dgl 2.2.1

DeepSpeed is a Python package developed by Microsoft that provides a deep learning optimization library designed to scale across multiple GPUs and servers. It is capable of training models with billions or even trillions of parameters, achieving excellent system throughput and efficiently scaling to thousands of GPUs.

DeepSpeed is particularly useful for training and inference of large language models, and it falls under the category of Machine Learning Frameworks and Libraries. It is designed to work with PyTorch and offers system innovations such as Zero Redundancy Optimizer (ZeRO), 3D parallelism, and model-parallelism to enable efficient training of large models.

Deep Learning experiments from University of Chicago.

HuggingFace community-driven open-source library of datasets

ClearML Agent - Auto-Magical DevOps for Deep Learning

ClearML - Auto-Magical Experiment Manager, Version Control, and MLOps for AI

Progress bar for deep learning training iterations

Fast and Accurate ML in 3 Lines of Code

An open-source NLP research library, built on PyTorch.

Fast, flexible, and advanced augmentation library for deep learning, computer vision, and medical imaging. Albumentations offers a wide range of transformations for both 2D (images, masks, bboxes, keypoints) and 3D (volumes, volumetric masks, keypoints) data, with optimized performance and seamless integration into ML workflows.