ML Libraries

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  • ​ml5.js - Friendly machine learning for the web.

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  • ​SynapseML - Simple and Distributed Machine Learning. (Web) (Article)
  • ​imgaug - Image augmentation for machine learning experiments.
  • ​PlaidML - Framework for making deep learning work everywhere.
  • ​Leaf - Open Machine Intelligence Framework for Hackers. (GPU/CPU).
  • ​Apache MXNet - Deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity.
  • ​Sonnet - Library built on top of TensorFlow for building complex neural networks.
  • ​tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators.
  • ​dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
  • ​PySyft - Library for encrypted, privacy preserving deep learning.
  • ​numpy-ml - Machine learning, in numpy.
  • ​cuML - Suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects.
  • ​ONNX Runtime - Cross-platform, high performance scoring engine for ML models.
  • ​MLflow - Machine Learning Lifecycle Platform.
  • ​auto-sklearn - Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
  • ​TensorNetwork - Library for easy and efficient manipulation of tensor networks.
  • ​lambda-ml - Small machine learning library aimed at providing simple, concise implementations of machine learning techniques and utilities.
  • ​scikit-learn - Python module for machine learning built on top of SciPy. (Tutorials) (Course) (Web) (HN)
  • ​MLBox - Powerful Automated Machine Learning python library.
  • ​Mlxtend (machine learning extensions) - Python library of useful tools for the day-to-day data science tasks.
  • ​CrypTen - Framework for Privacy Preserving Machine Learning built on PyTorch.
  • ​Faiss - Library for efficient similarity search and clustering of dense vectors. (Tips)
  • ​pyHSICLasso - Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data.
  • ​AutoGluon - AutoML Toolkit for Deep Learning.
  • ​DeepLearning.scala - Simple library for creating complex neural networks from object-oriented and functional programming constructs.
  • ​Optuna - Hyperparameter optimization framework. (Optuna Dashboard)
  • ​Vowpal Wabbit - Machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. (Web) (Article)
  • ​Brancher - User-centered Python package for differentiable probabilistic inference.
  • ​Karate Club - General purpose community detection and network embedding library for research built on NetworkX.
  • ​FlexFlow - Distributed deep learning framework that supports flexible parallelization strategies.
  • ​DeltaPy - Tabular Data Augmentation & Feature Engineering.
  • ​TensorStore - Library for reading and writing large multi-dimensional arrays.
  • ​FATE - Industrial Level Federated Learning Framework.
  • ​Deepkit - Collaborative and real-time machine learning training suite: Experiment execution, tracking, and debugging.
  • ​Sls - Stochastic Line Search.
  • ​PyCaret - Open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. (Web)
  • ​Flax - Neural network library for JAX designed for flexibility. (Docs)
  • ​scikit-multilearn - Python module capable of performing multi-label learning tasks.
  • ​imbalanced-learn - Python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.
  • ​DeepSpeed - Deep learning optimization library that makes distributed training easy, efficient, and effective.
  • ​HoMM - Library for Homoiconic Meta-mapping.
  • ​Hummingbird - Library for compiling trained traditional ML models into tensor computations.
  • ​Ax - Accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments.
  • ​Neuropod - Uniform interface to run deep learning models from multiple frameworks.
  • ​aerosolve - Machine learning package built for humans in Scala.
  • ​Kur - Descriptive Deep Learning.
  • ​NNI (Neural Network Intelligence) - Lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.
  • ​LMfit-py - Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize.leastsq, and with many additional classes and methods for curve fitting.
  • ​tslearn - Machine learning toolkit for time series analysis in Python.
  • ​Libra - Ergonomic machine learning for everyone. (Docs)
  • ​NGBoost - Natural Gradient Boosting for Probabilistic Prediction.
  • ​LightGBM - Gradient boosting framework that uses tree based learning algorithms.
  • ​XGBoost - Optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework.
  • ​DMLC-Core - Common bricks library for building scalable and portable distributed machine learning.
  • ​Linear Models - Add linear models including instrumental variable and panel data models that are missing from statsmodels.
  • ​skift - scikit-learn wrappers for Python fastText.
  • ​pulearn - Positive-unlabeled learning with Python.
  • ​pescador - Library for streaming (numerical) data, primarily for use in machine learning applications.
  • ​TPOT (Tree-based Pipeline Optimization Tool) - Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. (Docs)
  • ​GraKeL - Library that provides implementations of several well-established graph kernels. scikit-learn compatible.
  • ​creme - Python library for online machine learning. All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data. (Docs)
  • ​RecBole - Unified, comprehensive and efficient recommendation library.
  • ​NNFusion - Flexible and efficient DNN compiler that can generate high-performance executables from a DNN model description.
  • ​ncnn - High-performance neural network inference computing framework optimized for mobile platforms.
  • ​Scikit-Optimize - Sequential model-based optimization with a scipy.optimize interface.
  • ​scikit-rebate - Scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
  • ​Fedlearner - Collaborative machine learning frameowork that enables joint modeling of data distributed between institutions.
  • ​SkLearn2PMML - Python library for converting Scikit-Learn pipelines to PMML.
  • ​vecstack - Python package for stacking (machine learning technique).
  • ​LightSeq - High Performance Inference Library for Sequence Processing and Generation.
  • ​modestpy - Facilitates parameter estimation in models compliant with Functional Mock-up Interface.
  • ​Distiller - Open-source Python package for neural network compression research.
  • ​modAL - Modular active learning framework for Python.
  • ​Bambi - BAyesian Model-Building Interface in Python.
  • ​Bolt - Deep learning library with high performance and heterogeneous flexibility.
  • ​hypothesis - Python toolkit for (simulation-based) inference and the mechanization of science.
  • ​MMFeat - Multi-modal features toolkit in Python.
  • ​Flower - Friendly Federated Learning Framework. (Web) (Flower Summit 2021)
  • ​brain.js - GPU accelerated Neural networks in JavaScript for Browsers and Node.js. (Web)
  • ​Buffalo - Fast and scalable production-ready open source project for recommender systems.
  • ​EvalML - AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
  • ​MindSpore - New open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
  • ​Flashlight - Fast, Flexible Machine Learning in C++.
  • ​raster-deep-learning - ArcGIS built-in python raster functions for deep learning to get you started fast.
  • ​CTranslate2 - Fast inference engine for OpenNMT models.
  • ​Causal Discovery Toolbox - Algorithms for graph structure recovery (including algorithms from the bnlearn, pcalg packages), mainly based out of observational data.
  • ​FedML - Research Library and Benchmark for Federated Machine Learning.
  • ​Auto_TS - Automatically build multiple Time Series models using a Single Line of Code.
  • ​AutoGL (Auto Graph Learning) - AutoML framework & toolkit for machine learning on graphs.
  • ​tsalib - Tensor Shape Annotation Library (numpy, tensorflow, pytorch, ...).
  • ​MMClassification - Open source image classification toolbox based on PyTorch.
  • ​Nimble - Lightweight and Parallel GPU Task Scheduling for Deep Learning.
  • ​Dannjs - Neural Network library for JavaScript. (Web)
  • ​Shapley - Python library for evaluating binary classifiers in a machine learning ensemble.
  • ​Orion - Machine learning library built for unsupervised time series anomaly detection.
  • ​BigDL - Distributed Deep Learning on Apache Spark. (Docs)
  • ​MNN - Blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba.
  • ​Haste - CUDA implementation of fused RNN layers with built-in DropConnect and Zoneout regularization.
  • ​sklearn-xarray - Metadata-aware machine learning.
  • ​dabnn - Accelerated binary neural networks inference framework for mobile platform.
  • ​OneFlow - Performance-centered and open-source deep learning framework.
  • ​DeepWalk - Deep Learning for Graphs. (Web)
  • ​sequitur - Autoencoders for sequence data.
  • ​cleanlab - Machine learning python package for learning with noisy labels and finding label errors in datasets.
  • ​deeptime - Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation.
  • ​Jelly Bean World - Framework for experimenting with never-ending learning.
  • ​Larq - Open-source deep learning library for training neural networks with extremely low precision weights and activations, such as Binarized Neural Networks (BNNs). (Web)
  • ​tsai - State-of-the-art Deep Learning for Time Series and Sequence Modeling.
  • ​edbo - Experimental Design via Bayesian Optimization.
  • ​TensorJS - JS/TS library for accelerated tensor computation intended to be run in the browser.
  • ​micro-TCN - Efficient neural networks for audio effect modeling. (Web)
  • ​DESlib - Python library for dynamic classifier and ensemble selection.
  • ​BytePS - High performance and generic framework for distributed DNN training.
  • ​Hyperactive - Hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-learning models.
  • ​Jittor - Just-in-time(JIT) deep learning framework.
  • ​autofeat - Linear Prediction Model with Automated Feature Engineering and Selection Capabilities.
  • ​Distrax - Lightweight library of probability distributions and bijectors. It acts as a JAX-native reimplementation of a subset of TensorFlow Probability (TFP).
  • ​scikit-learn-extra - Set of useful tools compatible with scikit-learn.
  • ​GeneticAlgorithmPython - Building Genetic Algorithm in Python.
  • ​Newt - Gaussian process library in JAX.
  • ​Hedgehog - Bayesian networks in Python.
  • ​Backdoors 101 - PyTorch framework for state-of-the-art backdoor defenses and attacks on deep learning models.
  • ​Sabertooth - Standalone pre-training recipe with JAX+Flax.
  • ​ProbFlow - Python package for building Bayesian models with TensorFlow or PyTorch.
  • ​Mars - Tensor-based unified framework for large-scale data computation which scales Numpy, pandas, Scikit-learn and Python functions.
  • ​DeepMatch - Deep matching model library for recommendations & advertising.
  • ​Layout Parser - Unified toolkit for Deep Learning Based Document Image Analysis. (Web)
  • ​scikit-survival - Survival analysis built on top of scikit-learn.
  • ​PySR - Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing.
  • ​CLU - Contains common functionality for writing ML training loops using JAX.
  • ​SparseML - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models.
  • ​CogDL - Extensive Toolkit for Deep Learning on Graphs. (Web)
  • ​TensorLy - Tensor Learning in Python. (Web)
  • ​Cornac - Comparative Framework for Multimodal Recommender Systems.
  • ​MegEngine - Fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
  • ​SeqIO - Task-based datasets, preprocessing, and evaluation for sequence models.
  • ​OpenAI Python - Provides convenient access to the OpenAI API from applications written in Python.
  • ​Mesh Transformer JAX - Model parallel transformers in JAX and Haiku. (HN)
  • ​deepC - Vendor independent deep learning library, compiler and inference framework designed for small form-factor devices.
  • ​Dlib - Modern C++/Python Toolkit for Machine Learning . (Web) (HN)
  • ​Continuum - Clean and simple data loading library for Continual Learning.
  • ​Smile - Statistical Machine Intelligence & Learning Engine.
  • ​AugLy - Data augmentations library for audio, image, text, and video.
  • ​Surprise - Python scikit for building and analyzing recommender systems. (Web)
  • ​TNN - High-performance, lightweight neural network inference framework.
  • ​Parallax - Immutable Torch Modules for JAX.
  • ​EvalAI - Open source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. (Web)
  • ​Avalanche - End-to-End Library for Continual Learning. (Docs)
  • ​PyKale - Knowledge-Aware machine LEarning (KALE) from multiple sources in Python.
  • ​mltrace - Coarse-grained lineage and tracing for machine learning pipelines.
  • ​PPLNN - High-performance deep-learning inference engine for efficient AI inferencing.
  • ​Petastorm - Enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format.
  • ​Collie - Library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch. (Docs)
  • ​voxelmorph - Unsupervised Learning for Image Registration.
  • ​uTensor - TinyML AI inference library.
  • ​Tangram - Train a model from a CSV file on the command line.. (Web) (HN)
  • ​AdaptDL - Resource-adaptive cluster scheduler for deep learning training.
  • ​Triage - General Purpose Risk Modeling and Prediction Toolkit for Policy and Social Good Problems.
  • ​Gorse - Open source recommender system service written in Go. (Web) (HN)
  • ​LensKit - Python Tools for Recommender Experiments. (Web)
  • ​StarSpace - Learning embeddings for classification, retrieval and ranking.
  • ​ELFI - Engine for Likelihood-Free Inference. (Docs)
  • ​DaisyRec - Python toolkit dealing with rating prediction and item ranking issue.
  • ​AutoTS - Forecasting Model Selection for Multiple Time Series.
  • ​PyFlux - Open source time series library for Python.
  • ​trajax - Python library for differentiable optimal control on accelerators.
  • ​TransmogrifAI - End-to-end AutoML library for structured data written in Scala that runs on top of Apache Spark. (Web)
  • ​chitra - Multi-functional library for full-stack Deep Learning. It simplifies Model Building, API development, and Model Deployment.
  • ​DoubleML - Double Machine Learning in Python.
  • ​jaxfg - Factor graphs and nonlinear optimization in JAX.
  • ​pyltr - Python learning-to-rank toolkit with ranking models, evaluation metrics, data wrangling helpers, and more.
  • ​Wrangl - Ray-based parallel data preprocessing for NLP and ML.
  • ​Treex - Pytree-based Module system for Deep Learning in JAX. (Docs)
  • ​PhiFlow - Open-source simulation toolkit built for optimization and machine learning applications.
  • ​OpenVINO Toolkit - Deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
  • ​WILDS - Machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.
  • ​TurboTransformers - Fast and user-friendly runtime for transformer inference on CPU and GPU.
  • ​DeepOps - Mini Deep Learning framework supporting GPU accelerations written with CUDA.
  • ​Bayex - Bayesian Optimization Python Library powered by JAX.
  • ​Merlion - Machine Learning Framework for Time Series Intelligence.
  • ​Feast - Feature Store for Machine Learning. (Web)
  • ​nnabla - Neural Network Libraries by Sony. (Web)
  • ​RevLib - Simple and efficient RevNet-Library with DeepSpeed support.
  • ​DeepSparse - Neural network inference engine that delivers GPU-class performance for sparsified models on CPUs.
  • ​NVTabular - Engineering and preprocessing library for tabular data that is designed to easily manipulate terabyte scale datasets and train deep learning (DL) based recommender systems.
  • ​Treeo - Small library for creating and manipulating custom JAX Pytree classes.
  • ​FedJAX - JAX-based open source library for Federated Learning simulations that emphasizes ease-of-use in research.
  • ​oneAPI - OneAPI Deep Neural Network Library (oneDNN).
  • ​MosaicML Composer - Library of methods, and ways to compose them together for more efficient ML training.
  • ​deep-significance - Easy and Better Significance Testing for Deep Neural Networks.
  • ​Finetuner - Finetuning any DNN for better embedding on neural search tasks. (Docs)
  • ​mlcrate - Hon module of handy tools and functions, mainly for ML and Kaggle.
  • ​mle-hyperopt - Lightweight Hyperparameter Optimization Tool.
  • ​Feature Engine - Python library with multiple transformers to engineer and select features for use in machine learning models.
  • ​BaaL - Bayesian active learning library.
  • ​TorchArrow - torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format.
  • ​Arm NN - Software and tools that enables machine learning workloads on power-efficient devices.
  • ​OpenRec - Open-source and modular library for neural network-inspired recommendation algorithms.
  • ​FlexFlow - Distributed deep learning framework that supports flexible parallelization strategies.
  • ​ColossalAI - Unified Deep Learning System for Large-Scale Parallel Training. (Docs)
  • ​XManager - Framework for managing machine learning experiments.
  • ​T5X - Modular, composable, research-friendly framework for high-performance, configurable, self-service training.
  • ​mlinspect - Inspect ML Pipelines in Python in the form of a DAG.
  • ​Privacy Lint - Library that allows you to perform a privacy analysis (Membership Inference) of your model in PyTorch.
  • ​NVIDIA Object Detection Toolkit (ODTK) - Fast and accurate single stage object detection with end-to-end GPU optimization.
  • ​DeAI - Decentralized privacy-preserving ML training software framework, using p2p networking.
  • ​Varuna - Tool for efficient training of large DNN models on commodity GPUs and networking.
  • ​reXmeX - General purpose recommender metrics library for fair evaluation.
  • ​Einshape - DSL-based reshaping library for JAX and other frameworks.
  • ​BlobCity AutoAI - Framework to find the best performing AI/ML model for any AI problem.
  • ​PyPAL - Multiobjective active learning with tunable accuracy/efficiency tradeoff and clear stopping criterion.
  • ​RecList - Behavioral "black-box" testing for recommender systems.
  • ​dcbench - Benchmark of data-centric tasks from across the machine learning lifecycle.
  • ​Cockpit - Visual and statistical debugger specifically designed for deep learning.
  • ​CatBoost - Machine learning method based on gradient boosting over decision trees. (Web)
  • ​Xplique - Neural Networks Explainability Toolbox.
  • ​Causal ML - Python Package for Uplift Modeling and Causal Inference with ML.
  • ​sklearn-onnx - Convert scikit-learn models and pipelines to ONNX.
  • ​Tools for JAX - Variety of tools for the differential programming library JAX.
  • ​KML - Machine Learning Framework for Operating Systems & Storage Systems. (HN)
  • ​ENN Incubator - Collection of in-progress libraries for entity neural networks.
  • ​Syne Tune - Large scale and asynchronous Hyperparameter Optimization at your fingertip.
  • ​Maggy - Framework for distribution transparent machine learning experiments on Apache Spark.
  • ​Apache SINGA - Distributed deep learning system. (Web)
  • ​Tiny CUDA Neural Networks - Lightning fast & tiny C++/CUDA neural network framework.
  • ​Apache TVM - Open Deep Learning Compiler Stack.
  • ​imodels - Interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible).
  • ​FLSim - Flexible, standalone library written in PyTorch that simulates FL settings with a minimal, easy-to-use API.
  • ​Human Learn - Machine Learning models should play by the rules, literally.
  • ​MiniTorch - DIY teaching library for machine learning engineers who wish to learn about the internal concepts underlying deep learning systems.
  • ​TorchRecipes - Train machine learning models with a couple of lines of code.
  • ​DABS - Domain-Agnostic Benchmark for Self-Supervised Learning.
  • ​apricot - Implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly.
  • ​Theseus - Library for differentiable nonlinear optimization built on PyTorch.
  • ​MMSelfSup - OpenMMLab Self-Supervised Learning Toolbox and Benchmark.
  • ​NVFlare - NVIDIA Federated Learning Application Runtime Environment. (Docs)
  • ​OSLO - Open Source framework for Large-scale transformer Optimization.
  • ​snntorch - Deep and online learning with spiking neural networks in Python.
  • ​NVIDIA DALI - GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
  • ​MIPLearn - Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML).
  • ​tree-math - Mathematical operations for JAX pytrees.
  • ​ExplainX - Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.
  • ​Contextual AI - Adds explainability to different stages of machine learning pipelines.
  • ​jax_dataclasses - Pytrees + static analysis.
  • ​kingly - Zero-cost state-machine library for robust, testable and portable user interfaces (most machines compile ~1-2KB).
  • ​RTNeural - Lightweight neural network inferencing engine written in C++.
  • ​JAXopt - Hardware accelerated, batchable and differentiable optimizers in JAX.
  • ​chop - Optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.
  • ​WebDNN - Fastest DNN Running Framework on Web Browser.
  • ​nonconformist - Python implementation of the conformal prediction framework.
  • ​jaxdf - JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations.
  • ​DoWhy - End-to-end library for causal inference.
  • ​hypopt - Parallelized hyper-param optimization with validation set, not crossval.
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