June 18, 2021
An Overview of the PyTorch Mobile Demo Apps
PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency is reduced, privacy preserved, and models can run on mobile devices anytime, anywhere.
June 16, 2021
Everything You Need To Know About Torchvision’s SSD Implementation
In TorchVision v0.10, we’ve released two new Object Detection models based on the SSD architecture. Our plan is to cover the key implementation details of the algorithms along with information on how they were trained in a two-part article.
June 15, 2021
PyTorch 1.9 Release, including torch.linalg and Mobile Interpreter
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: Major improvements to support scientific computing, including torch.linalg, torch.special, and Complex Autograd Major improvements in on-device binary size with Mobile Interpreter Native support for elastic-fault tolerance training through the upstreaming of TorchElastic into PyTorch Core...
June 15, 2021
New PyTorch Library Releases in PyTorch 1.9, including TorchVision, TorchAudio, and more
Today, we are announcing updates to a number of PyTorch libraries, alongside the PyTorch 1.9 release. The updates include new releases for the domain libraries including TorchVision, TorchText and TorchAudio. These releases, along with the PyTorch 1.9 release, include a number of new features and improvements that will provide a broad set of updates for the PyTorch community.
June 08, 2021
Overview of PyTorch Autograd Engine
This blog post is based on PyTorch version 1.8, although it should apply for older versions too, since most of the mechanics have remained constant.
May 26, 2021
Everything you need to know about TorchVision’s MobileNetV3 implementation
In TorchVision v0.9, we released a series of new mobile-friendly models that can be used for Classification, Object Detection and Semantic Segmentation. In this article, we will dig deep into the code of the models, share notable implementation details, explain how we configured and trained them, and highlight important tradeoffs we made during their tuning. Our goal is to disclose technical details that typically remain undocumented in the original papers and repos of the models.
May 25, 2021
Announcing the PyTorch Enterprise Support Program
Today, we are excited to announce the PyTorch Enterprise Support Program, a participatory program that enables service providers to develop and offer tailored enterprise-grade support to their customers. This new offering, built in collaboration between Facebook and Microsoft, was created in direct response to feedback from PyTorch enterprise users who are developing models in production at scale for mission-critical applications.
