July 12, 2022
A BetterTransformer for Fast Transformer Inference
tl;dr Transformers achieve state-of-the-art performance for NLP, and are becoming popular for a myriad of other tasks. They are computationally expensive which has been a blocker to their widespread productionisation. Launching with PyTorch 1.12, BetterTransformer implements a backwards-compatible fast path of torch.nn.TransformerEncoder for Transformer Encoder Inference and does not require model authors to modify their models. BetterTransformer improvements can exceed 2x in speedup and thro...
June 28, 2022
PyTorch 1.12: TorchArrow, Functional API for Modules and nvFuser, are now available
We are excited to announce the release of PyTorch 1.12 (release note)! This release is composed of over 3124 commits, 433 contributors. Along with 1.12, we are releasing beta versions of AWS S3 Integration, PyTorch Vision Models on Channels Last on CPU, Empowering PyTorch on Intel® Xeon® Scalable processors with Bfloat16 and FSDP API. We want to sincerely thank our dedicated community for your contributions.
June 28, 2022
New library updates in PyTorch 1.12
We are bringing a number of improvements to the current PyTorch libraries, alongside the PyTorch 1.12 release. These updates demonstrate our focus on developing common and extensible APIs across all domains to make it easier for our community to build ecosystem projects on PyTorch.
June 27, 2022
How Computational Graphs are Executed in PyTorch
Welcome to the last entry into understanding the autograd engine of PyTorch series! If you haven’t read parts 1 & 2 check them now to understand how PyTorch creates the computational graph for the backward pass!
June 23, 2022
Geospatial deep learning with TorchGeo
TorchGeo is a PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
June 16, 2022
How Disney Improved Activity Recognition Through Multimodal Approaches with PyTorch
Introduction
May 18, 2022
Introducing Accelerated PyTorch Training on Mac
In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac. Until now, PyTorch training on Mac only leveraged the CPU, but with the upcoming PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.
