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Fast and lightweight AutoML (paper).

A hyperparameter optimization framework, inspired by Optuna.

a framework for distributed hyperparameter optimization.

a library for hyperparameter optimization and black box optimization benchmarks.

open source code for tuning hyperparams with Hyperband.

A General Automated Machine Learning Framework.

Distributed Asynchronous Hyperparameter Optimization in Python.

A toolset for black-box hyperparameter optimisation.

A framework to connect a flow of ML models by applying graph theory.

Katib is a Kubernetes-native project for automated machine learning (AutoML).

PyTorch Meta-learning Framework for Researchers.

a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.

a global, black box optimization engine for real world metric optimization by Yelp.

a framework that implements AutoML algorithms for model architecture search at scale.

a proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Bayesian optimization in high-dimensions via random embedding.

a Robust Bayesian Optimization framework.

Sequential model-based optimization with a scipy.optimize interface.

A Meta-Learning library for PyTorch.

an AutoML algorithm tool chain by Huawei Noah's Arb Lab.

Information-theoretic context optimization proxy. Cuts LLM token costs by 70–95% with zero accuracy loss using greedy submodular knapsack maximization.

FeatherCNN is a high performance inference engine for convolutional neural networks.

A library for high performance deep learning inference on NVIDIA GPUs.

Context runtime and MCP server that reduces AI coding agent token costs via session caching, AST-aware compression, and shell output patterns. Website

ncnn is a high-performance neural network inference framework optimized for the mobile platform.

use AutoML to do model compression.

A suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution.

A uniform deep learning inference framework for mobile, desktop and server.

Google TPU optimizations for transformers models

Automated optimization engine for improving agent workflows using feedback-driven iterative refinements.

An Easy-to-use Federated Learning Platform

An Industrial Grade Federated Learning Framework

The federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale. Supporting large-scale cross-silo federated learning, cross-device federated learning on smartphones/IoTs, and research simulation.

A Friendly Federated Learning Framework

Harmonia is an open-source project aiming at developing systems/infrastructures and libraries to ease the adoption of federated learning (abbreviated to FL) for researches and production usage.