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Cutting-edge AI models are becoming extremely large. The cost and overhead of training these models…

Large model training using a cloud native approach is of growing interest for many enterprises…

Introducing PyTorch 2.0, our first steps toward the next generation 2-series release of PyTorch. Over…

torch.compile() makes it easy to experiment with different compiler backends to make PyTorch code faster with…

1. Introduction PyTorch supports two execution modes : eager mode and graph mode. In eager…

tl;dr Multi-Objective Optimization in Ax enables efficient exploration of tradeoffs (e.g. between model performance and…

Introduction In recent years, scaling model sizes has become a promising area of research. In…

We are announcing TorchMultimodal Beta, a PyTorch domain library for training SoTA multi-task multimodal models…

On May 25, 2021, we announced the PyTorch Enterprise Support Program (ESP) that enabled providers to develop…

Note: A previous version of this post was published in November 2022. We have updated…

Summary We are bringing a number of improvements to the current PyTorch libraries, alongside the…

We are excited to announce the release of PyTorch® 1.13 (release note)! This includes Stable versions…

Introduction TL;DR: It can be challenging to run PyTorch on mobile devices, SBCs (Single Board…

Introduction The research community has witnessed a lot of successes with large models across NLP,…

1. Meta’s AI Performance Profiling (MAIProf) Figure 1: A simplified illustration of the Meta’s AI…

We are excited to announce that the PyTorch Conference returns in-person as a satellite event…