July 10, 2024
Learn how to develop Android applications with ExecuTorch and Llama models
This blog is courtesy of the PyTorch team at Arm. More details can be found here.
July 03, 2024
Announcing Hacker Cup AI Track at NeurIPS 2024
The PyTorch team in partnership with Meta Hacker Cup, and Microsoft Research, are excited to announce the Hacker Cup AI Track at NeurIPS 2024. This will be the first AI track for the popular Meta Hacker Cup programming competition designed to assess the capabilities of Generative AI in performing autonomous code generation tasks. We aim to test the limits of AI in complex coding challenges and measure the performance gap between AI systems and human programmers. We will provide access to all ...
June 25, 2024
Powering the AI Revolution: The PyTorch Documentary
Now live: The official PyTorch Documentary! This film unveils the authentic narrative of PyTorch’s inception, attributing its existence to a dedicated group of unsung heroes driving technological innovation.
June 23, 2024
Training MoEs at Scale with PyTorch
Over the past year, Mixture of Experts (MoE) models have surged in popularity, fueled by powerful open-source models like DBRX, Mixtral, DeepSeek, and many more. At Databricks, we’ve worked closely with the PyTorch team to scale training of MoE models. In this blog post, we’ll talk about how we scale to over three thousand GPUs using PyTorch Distributed and MegaBlocks, an efficient open-source MoE implementation in PyTorch.
June 20, 2024
🎉 PyTorch Docathon H1 2024 Wrap-up 🎉
We are thrilled to announce the successful completion of the H1 2024 PyTorch Docathon! The event was a resounding success, and we want to extend our heartfelt gratitude to all the participants who made it possible. Dedication, expertise, and tireless efforts of our open-source contributors have once again helped us to improve PyTorch documentation.
June 20, 2024
Accelerating Neural Network Training with Semi-Structured (2:4) Sparsity
Over the past year, we’ve added support for semi-structured (2:4) sparsity into PyTorch. With just a few lines of code, we were able to show a 10% end-to-end inference speedup on segment-anything by replacing dense matrix multiplications with sparse matrix multiplications.