Popular GitHub Repositories for Distributed Data Parallel

An Open Source Machine Learning Framework for Everyone

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

Deep Learning for humans

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

? Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.

LlamaIndex is the leading framework for building LLM-powered agents over your data.

Learn how to design, develop, deploy and iterate on production-grade ML applications.

Making large AI models cheaper, faster and more accessible

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Build and share delightful machine learning apps, all in Python. ? Star to support our work!

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

AI's query engine - Platform for building AI that can learn and answer questions over large scale federated data.

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P inference

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

? Industrial-strength Natural Language Processing (NLP) in Python

Easily train a good VC model with voice data <= 10 mins!

Roadmap to becoming an Artificial Intelligence Expert in 2022

Code and documentation to train Stanford's Alpaca models, and generate the data.