102 Open source projects that are alternatives of or similar to Cmaes

Black box hyperparameter optimization made easy.

⚡️ AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy

AutoGluon: AutoML for Text, Image, and Tabular Data

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Streamlined machine learning experiment management.

a distributed Hyperband implementation on Steroids

MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Your local Flux surgeon

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

Robyn 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…

A general-purpose pythonic genetic algorithm.

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

Hyperparameter optimization in Julia.

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

🚀 A demonstration of hyperparameter optimization using Optuna for models implemented with AllenNLP.

A 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.

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A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Examples for https://github.com/optuna/optuna

A curated list of awesome Distributed Deep Learning resources.

A simple collection of well working NLP models (Keras, H2O, StarSpace) tuned and benchmarked on a variety of datasets.

Automated Machine Learning with scikit-learn

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An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

A library for performing hyperparameter optimization

Set model hyperparameters using deterministic, exact algorithms.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Easy Hyper Parameter Optimization with mlr and mlrMBO.

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

Python library for Bayesian hyper-parameters optimization

A hyperparameter optimization framework

Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.

Simple, but essential Bayesian optimization package

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Experimental Global Optimization Algorithm

Python library for CMA Evolution Strategy.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

Hyperparameter optimization for PyTorch.

Autonomous Performance Tuning for Kubernetes !

Determined: Deep Learning Training Platform

A shell-friendly hyperparameter search tool inspired by Optuna

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

Machine Learning Tool Box

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

A friendly python package for Keras Hyperparameters Tuning based only on NumPy and hyperopt.

A curated list of automated machine learning papers, articles, tutorials, slides and projects

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

Using Kafka-Python to illustrate a ML production pipeline

Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Sequential Model-based Algorithm Configuration

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Purely functional genetic algorithms for multi-objective optimisation

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

Tuning hyperparams fast with Hyperband

Automated modeling and machine learning framework FEDOT