PyTorch

PyTorch teams at Arm and Meta teamed up to optimize #AI performance through the ExecuTorch framework, which is now available in Beta 🔥 See how you can get started today: https://hubs.la/Q02W1YkR0

Mobile developers, listen up 📲 Thanks to the direct integration of Arm KleidiAI with the ExecuTorch framework through XNNPACK, you can benefit from quicker, more responsive AI-based experiences on mobile. Dive into what's possible: https://hubs.la/Q02W0njD0

PyTorch Expert Exchange Webinar: How does batching work on modern GPUs? with Finbarr Timbers, an AI researcher, who writes at Artificial Fintelligence and has worked at a variety of large research labs, including DeepMind and Midjourney Batch inference is the most basic optimization that you can do to improve GPU utilization. It is often overlooked and misunderstood because of how common it is. Here, we walk through why, exactly, batching works, and help you develop intuition for what exactly is going on inside your GPU.

Learn more about Intel Corporation GPU support for #PyTorch 2.5 Read the blog: https://hubs.la/Q02VTL670

Introducing ExecuTorch Beta; Faster on-device LLM support with stable APIs and broader partner coverage. https://hubs.la/Q02Vzp-r0 #OnDeviceAI #Edge #PyTorch #LLMs #ODLLM

We are happy to announce the stable release, 1.0, for TorchRec and FBGEMM. TorchRec is the PyTorch native recommendation systems library, powered by FBGEMM’s (Facebook GEneral Matrix Multiplication) efficient, low level kernels. Check out the release here: https://hubs.la/Q02VB0Kt0

Don’t miss PyTorch Foundation Executive Director Matt White at TEDAI San Francisco this week as part of the panel: Industry Experts in Conversation with the AI for Good Hackathon Winners #TEDAI2024 #Hackathon #AIForGood #AI #yPorch

Wondering what's new in the recent PyTorch 2.5 release? Do you have questions? Join us for a live Q&A on PyTorch 2.5 with PyTorch Core Maintainer, Alban Desmaison. Alban has been working on PyTorch since nearly its inception, first during his PhD at the University of Oxford and now at Meta. He is focused on maintaining core components, designing a wide breadth of features and fostering the PyTorch Community. Bring your PyTorch 2.5 questions for Alban during this live Q&A session.

Live Q&A on the PyTorch 2.5 release begins soon!

PyTorch 2.5 is here 🔥 We are excited to announce the release of PyTorch® 2.5 featuring: 🔥 a new CuDNN backend for SDPA, enabling speedups by default for users of SDPA on H100s or newer GPUs 🔥 regional compilation of torch.compile, offering a way to reduce the cold start up time for torch.compile by allowing users to compile a repeated nn.Module (e.g. a transformer layer in LLM) without recompilations 🔥 TorchInductor CPP backend offering solid performance speedup with numerous enhancements like FP16 support, CPP wrapper, AOT-Inductor mode, and max-autotune mode Read more in our PyTorch 2.5 Release Blog: https://hubs.la/Q02TRx6f0