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DTSTART;VALUE=DATE:20260207
DTEND;VALUE=DATE:20260208
DTSTAMP:20260226T020134
CREATED:20260108T205931Z
LAST-MODIFIED:20260127T201712Z
UID:10000050-1770422400-1770508799@pytorch.org
SUMMARY:PyTorch Day India 2026
DESCRIPTION:PyTorch Day India 2026 brings the PyTorch community to Bengaluru on 7 February 2026. Hosted by PyTorch Foundation and co-hosted with IBM\, NVIDIA\, and Red Hat\, the event focuses on open source AI and machine learning through a full day of technical talks\, and discussions. REGISTER TODAY \n\n\nImmerse yourself in a vibrant day of insightful technical talks\, interactive discussions\, and engaging poster sessions designed to foster knowledge exchange and collaboration. PyTorch Day India is your gateway to connecting with leading experts and peers in the open source AI community\, offering you unique opportunities to explore cutting-edge advancements and shape the future of deep learning.
URL:https://pytorch.org/event/pytorch-day-india-2026/
LOCATION:Bengaluru\, India\, Bengaluru\, India
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2026/01/Social-Snackable-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260128T110000
DTEND;TZID=America/Los_Angeles:20260128T120000
DTSTAMP:20260226T020134
CREATED:20260121T200822Z
LAST-MODIFIED:20260202T220215Z
UID:10000103-1769598000-1769601600@pytorch.org
SUMMARY:PyTorch 2.10 Release Live Q&A
DESCRIPTION:The PyTorch 2.10 release features a number of improvements for performance and numerical debugging. On Wednesday\, January 28th\, Andrey Talman\,  Nikita Shulga\, Shangdi Yu\, and Ashok Emani gave a brief update on the release and answered your questions. Watch here. Topics: \n\nRelease cadence updates link. \nTorchScript deprecation\nTorch Compile Support for Python 3.14\nContinuing support Wheel-Variant in release 2.10. PEP 817 was published. Link\nDebugMode and tlparse\nDeprecating Volta support for cuda 12.8 builds\, starting release 2.11. Link\nRemind about Linux aarch64 CUDA binaries availability since release 2.9. The instructions for these are now visible on getting started page\nTorchAudio migration finalization: TorchAudio 2.10 marks the finalization of the ongoing migration\, and should be the last major release in the foreseeable future. Based on user feedback\, some critical optimized ops like lfilter\, which were originally slated for deletion\, are preserved!\nIntel GPUs support: Expand PyTorch support to the latest Panther Lake on Windows and Linux by enabling FP8 (core ops and scaled matmul) and complex MatMul support\, and extending SYCL support in the C++ Extension API for Windows custom ops.\n\nBios: \nAndrey 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. \nNikita is a Software Engineer at Meta\, where\, among other things\, Nikita is responsible for PyTorch releases and continuous integration. Nikita is committed to uplifting the developer community and continuously improving PyTorch. \nShangdi is a Research Scientist at Meta\, where\, among other things\, she builds PyTorch Compile tooling to improve debuggability and observability. \nAshok is a Software Engineering Manager at Intel on the PyTorch team\, focused on performance\, optimizations\, and platform enablement for PyTorch on Intel hardware.
URL:https://pytorch.org/event/pytorch-2-10-release-live-qa/
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2026/01/2.10-Webinar-Card-Final.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251211T110000
DTEND;TZID=America/Los_Angeles:20251211T120000
DTSTAMP:20260226T020134
CREATED:20251020T221800Z
LAST-MODIFIED:20251211T213633Z
UID:10000044-1765450800-1765454400@pytorch.org
SUMMARY:Inside Helion: Live Q&A with the Developers
DESCRIPTION:Helion — a new Python-embedded domain-specific language (DSL) for wiring high-performance ML kernels — is built to compile down to Triton. Helion raises the level of abstraction for kernel authoring with PyTorch-centric syntax\, making it dramatically easier to write correct\, fast\, and portable kernels. Its innovative autotuning system explores thousands of candidate Triton kernels to find the most performant implementation. Thus\, automation handles complex\, hardware-dependent details to ensure portability across different hardware architectures. \nWe dove into: \n\nThe design philosophy and architecture of Helion\nHow autotuning delivers state-of-the-art performance across GPUs\nWhat’s next on the roadmap — and how you can get involved\n\nWatch here: \n \nBios: \n\nJason Ansel: Research Scientist at Meta and the creator of PyTorch Compiler and Helion DSL\nOguz Ulgen: Software Engineer at Meta and the creator of the PyTorch Compiler cache\, working on Helion\nWill Feng: Software Engineer at Meta\, working on PyTorch Compiler and Helion\nJongsok Choi: Engineering Manager at Meta\, supporting the PyTorch Compiler Backend (Inductor) team and Helion
URL:https://pytorch.org/event/inside-helion-live-qa/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251203T180000
DTEND;TZID=America/Los_Angeles:20251203T210000
DTSTAMP:20260226T020134
CREATED:20251121T224244Z
LAST-MODIFIED:20251126T011522Z
UID:10000046-1764784800-1764795600@pytorch.org
SUMMARY:Open Source AI Reception 2025
DESCRIPTION:Hosted by PyTorch Foundation and CNCF\, the Open Source AI Reception brings together the community during NeurIPS 2025 for an evening focused on open source collaboration. The reception is presented with Anyscale\, Featherless\, Hugging Face\, and Unsloth and offers a space for attendees to connect with others working across the open source AI ecosystem. \nThis gathering welcomes anyone at NeurIPS interested in open source AI and the projects\, people\, and ideas shaping its future. \nRegister: https://linuxfoundation.regfox.com/open-source-ai-reception-2025 \n6:00 PM–9:00 PM PT\, Union Kitchen and Tap Gaslamp\, San Diego\, California\, USA
URL:https://pytorch.org/event/open-source-ai-reception-2025/
LOCATION:San Diego\, CA
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2025/11/Open-Source-AI-Reception-at-NuerIPS-Dec-3-2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251022
DTEND;VALUE=DATE:20251024
DTSTAMP:20260226T020134
CREATED:20241205T094643Z
LAST-MODIFIED:20250904T194243Z
UID:10000001-1761091200-1761263999@pytorch.org
SUMMARY:PyTorch Conference 2025
DESCRIPTION:Join us for PyTorch Conference 2025\, October 22 – 23\, 2025 in San Francisco – the world’s premier event dedicated to the framework powering today’s most groundbreaking AI innovations. Connect with AI pioneers\, researchers\, developers\, and startup founders through deep-dive technical sessions\, panels\, workshops on AI from bare metal all the way up to the application and agent layers. Our program features keynotes from visionary AI leaders\, interactive sessions on scaling and benchmarking models\, and special tracks focusing on AI safety and ethical development. \nLearn more and register at: https://events.linuxfoundation.org/pytorch-conference/
URL:https://pytorch.org/event/pytorch-conference-2025/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250814T100000
DTEND;TZID=America/Los_Angeles:20250814T110000
DTSTAMP:20260226T020134
CREATED:20250718T163422Z
LAST-MODIFIED:20250821T013230Z
UID:10000042-1755165600-1755169200@pytorch.org
SUMMARY:PyTorch 2.8 Live Release Q&A
DESCRIPTION:Our PyTorch 2.8 Live Q&A webinar will focus on PyTorch packaging\, exploring the release of wheel variant support as a new experimental feature in the 2.8 release. This feature is designed to improve the PyTorch install experience for users once it becomes generally available. \nCharlie is the founder of Astral\, whose tools like Ruff—a Python linter\, formatter\, and code transformation tool—and uv\, a next-generation package and project manager\, have seen rapid adoption across open source and enterprise\, with over 100 million downloads per month. \nJonathan has contributed to deep learning libraries\, compilers\, and frameworks since 2019. At NVIDIA\, Jonathan helped design release mechanisms and solve packaging challenges for GPU-accelerated Python libraries. A founding force behind WheelNext\, Jonathan actively works on proofs of concept\, demos\, and PEPs. \nRalf is CEO\, Technology at Quansight and a long-time maintainer of NumPy and SciPy. With over 15 years in the scientific Python ecosystem\, Ralf also maintains meson-python\, created the Array API standard and pypackaging-native\, and focuses on building sustainable open source communities. \nEli Uriegas is a Staff Software Engineer at Meta and a key contributor to the PyTorch project. Eli focuses on improving the developer experience through infrastructure enhancements and the application of AI to developer tools\, and is a maintainer of PyTorch’s build and CI systems \nWatch on demand on YouTube.
URL:https://pytorch.org/event/pytorch-live-2-8-release-qa/
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2025/07/2.8-1-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250806T110000
DTEND;TZID=America/Los_Angeles:20250806T120000
DTSTAMP:20260226T020134
CREATED:20250707T201254Z
LAST-MODIFIED:20250811T204034Z
UID:10000040-1754478000-1754481600@pytorch.org
SUMMARY:verl: Flexible and Scalable Reinforcement Learning Library for LLM Reasoning and Tool-Calling
DESCRIPTION:Speaker: Haibin Lin \n\nverl is a flexible and efficient framework for building end-to-end reinforcement learning pipelines for LLMs. It provides a user-friendly hybrid-controller programming model\, supporting various algorithms such as PPO/GRPO/DAPO with effortless scaling. Recent trends in reasoning models bring new challenges to RL infrastructure\, such as efficient tool calling\, multi-turn interactions\, and capability to scale up to giant MoE models like DeepSeek. To lower the barrier to RL for advanced reasoning and tool calling\, we improve verl with (1) efficient request level async multi-turn rollout and tool calling\, (2) integration with expert parallelism for large scale MoE models\, (3) async system architecture for off-policy / async RL algorithms and flexible device placement.\n\n\n\n\nHaibin Lin works on LLM infrastructure at Bytedance Seed\, focusing on optimizing training performance for LLMs & multimodal understanding and generation models on large scale clusters\, from pre-training to post-training. Before he joined Bytedance\, he was working on Apache MXNet (training\, inference\, runtime\, and recipes like gluon-nlp).\n\n\n\nLinkedIn\nGitHub
URL:https://pytorch.org/event/verl-flexible-and-scalable-reinforcement-learning-library-for-llm-reasoning-and-tool-calling/
CATEGORIES:PyTorch-hosted
ATTACH;FMTTYPE=image/png:https://pytorch.org/wp-content/uploads/2025/07/Haibin-Lin.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250612T120000
DTEND;TZID=America/Los_Angeles:20250612T130000
DTSTAMP:20260226T020134
CREATED:20250530T200058Z
LAST-MODIFIED:20250715T224455Z
UID:10000038-1749729600-1749733200@pytorch.org
SUMMARY:Accelerating DLRMv2 Inference on Arm Neoverse CPUs with PyTorch
DESCRIPTION:Speaker: Annop Wongwathanarat \nAnnop Wongwathanarat is a Principal Software Engineer focused on performance optimization and ML acceleration on Arm Neoverse CPUs. He specializes in low-level optimization\, advanced vectorization\, and hardware-aware techniques to enable efficient ML workloads. Annop leads efforts to enhance PyTorch performance on server-class Arm CPUs\, bridging ML frameworks with next-gen Arm infrastructure. \nThe webinar\, “Accelerating DLRMv2 Inference on Arm Neoverse CPUs with PyTorch\,” took place on Thursday\, June 12 at 12 pm PST.
URL:https://pytorch.org/event/accelerating-dlrmv2-inference-on-arm-neoverse-cpus-with-pytorch/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250330T130000
DTEND;TZID=UTC:20250330T180000
DTSTAMP:20260226T020134
CREATED:20250317T154632Z
LAST-MODIFIED:20250328T113137Z
UID:10000029-1743339600-1743357600@pytorch.org
SUMMARY:PyTorch KR Conference
DESCRIPTION:Location: Seoul\, Republic of Korea \nHear from speakers from the PyTorch Foundation\, Meta\, FuriosaAI\, Lablup\, Nota AI\, Rebellions\, etc. \nEvent Info
URL:https://pytorch.org/event/pytorch-kr-conference/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250327T120000
DTEND;TZID=UTC:20250327T140000
DTSTAMP:20260226T020134
CREATED:20250317T154350Z
LAST-MODIFIED:20250503T161411Z
UID:10000028-1743076800-1743084000@pytorch.org
SUMMARY:Using PyTorch and DINOv2 for Multi-label Plant Species Classification
DESCRIPTION:Speaker: Murilo Gustineli \nJoin us for an engaging webinar on our innovative transfer learning approach using self-supervised Vision Transformers (DINOv2) for multi-label plant species classification in the PlantCLEF 2024 challenge. We’ll cover how we efficiently extract feature embeddings from a dataset of 1.4 million images and utilize PyTorch Lightning for model training and Apache Spark for data management. Learn about our image processing techniques\, including transforming images into grids of tiles and aggregating predictions to overcome computational challenges. Discover the significant performance improvements achieved and get insights into multi-label image classification. Perfect for PyTorch developers\, this session will include a Q&A and access to our complete codebase at github.com/dsgt-kaggle-clef/plantclef-2024. \nMurilo Gustineli is a Senior AI Software Solutions Engineer at Intel\, and is currently pursuing a Master’s in Computer Science at Georgia Tech focusing on machine learning. His work involves creating synthetic datasets\, fine-tuning large language models\, and training multi-modal models using Intel® Gaudi® Al accelerators as part of the Development Enablement team. He is particularly interested in deep learning\, information retrieval\, and biodiversity research\, aiming to improve species identification and support conservation efforts. Visit Murilo on GitHub. \n Watch the recording:
URL:https://pytorch.org/event/using-pytorch-and-dinov2-for-multi-label-plant-species-classification-2/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250228
DTEND;VALUE=DATE:20250301
DTSTAMP:20260226T020134
CREATED:20250317T154824Z
LAST-MODIFIED:20250328T113138Z
UID:10000030-1740700800-1740787199@pytorch.org
SUMMARY:PyTorch Meetup at DevConf.IN 2025
DESCRIPTION:Location: Pune\, India \nEvent Blog
URL:https://pytorch.org/event/pytorch-meetup-at-devconf-in-2025/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250219T120000
DTEND;TZID=UTC:20250219T130000
DTSTAMP:20260226T020134
CREATED:20250209T101047Z
LAST-MODIFIED:20250328T113138Z
UID:10000025-1739966400-1739970000@pytorch.org
SUMMARY:Multi-Modal Tabular Deep Learning with PyTorch Frame
DESCRIPTION:  \nDate: February 19\, 12 pm PST\nSpeaker: Akihiro Nitta\, Software Engineer\, Kumo.ai\nLink to session video\nDownload slides \nIn this talk\, Akihiro introduced PyTorch Frame\, a modular framework for multi-modal tabular deep learning. PyTorch Frame enables seamless integration with the PyTorch ecosystem\, including PyTorch Geometric for graph-based message passing across relational data and Hugging Face Transformers for extracting rich text features. The talk also highlights its specialized data structures for efficiently handling sparse features\, making PyTorch Frame an essential tool for modern tabular data. \nAkihiro Nitta is a software engineer on the ML team at Kumo.ai and a core contributor to PyTorch Frame and PyTorch Geometric\, with prior experience as a maintainer of PyTorch Lightning.
URL:https://pytorch.org/event/multi-modal-tabular-deep-learning-with-pytorch-frame/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250207T080000
DTEND;TZID=UTC:20250207T100000
DTSTAMP:20260226T020134
CREATED:20250209T100836Z
LAST-MODIFIED:20250328T113138Z
UID:10000024-1738915200-1738922400@pytorch.org
SUMMARY:PyTorch 2.6 Live Q&A
DESCRIPTION:Date: February 7\, 10 am PST\nSpeaker: Nikita Shulga\nLocation: Online \nWondering what’s new in the recent PyTorch 2.6 release? Do you have questions? Join us for a live Q&A on PyTorch 2.6 with PyTorch Core Maintainer\, Nikita Shulga (Meta). \nNikita is a Software Engineer at Meta where he is\, among other things\, responsible for PyTorch releases and continuous integration. Nikita is committed to uplifting the developer community and continuously improving PyTorch. He earned his Master’s degree in Applied Mathematics from the Moscow Institute of Physics and Technology (MIPT). \nBring your PyTorch 2.6 questions for Nikita during this live Q&A session. \nWatch the recording: \n \n 
URL:https://pytorch.org/event/pytorch-2-6-live-qa/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250124T130000
DTEND;TZID=UTC:20250124T140000
DTSTAMP:20260226T020134
CREATED:20250209T101310Z
LAST-MODIFIED:20250328T113138Z
UID:10000026-1737723600-1737727200@pytorch.org
SUMMARY:AI-Powered Competitive Programming: My HackerCup 2024 Experience
DESCRIPTION:Speaker: Anton Pidkuiko\, Software Engineer\, Meta\nLocation: Online \nIn this talk\, Anton shared how he built an AI agent that ranked #1 in the finals of Meta HackerCup 2024 (AI division). Anton developed a workflow that could solve the hardest competitive programming problems quickly and reliably. Anton will walk through how he used state-of-the-art reasoning LLM models\, curated RAG\, and leveraged cloud infrastructure to safely test and execute solutions at scale. This approach highlights the massive potential of test-time compute scaling and provides insights into AI’s future role in programming. \nAnton Pidkuiko is a Software Engineer at Meta\, Reality Labs in London. He is currently working on applying the power of Large Language Models to Metaverse Avatar product experiences. \nWatch the recording now and access Anton’s presentation slides here.
URL:https://pytorch.org/event/ai-powered-competitive-programming-my-hackercup-2024-experience/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241130
DTEND;VALUE=DATE:20241201
DTSTAMP:20260226T020134
CREATED:20241206T045212Z
LAST-MODIFIED:20250328T113138Z
UID:10000022-1732924800-1733011199@pytorch.org
SUMMARY:PyTorch Korea User Group Meetup
DESCRIPTION:Event info
URL:https://pytorch.org/event/pytorch-korea-user-group-meetup/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240918
DTEND;VALUE=DATE:20240920
DTSTAMP:20260226T020134
CREATED:20241205T100444Z
LAST-MODIFIED:20250328T113138Z
UID:10000002-1726617600-1726790399@pytorch.org
SUMMARY:PyTorch Conference 2024
DESCRIPTION:Join us in San Francisco on September 18th-19th\, and learn about PyTorch\, the cutting-edge renowned open-source machine learning framework. This year is a two-day event that brings together top-tier researchers\, developers\, and academic communities\, fostering collaboration and advancing end-to-end machine learning.
URL:https://pytorch.org/event/pytorch-conference-2024/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240815
DTEND;VALUE=DATE:20240816
DTSTAMP:20260226T020134
CREATED:20241206T045242Z
LAST-MODIFIED:20250328T113138Z
UID:10000023-1723680000-1723766399@pytorch.org
SUMMARY:PyTorch Shanghai Meetup
DESCRIPTION:Read the notes
URL:https://pytorch.org/event/pytorch-shanghai-meetup/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240604
DTEND;VALUE=DATE:20240621
DTSTAMP:20260226T020134
CREATED:20241205T100732Z
LAST-MODIFIED:20250328T113138Z
UID:10000003-1717459200-1718927999@pytorch.org
SUMMARY:PyTorch Docathon 2024
DESCRIPTION:The Docathon\, similar to a hackathon\, is an event focused on improving PyTorch documentation with help from our community. Quality documentation is crucial for any technology\, and by enhancing it\, we make it easier for new users to start with PyTorch\, use its features effectively\, and accelerate the shift from research to production in machine learning. See our previous events here and here. \nWhy Participate\nThe Docathon is an inclusive event designed to be accessible to newcomers\, requiring only a basic understanding of Python\, PyTorch\, and Machine Learning\, with some tasks not even requiring these skills. It offers a rewarding experience as participants can see the direct impact of their contributions on the project’s usability and accessibility. The Docathon promotes a collaborative environment\, allowing participants to work with other contributors and PyTorch maintainers\, fostering the exchange of ideas and networking. It also provides a rich learning experience\, offering the opportunity to explore PyTorch modules\, update docstrings\, and test tutorials. \nEvent Details\n\nJune 4: Kick-off\nJune 4 – 16: Submissions and Feedback\nJune 17 – 18: Final Reviews\nJune 20: Winner Announcements\n\nFurther details for the Docathon will be announced at the Kick-off call on June 4.
URL:https://pytorch.org/event/pytorch-docathon-2024/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231016
DTEND;VALUE=DATE:20231018
DTSTAMP:20260226T020134
CREATED:20241205T100842Z
LAST-MODIFIED:20250328T113138Z
UID:10000004-1697414400-1697587199@pytorch.org
SUMMARY:PyTorch Conference 2023
DESCRIPTION:The conference will showcase PyTorch 2.1\, the next-generation release of the popular machine learning framework. As part of the Linux Foundation\, the PyTorch Foundation Conference continues the tradition of bringing together leading researchers\, developers\, and academic communities to advance the education and development of end-to-end machine learning. \nThe conference agenda features an engaging lineup of events\, including an opening reception\, engaging community and partner discussions\, informative panels\, poster sessions\, enlightening use cases and community stories\, as well as discussions on the latest trends in machine learning and deep learning development and deployment.
URL:https://pytorch.org/event/pytorch-conference-2023/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230531
DTEND;VALUE=DATE:20230601
DTSTAMP:20260226T020134
CREATED:20241205T100938Z
LAST-MODIFIED:20250328T113138Z
UID:10000005-1685491200-1685577599@pytorch.org
SUMMARY:PyTorch 2023 Docathon
DESCRIPTION:We are excited to announce the first-ever PyTorch Docathon! \nThe Docathon is a hackathon-style event focused on improving documentation by enlisting the community’s help. Documentation is a crucial aspect of any technology. \nBy improving the documentation\, we can make it easier for users to get started with PyTorch\, help them understand how to use its features effectively\, and ultimately accelerate research to production in the field of machine learning. \nDetails for the Docathon will be announced at the kick-off call on May 31.
URL:https://pytorch.org/event/pytorch-2023-docathon/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230510
DTEND;VALUE=DATE:20230511
DTSTAMP:20260226T020134
CREATED:20241205T101104Z
LAST-MODIFIED:20250328T113138Z
UID:10000006-1683676800-1683763199@pytorch.org
SUMMARY:Vancouver Meetup
DESCRIPTION:Agenda\n3:45 pm – Meet in lobby and check in\n4:00 – 4:30 pm – Generative AI and Stable Diffusion – Will Berman | Hugging Face\n4:30 – 5:00 pm – Joe Spisak & Milad Mohammadi | Open XLA\n5:00 – 5:10 pm – Break\n5:10 – 5:40 pm – How and why to become a contributor to PyTorch – Dmitry Vinnik | Meta\n5:40 – 6:00 pm – Social/networking
URL:https://pytorch.org/event/vancouver-meetup/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230309
DTEND;VALUE=DATE:20230310
DTSTAMP:20260226T020134
CREATED:20241205T101530Z
LAST-MODIFIED:20250328T113139Z
UID:10000007-1678320000-1678406399@pytorch.org
SUMMARY:PyTorch New York Meetup
DESCRIPTION:Watch on YouTube
URL:https://pytorch.org/event/pytorch-new-york-meetup/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230301
DTEND;VALUE=DATE:20230302
DTSTAMP:20260226T020134
CREATED:20241205T101629Z
LAST-MODIFIED:20250328T113139Z
UID:10000008-1677628800-1677715199@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: 2D + Distributed Tensor
DESCRIPTION:Speakers: Wanchao Liang and Junjie Wang\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-2d-distributed-tensor/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230223
DTEND;VALUE=DATE:20230224
DTSTAMP:20260226T020134
CREATED:20241205T101707Z
LAST-MODIFIED:20250328T113139Z
UID:10000009-1677110400-1677196799@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: TorchMultiModal
DESCRIPTION:Speakers: Kartikay Khandelwal and Ankita De\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-torchmultimodal/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230215
DTEND;VALUE=DATE:20230216
DTSTAMP:20260226T020134
CREATED:20241205T101739Z
LAST-MODIFIED:20250328T113139Z
UID:10000010-1676419200-1676505599@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: Torch RL
DESCRIPTION:Speaker: Vincent Moens\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-torch-rl/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230207
DTEND;VALUE=DATE:20230208
DTSTAMP:20260226T020134
CREATED:20241205T101805Z
LAST-MODIFIED:20250328T113139Z
UID:10000011-1675728000-1675814399@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: Dynamic Shapes and Calculating Maximum Batch Size
DESCRIPTION:Speakers: Edward Yang and Elias Ellison\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-dynamic-shapes-and-calculating-maximum-batch-size/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230202
DTEND;VALUE=DATE:20230203
DTSTAMP:20260226T020134
CREATED:20241205T101845Z
LAST-MODIFIED:20250328T113139Z
UID:10000012-1675296000-1675382399@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: Optimizing Transformers for Inference
DESCRIPTION:Speakers: Hamid Shojanazeri and Mark Saroufim\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-optimizing-transformers-for-inference/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230201
DTEND;VALUE=DATE:20230202
DTSTAMP:20260226T020134
CREATED:20241205T101912Z
LAST-MODIFIED:20250328T113139Z
UID:10000013-1675209600-1675295999@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: Rethinking Data Loading with TorchData
DESCRIPTION:Speakers: Kevin Tse and Erjia Guan\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-rethinking-data-loading-with-torchdata/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230125
DTEND;VALUE=DATE:20230126
DTSTAMP:20260226T020134
CREATED:20241205T101939Z
LAST-MODIFIED:20250328T113139Z
UID:10000014-1674604800-1674691199@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: A deep dive on TorchInductor and PT2 Backend Integration
DESCRIPTION:Speakers: Natalia Gimelshein\, Bin Bao\, Sherlock Huang and Eikan Wang\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-a-deep-dive-on-torchinductor-and-pt2-backend-integration/
CATEGORIES:PyTorch-hosted
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230124
DTEND;VALUE=DATE:20230125
DTSTAMP:20260226T020134
CREATED:20241205T102049Z
LAST-MODIFIED:20250328T113139Z
UID:10000015-1674518400-1674604799@pytorch.org
SUMMARY:PyTorch 2.0 Ask the Engineers Live Q&A Series: DDP/FDSP Support
DESCRIPTION:Speaker: Will Constable\nWatch on YouTube\nWatch on LinkedIn
URL:https://pytorch.org/event/pytorch-2-0-ask-the-engineers-live-qa-series-ddp-fdsp-support/
CATEGORIES:PyTorch-hosted
END:VEVENT
END:VCALENDAR