Adeepspeed 0.9.2

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.

fastai simplifies training fast and accurate neural nets using modern best practices

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.

Time series forecasting suite using deep learning models

This is a comprehensive library for generalized Out-of-Distribution (OOD) detection research. It provides over 20 detection methods, evaluation pipelines, OOD datasets, and model architectures integrated with timm. Additionally, it offers fast OOD evaluation metrics, multi-layer detection methods, and pipelines for open set recognition and covariate drift detection.

SkyPilot: An intercloud broker for the clouds

Flower: A Friendly Federated Learning Framework

Web app for https://github.com/labmlai/labml

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications. It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more.