133 Open source projects that are alternatives of or similar to bbopt

Distribution transparent Machine Learning experiments on Apache Spark

Parallel Hyperparameter Tuning in Python

Generalized and Efficient Blackbox Optimization System.

Purely functional genetic algorithms for multi-objective optimisation

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

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

Machine Learning Tool Box

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

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

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

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

A general-purpose pythonic genetic algorithm.

A scikit-learn compatible implementation of hyperband

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

Hyperparameter optimization package of the mlr3 ecosystem

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

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

Sequential Model-based Algorithm Configuration

An automatic ML model optimization tool.

Library for Semi-Automated Data Science

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.

Automated Machine Learning with scikit-learn

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

Determined: Deep Learning Training Platform

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

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

Tuning hyperparams fast with Hyperband

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

A library for performing hyperparameter optimization

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

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

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Coconut syntax highlighting for Sublime Text and VSCode.

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

Audio feature extraction and classification

Diviner is a serverless machine learning and hyper parameter tuning platform

Iterated Racing for Automatic Algorithm Configuration

Scalable, structured, dynamically-scheduled hyperparameter optimization.

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

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

Data System for Optimized Deep Learning Model Selection

Autonomous Performance Tuning for Kubernetes !

Julia interface to the NOMAD blackbox optimization software

Elo ratings for global black box derivative-free optimizers

A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.

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

OptKeras: wrapper around Keras and Optuna for hyperparameter optimization

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

Streamlined machine learning experiment management.

A Clojure machine learning library

Bootstrap for Haxe/Coconut.ui

Resolution theorem proving for predicate logic in pure Python.

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

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

MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

A shell-friendly hyperparameter search tool inspired by Optuna

Using Kafka-Python to illustrate a ML production pipeline

Python code for bayesian optimization using Gaussian processes

Bayesian Optimization using GPflow

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