March 03, 2021
The torch.fft module: Accelerated Fast Fourier Transforms with Autograd in PyTorch
The Fast Fourier Transform (FFT) calculates the Discrete Fourier Transform in O(n log n) time. It is foundational to a wide variety of numerical algorithms and signal processing techniques since it makes working in signals’ “frequency domains” as tractable as working in their spatial or temporal domains.
November 12, 2020
Prototype Features Now Available - APIs for Hardware Accelerated Mobile and ARM64 Builds
Today, we are announcing four PyTorch prototype features. The first three of these will enable Mobile machine-learning developers to execute models on the full set of hardware (HW) engines making up a system-on-chip (SOC). This gives developers options to optimize their model execution for unique performance, power, and system-level concurrency.
November 01, 2020
Announcing PyTorch Developer Day 2020
Starting this year, we plan to host two separate events for PyTorch: one for developers and users to discuss core technical development, ideas and roadmaps called “Developer Day”, and another for the PyTorch ecosystem and industry communities to showcase their work and discover opportunities to collaborate called “Ecosystem Day” (scheduled for early 2021).
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.
