March 22, 2023
PyTorch 2.0 & XLA—The Latest Cutting Edge Features
Today, we are excited to share our latest work for PyTorch/XLA 2.0. The release of PyTorch 2.0 is yet another major milestone for this storied community and we are excited to continue to be part of it. When the PyTorch/XLA project started in 2018 between Google and Meta, the focus was on bringing cutting edge Cloud TPUs to hel...
March 16, 2023
Accelerated Diffusers with PyTorch 2.0
PyTorch 2.0 has just been released. Its flagship new feature is torch.compile(), a one-line code change that promises to automatically improve performance across codebases. We have previously checked on that promise in Hugging Face Transformers and TIMM models, and delved deep into its motivation, arc...
March 15, 2023
PyTorch 2.0: Our next generation release that is faster, more Pythonic and Dynamic as ever
We are excited to announce the release of PyTorch® 2.0 which we highlighted during the PyTorch Conference on 12/2/22! PyTorch 2.0 offers the same eager-mode development and user experience, while fundamentally changing and supercharging how PyTorch operates at compiler level under the hood with faster performance and support for Dynam...
February 02, 2023
Deprecation of CUDA 11.6 and Python 3.7 Support
For the upcoming PyTorch 2.0 feature release (target March 2023), we will target CUDA 11.7 as the stable version and CUDA 11.8 as the experimental version of CUDA and Python >=3.8, <=3.11.
January 09, 2023
PyTorch Trace Analysis for the Masses
We are excited to announce the public release of Holistic Trace Analysis (HTA), an open source performance analysis and visualization Python library for PyTorch users. HTA takes as input Kineto traces collected by the PyTorch profiler, which are complex and challenging to interpret, and up-levels the performance information contained in the...
