October 28, 2020
Adding a Contributor License Agreement for PyTorch
To ensure the ongoing growth and success of the framework, we’re introducing the use of the Apache Contributor License Agreement (CLA) for PyTorch. We care deeply about the broad community of contributors who make PyTorch such a great framework, so we want to take a moment to explain why we are adding a CLA.
October 27, 2020
PyTorch 1.7 released w/ CUDA 11, New APIs for FFTs, Windows support for Distributed training and more
Today, we’re announcing the availability of PyTorch 1.7, along with updated domain libraries. The PyTorch 1.7 release includes a number of new APIs including support for NumPy-Compatible FFT operations, profiling tools and major updates to both distributed data parallel (DDP) and remote procedure call (RPC) based distributed training. In addition, several features moved to stable including custom C++ Classes, the m...
October 01, 2020
Announcing the Winners of the 2020 Global PyTorch Summer Hackathon
More than 2,500 participants in this year’s Global PyTorch Summer Hackathon pushed the envelope to create unique new tools and applications for PyTorch developers and researchers.
August 24, 2020
PyTorch framework for cryptographically secure random number generation, torchcsprng, now available
One of the key components of modern cryptography is the pseudorandom number generator. Katz and Lindell stated, “The use of badly designed or inappropriate random number generators can often leave a good cryptosystem vulnerable to attack. Particular care must be taken to use a random number generator that is designed for cryptographic use, rather than a ‘general-purpose’ random number generator which may be fine for some applications but not ones that are required to be cryptographically secu...
August 18, 2020
PyTorch 1.6 now includes Stochastic Weight Averaging
Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly better generalization at virtually no additional cost with a simple new technique now natively supported in PyTorch 1.6, Stochastic Weight Averaging (SWA) [1]. Even if you have already trained your model, it’s easy to realize the benefits of SWA by running SWA for a small number of epochs starting with a pre-trained model.
August 11, 2020
Efficient PyTorch I/O library for Large Datasets, Many Files, Many GPUs
Data sets are growing bigger every day and GPUs are getting faster. This means there are more data sets for deep learning researchers and engineers to train and validate their models.
July 28, 2020
PyTorch 1.6 released w/ Native AMP Support, Microsoft joins as maintainers for Windows
Today, we’re announcing the availability of PyTorch 1.6, along with updated domain libraries. We are also excited to announce the team at Microsoft is now maintaining Windows builds and binaries and will also be supporting the community on GitHub as well as the PyTorch Windows discussion forums.
July 28, 2020
PyTorch feature classification changes
Traditionally features in PyTorch were classified as either stable or experimental with an implicit third option of testing bleeding edge features by building master or through installing nightly builds (available via prebuilt whls). This has, in a few cases, caused some confusion around the level of readiness, commitment to the feature and backward compatibility that can be expected from a user perspective. Moving forward, we’d like to better classify the 3 types of features as well as defin...
