Awesome Opensource Ai

Fast and Accurate ML in 3 Lines of Code

OpenVINO™ is an open source toolkit for optimizing and deploying AI inference

Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, Slurm, 20+ clouds, on-prem).

A collection of pre-trained, state-of-the-art models in the ONNX format

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

A python library for user-friendly forecasting and anomaly detection on time series.

AutoML library for deep learning

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

ModelScope: bring the notion of Model-as-a-Service to life.

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.

AI Toolkit for Healthcare Imaging

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

TensorFlow documentation

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

Supercharge Your Model Training

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2026 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

Simple and Distributed Machine Learning

Probabilistic time series modeling in Python

This repo contains the Hugging Face Deep Reinforcement Learning Course.

Time series forecasting with PyTorch

An Engine-Agnostic Deep Learning Framework in Java

On-device AI across mobile, embedded and edge for PyTorch

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Relax! Flux is the ML library that doesn't make you tensor

Fast inference engine for Transformer models

LLM training code for Databricks foundation models

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.

TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.

The official Python client for the Hugging Face Hub.

AI Infra / AI Orchestration / AI Control Plane

Synthetic data generation for tabular data

JAX-based neural network library

Open source, local, and self-hosted Amazon Echo/Google Home competitive Voice Assistant alternative

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Machine learning metrics for distributed, scalable PyTorch applications.

SDG is a specialized framework designed to generate high-quality structured tabular data.

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

Interpretability and explainability of data and machine learning models

Synthetic data curation for post-training and structured data extraction

A Data Streaming Library for Efficient Neural Network Training

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.