November 02, 2023
Accelerating Inference on x86-64 Machines with oneDNN Graph
Supported in PyTorch 2.0 as a beta feature, oneDNN Graph leverages aggressive fusion patterns to accelerate inference on x86-64 machines, especially Intel® Xeon® Scalable processors.
October 31, 2023
AMD Extends Support for PyTorch Machine Learning Development on Select RDNA™ 3 GPUs with ROCm™ 5.7
Researchers and developers working with Machine Learning (ML) models and algorithms using PyTorch can now use AMD ROCm 5.7 on Ubuntu® Linux® to tap into the parallel computing power of the Radeon™ RX 7900 XTX and the Radeon™ PRO W7900 graphics cards which are based on the AMD RDNA™ 3 GPU architecture.
October 17, 2023
PyTorch Edge: Enabling On-Device Inference Across Mobile and Edge Devices with ExecuTorch
We are excited to announce ExecuTorch, our all-new solution for enabling on-device inference capabilities across mobile and edge devices with the backing of industry leaders like Arm, Apple, and Qualcomm Innovation Center.
October 17, 2023
Lightning AI Joins the PyTorch Foundation as a Premier Member
The PyTorch Foundation, a neutral home for the deep learning community to collaborate on the open source PyTorch framework and ecosystem, is announcing today that Lightning AI has joined as a premier member.
October 17, 2023
Huawei Joins the PyTorch Foundation as a Premier Member
Today, the PyTorch Foundation, a neutral home for the deep learning community to collaborate on the open source PyTorch framework and ecosystem, announced that Huawei has joined as a premier member.
October 17, 2023
Compiling NumPy code into C++ or CUDA via torch.compile
Quansight engineers have implemented support for tracing through NumPy code via torch.compile in PyTorch 2.1. This feature leverages PyTorch’s compiler to generate efficient fused vectorized code without having to modify your original NumPy code. Even more, it also allows for executing NumPy code on CUDA just by running it through torch.compile under torch.device("cuda")!
