March 26, 2020
It’s important to make efficient use of both server-side and on-device compute resources when developing machine learning applications. To support more efficient deployment on servers and edge devices, PyTorch added a support for model quantization using the familiar eager mode Python API.
January 15, 2020
Today, we’re announcing the availability of PyTorch 1.4, along with updates to the PyTorch domain libraries. These releases build on top of the announcements from NeurIPS 2019, where we shared the availability of PyTorch Elastic, a new classification framework for image and video, and the addition of Preferred Networks to the PyTorch community. For those that attended the ...
December 06, 2019
PyTorch continues to be used for the latest state-of-the-art research on display at the NeurIPS conference next week, making up nearly 70% of papers that cite a framework. In addition, we’re excited to welcome Preferred Networks, the maintainers of the Chainer framework, to the PyTorch community. Their teams are moving fully over to PyTorch for developing their ML capabilities and services.