Introducing Lightning Transformers, a new library that seamlessly integrates PyTorch Lightning, HuggingFace Transformers and Hydra, to scale up deep learning research across multiple modalities. —

Machine learning metrics making evaluations of distributed PyTorch models clean and simple. — Figuring out which metrics you need to evaluate is key to deep learning. There are various metrics that we can evaluate the performance of ML algorithms. TorchMetrics is a collection of PyTorch metric implementations, originally a part of the PyTorch Lightning framework for high-performance deep learning. This article will go…

PyTorch’s Meta Tensors can save you huge amounts of time. PyTorch Lightning, together with DeepSpeed and just a single line of code, allows you to train large, billion-parameter models even faster. — PyTorch Lighting is a lightweight PyTorch wrapper for high-performance AI research. PyTorch Lightning provides true flexibility by reducing the engineering boilerplate and resources required to implement state-of-the-art AI. …

Presenting Bayesian Active Learning (BaaL) with Lightning Flash to train faster with fewer samples. — Lightning Flash is a PyTorch AI Factory built on top of PyTorch Lightning. Flash helps you quickly develop strong baselines on your data across multiple tasks and data modalities. BaaL is a bayesian active learning library developed at ElementAI. Element AI makes its BAyesian Active Learning library open source Element AI's BAyesian Active Learning library (BaaL library) is now open source and available on GitHub. In this…www.elementai.com

Persist the state of multiple checkpoint callbacks, enabling a more advanced configuration of checkpointing strategies. — PyTorch Lightning v1.5 marks a major leap of reliability to support the increasingly complex demands of the leading AI organizations and prestigious research labs that rely on Lightning to develop and deploy AI at scale. As a result of our growth, PyTorch Lightning aims to be the most accessible, flexible…

The Lightning v1.5 introduces a new plugin to enable better extensibility for custom checkpointing implementation. — PyTorch Lightning v1.5 marks a major leap of reliability to support the increasingly complex demands of the leading AI organizations and prestigious research labs that rely on Lightning to develop and deploy AI at scale. Pytorch Lightning aims to bethe most accessible, flexible, and stable framework for expediting any kind…

Lightning introduces BFloat16 support with a single flag to the Trainer, for faster and stable training in lower precision. — PyTorch Lightning v1.5 marks a major leap of reliability to support the increasingly complex demands of the leading AI organizations and prestigious research labs that rely on Lightning to develop and deploy AI at scale. To better support our fast-growing community, PyTorch Lightning aims at becoming the simplest, most flexible…

Introducing Improved Lightning Logger Support for Weight & Biases + Neptune.ai — PyTorch Lightning v1.5 marks a major leap of reliability to support the increasingly complex demands of the leading AI organizations and prestigious research labs that rely on Lightning to develop and deploy AI at scale. To better support our fast-growing community, PyTorch Lightning aims at becoming the simplest, most flexible…

Lightning 1.5 introduces Fault-Tolerant Training, LightningLite, Loops Customization, Lightning Tutorials, RichProgressBar, LightningCLI V2, with many more exciting features to be announced. — PyTorch Lightning v1.5 marks a significant leap of reliability to support the increasingly complex demands of the leading AI organizations and prestigious research labs that rely on Lightning to develop and deploy AI at scale.