March 25, 2021
Along with PyTorch 1.8.1 release, we are excited to announce PyTorch Profiler – the new and improved performance debugging profiler for PyTorch. Developed as part of a collaboration between Microsoft and Facebook, the PyTorch Profiler is an open-source tool that enables accurate and efficient performance analysis and troubleshooting for large-scale deep learning models.
March 24, 2021
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
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
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
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 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
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
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).