March 24, 2021
PyTorch for AMD ROCm™ Platform now available as Python package
With the PyTorch 1.8 release, we are delighted to announce a new installation option for users of PyTorch on the ROCm™ open software platform. An installable Python package is now hosted on pytorch.org, along with instructions for local installation in the same simple, selectable format as PyTorch packages for CPU-only configurations and other GPU platforms. PyTorch on ROCm includes full capability for mixed-precision and large-scale training using AMD’s MIOpen & RCCL libraries. This prov...
March 09, 2021
Announcing PyTorch Ecosystem Day
We’re proud to announce our first PyTorch Ecosystem Day. The virtual, one-day event will focus completely on our Ecosystem and Industry PyTorch communities!
March 04, 2021
PyTorch 1.8 Release, including Compiler and Distributed Training updates, and New Mobile Tutorials
We are excited to announce the availability of PyTorch 1.8. This release is composed of more than 3,000 commits since 1.7. It includes major updates and new features for compilation, code optimization, frontend APIs for scientific computing, and AMD ROCm support through binaries that are available via pytorch.org. It also provides improved features for large-scale training for pipeline and model parallelism, and gradient compression. A few of the highlights include:
- Support for doi...
March 04, 2021
New PyTorch library releases including TorchVision Mobile, TorchAudio I/O, and more
Today, we are announcing updates to a number of PyTorch libraries, alongside the PyTorch 1.8 release. The updates include new releases for the domain libraries including TorchVision, TorchText and TorchAudio as well as new version of TorchCSPRNG. These releases include a number of new features and improvements and, along with the PyTorch 1.8 release, provide a broad set of updates for the PyTorch community to build on and leverage.
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.
