Hpbandstera distributed Hyperband implementation on Steroids
Stars: ✭ 456 (+700%)
Smac3Sequential Model-based Algorithm Configuration
Stars: ✭ 564 (+889.47%)
Hyperopt.jlHyperparameter optimization in Julia.
Stars: ✭ 144 (+152.63%)
Cornell MoeA Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
Stars: ✭ 198 (+247.37%)
Gradient Free OptimizersSimple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Stars: ✭ 711 (+1147.37%)
hyper-enginePython library for Bayesian hyper-parameters optimization
Stars: ✭ 80 (+40.35%)
SimpleExperimental Global Optimization Algorithm
Stars: ✭ 450 (+689.47%)
MlrmboToolbox for Bayesian Optimization and Model-Based Optimization in R
Stars: ✭ 173 (+203.51%)
ChocolateA fully decentralized hyperparameter optimization framework
Stars: ✭ 112 (+96.49%)
HyperactiveA hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.
Stars: ✭ 182 (+219.3%)
HypertunityA toolset for black-box hyperparameter optimisation.
Stars: ✭ 119 (+108.77%)
syne-tuneLarge scale and asynchronous Hyperparameter Optimization at your fingertip.
Stars: ✭ 105 (+84.21%)
ultraoptDistributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。
Stars: ✭ 93 (+63.16%)
Auto SklearnAutomated Machine Learning with scikit-learn
Stars: ✭ 5,916 (+10278.95%)
SherpaHyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Stars: ✭ 289 (+407.02%)
Scikit OptimizeSequential model-based optimization with a `scipy.optimize` interface
Stars: ✭ 2,258 (+3861.4%)
GpflowoptBayesian Optimization using GPflow
Stars: ✭ 229 (+301.75%)
NniAn open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Stars: ✭ 10,698 (+18668.42%)
mangoParallel Hyperparameter Tuning in Python
Stars: ✭ 241 (+322.81%)
mindwareAn efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Stars: ✭ 34 (-40.35%)
FEDOTAutomated modeling and machine learning framework FEDOT
Stars: ✭ 312 (+447.37%)
AutoOEDAutoOED: Automated Optimal Experimental Design Platform
Stars: ✭ 87 (+52.63%)
phoenicsPhoenics: Bayesian optimization for efficient experiment planning
Stars: ✭ 68 (+19.3%)
tunetaIntelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
Stars: ✭ 77 (+35.09%)
OptunaA hyperparameter optimization framework
Stars: ✭ 5,679 (+9863.16%)
Test TubePython library to easily log experiments and parallelize hyperparameter search for neural networks
Stars: ✭ 663 (+1063.16%)
SG MCMCImplementation of Stochastic Gradient MCMC algorithms
Stars: ✭ 37 (-35.09%)
open-boxGeneralized and Efficient Blackbox Optimization System [SIGKDD'21].
Stars: ✭ 174 (+205.26%)
Hyperparameter hunterEasy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Stars: ✭ 648 (+1036.84%)
NeuraxleA Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.
Stars: ✭ 377 (+561.4%)
bayexBayesian Optimization in JAX
Stars: ✭ 24 (-57.89%)
polystoresA library for performing hyperparameter optimization
Stars: ✭ 48 (-15.79%)
Mljar SupervisedAutomated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Stars: ✭ 961 (+1585.96%)
RobynRobyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define m…
Stars: ✭ 433 (+659.65%)
HypersearchHyperparameter optimization for PyTorch.
Stars: ✭ 376 (+559.65%)
mlrHyperoptEasy Hyper Parameter Optimization with mlr and mlrMBO.
Stars: ✭ 30 (-47.37%)
EmukitA Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Stars: ✭ 316 (+454.39%)
AutogluonAutoGluon: AutoML for Text, Image, and Tabular Data
Stars: ✭ 3,920 (+6777.19%)
Deep trafficMIT DeepTraffic top 2% solution (75.01 mph) 🚗.
Stars: ✭ 47 (-17.54%)
Rl Baselines ZooA collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Stars: ✭ 839 (+1371.93%)
codeflareSimplifying the definition and execution, scaling and deployment of pipelines on the cloud.
Stars: ✭ 163 (+185.96%)
bboptBlack box hyperparameter optimization made easy.
Stars: ✭ 66 (+15.79%)
optuna-allennlp🚀 A demonstration of hyperparameter optimization using Optuna for models implemented with AllenNLP.
Stars: ✭ 17 (-70.18%)
cmaesPython library for CMA Evolution Strategy.
Stars: ✭ 174 (+205.26%)
BayesianoptimizationA Python implementation of global optimization with gaussian processes.
Stars: ✭ 5,611 (+9743.86%)
Awesome Automl PapersA curated list of automated machine learning papers, articles, tutorials, slides and projects
Stars: ✭ 3,198 (+5510.53%)
FLEXSFitness landscape exploration sandbox for biological sequence design.
Stars: ✭ 92 (+61.4%)
miraimlMiraiML: asynchronous, autonomous and continuous Machine Learning in Python
Stars: ✭ 23 (-59.65%)
osprey🦅Hyperparameter optimization for machine learning pipelines 🦅
Stars: ✭ 71 (+24.56%)
HyperbandTuning hyperparams fast with Hyperband
Stars: ✭ 555 (+873.68%)
Meta-SACAuto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
Stars: ✭ 19 (-66.67%)
GPimGaussian processes and Bayesian optimization for images and hyperspectral data
Stars: ✭ 29 (-49.12%)
autotuneAutonomous Performance Tuning for Kubernetes !
Stars: ✭ 84 (+47.37%)
ytoptytopt: machine-learning-based search methods for autotuning
Stars: ✭ 17 (-70.18%)