AMD Advancing AI
DESCRIPTION:PyTorch Foundation CTO Matt White will speak at AMD Advancing AI on how right-sized models\, intelligent routing\, caching\, bounded escalation\, and task-level measurement can improve AI efficiency and inference economics. \nJuly 22\, 2026 at 11:50 AM PDT\nMoscone West\, San Francisco \nExplore the track >
URL:https://pytorch.org/event/amd-advancing-ai/
CATEGORIES:Community-hosted
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DTSTART;TZID=America/Los_Angeles:20260722T110000
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UID:10000196-1784718000-1784721600@pytorch.org
SUMMARY:PyTorch 2.13 Release Live Q&A
DESCRIPTION:PyTorch 2.13 introduces updates across attention\, compilation\, distributed training\, memory efficiency\, Python support\, and accelerator platforms. Highlights include FlexAttention support on Apple Silicon with up to approximately 12x speedup over SDPA on sparse patterns\, the CuTeDSL “Native DSL” backend for key GPU operations\, and nn.LinearCrossEntropyLoss to reduce peak GPU memory by up to 4x during large-vocabulary language model training. \nOn Wednesday\, July 22\, 2026\, at 11 a.m. PT\, PyTorch maintainers and contributors will provide a brief overview of the PyTorch 2.13 release and answer questions from the community live. \nTopics will include: \n\nFlexAttention support on Apple Silicon and deterministic backward computation on CUDA\nThe CuTeDSL “Native DSL” backend for Inductor\nnn.LinearCrossEntropyLoss for reducing peak GPU memory\ntorchcomms for large-cluster training\nFSDP2 communication overlap improvements\nTorch wheel support for Python 3.15 on Linux\, including free-threaded 3.15t builds\nExpanded ROCm\, Arm\, and Intel XPU platform support\n\nThe live Q&A will feature expert panelists Alban Desmaison\, Andrey Talman\, and Piotr Bialecki\, with Chris Gottbrath moderating. \nPyTorch 2.13 includes 3\,328 commits from 526 contributors since PyTorch 2.12. \nRegister for the live Q&A:
URL:https://pytorch.org/event/pytorch-2-13-release-live-qa/
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2026/07/2.13.png
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