Blog

For the upcoming PyTorch 2.0 feature release (target March 2023), we will target CUDA 11.7…

We are excited to announce the public release of Holistic Trace Analysis (HTA), an open…

If you installed PyTorch-nightly on Linux via pip between December 25, 2022 and December 30,…

In this post we discuss performance tuning of Torchserve for serving your models in production.…

TL;DR: We demonstrate the use of PyTorch with FairScale’s FullyShardedDataParallel (FSDP) API in writing large…

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…