107 Open source projects that are alternatives of or similar to Hyperas

Using Kafka-Python to illustrate a ML production pipeline

Machine Learning Tool Box

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

Tuning hyperparams fast with Hyperband

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

A general, modular, and programmable architecture search framework

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

a distributed Hyperband implementation on Steroids

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

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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

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DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

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

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

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.

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

A curated list of awesome Distributed Deep Learning resources.

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

Notes for Deep Learning Specialization Courses led by Andrew Ng.

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

Purely functional genetic algorithms for multi-objective optimisation

Automated modeling and machine learning framework FEDOT

Free automated deep learning for spreadsheets

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

A toolset for black-box hyperparameter optimisation.

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.

mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...

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

A fully decentralized hyperparameter optimization framework

Sequential Model-based Algorithm Configuration

A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.

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

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

A hyperparameter optimization framework

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Experimental Global Optimization Algorithm

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

Hyperparameter optimization for PyTorch.

Toolbox for Bayesian Optimization and Model-Based Optimization in R

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

Python library for CMA Evolution Strategy.

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

State-of-the art Automated Machine Learning python library for Tabular Data

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

Determined: Deep Learning Training Platform

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

A simple, extensible library for developing AutoML systems

Isn't that what we all want? Our money to go many? Well that's what this framework/strategy hopes to do for you! By giving you & HyperOpt a lot of signals to alter the weights from.

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A library for performing hyperparameter optimization

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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…

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

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

An automatic ML model optimization tool.