July 28, 2020
Microsoft becomes maintainer of the Windows version of PyTorch
Along with the PyTorch 1.6 release, we are excited to announce that Microsoft has expanded its participation in the PyTorch community and will be responsible for the development and maintenance of the PyTorch build for Windows.
July 28, 2020
Introducing native PyTorch automatic mixed precision for faster training on NVIDIA GPUs
Most deep learning frameworks, including PyTorch, train with 32-bit floating point (FP32) arithmetic by default. However this is not essential to achieve full accuracy for many deep learning models. In 2017, NVIDIA researchers developed a methodology for mixed-precision training, which combined single-precision (FP32) with half-precision (e.g. FP16) format when training a network, and achieved the same accuracy as FP32 training using the same hyperparameters, with additional performance benef...
May 05, 2020
Updates & Improvements to PyTorch Tutorials
PyTorch.org provides researchers and developers with documentation, installation instructions, latest news, community projects, tutorials, and more. Today, we are introducing usability and content improvements including tutorials in additional categories, a new recipe format for quickly referencing common topics, sorting using tags, and an updated homepage.
April 21, 2020
PyTorch library updates including new model serving library
Along with the PyTorch 1.5 release, we are announcing new libraries for high-performance PyTorch model serving and tight integration with TorchElastic and Kubernetes. Additionally, we are releasing updated packages for torch_xla (Google Cloud TPUs), torchaudio, torchvision, and torchtext. All of these new libraries and enhanced capabilities are available today and accompany all of the core features released in PyTorch 1.5.
April 21, 2020
PyTorch 1.5 released, new and updated APIs including C++ frontend API parity with Python
Today, we’re announcing the availability of PyTorch 1.5, along with new and updated libraries. This release includes several major new API additions and improvements. PyTorch now includes a significant update to the C++ frontend, ‘channels last’ memory format for computer vision models, and a stable release of the distributed RPC framework used for model-parallel training. The release also has new APIs for autograd for hessians and jacobians, and an API that allows the creation of Custom C++ ...
March 26, 2020
Introduction to Quantization on PyTorch
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
PyTorch 1.4 released, domain libraries updated
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 workshops at NeurIPS, the content can be found here.
