9 stories tagged Pytorch
Google released a tool to help PyTorch developers move AI apps off Nvidia GPUs, joining Amazon in a bid to reduce reliance on Nvidia's hardware. This could lower costs and increase flexibility for AI developers.
Google and Meta have released TorchTPU, an open-source hybrid eager/compiled architecture that enables native PyTorch execution on Google TPUs with industry-competitive training and inference performance.
OpenLanguageModel (OLM) is an open-source PyTorch library that makes small language model pretraining readable and composable. Its code mirrors the architecture using components like Block, Residual, Repeat, and Parallel, enabling use in teaching notebooks, full pretraining runs, and research ablations without modification.
The Hugging Face blog has published a detailed guide on profiling attention mechanisms in PyTorch, the third in a series on PyTorch profiling. The guide walks through how to use PyTorch's profiling tools to identify performance bottlenecks in transformer models, with a focus on attention layers.
PyTorch has introduced a new optimization technique called fused MLP that speeds up AI model training. This advancement makes it easier and faster for developers to build and train complex AI models.
PyTorch's torch.profiler helps you find performance bottlenecks in your AI code. It's a powerful tool for developers, even if you're new to optimization.
OpenMythos is an open-source implementation of Claude Mythos, using a looped transformer with Mixture-of-Experts routing. This project aims to advance theoretical AI research by providing a transparent, modular framework.
Safetensors, a popular open-source library for secure model serialization, has joined the PyTorch Foundation. This move aims to improve AI safety standards and interoperability within the PyTorch ecosystem.
The PyTorch Foundation has added three new projects to its AI stack: Safetensors, ExecuTorch, and Helion. These tools aim to enhance safety, deployment, and performance in AI development.