
layout: blog_detail
title: 'PyTorch 1.9 Release, including torch.linalg and Mobile Interpreter'
author: Team PyTorch 

We are excited to announce the release of PyTorch 1.9. The release is composed of more than 3,400 commits since 1.8, made by 398 contributors. The release notes are available here. Highlights include:
1. Major improvements to support scientific computing, including torch.linalg, torch.special, and Complex Autograd
2. Major improvements in on-device binary size with Mobile Interpreter
3. Native support for elastic-fault tolerance training through the upstreaming of TorchElastic into PyTorch Core
4. Major updates to the PyTorch RPC framework to support large scale distributed training with GPU support
5. New APIs to optimize performance and packaging for model inference deployment 
6. Support for Distributed training, GPU utilization and SM efficiency in the PyTorch Profiler