Open-source alternatives to MLBox

This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur

EvalML is an AutoML library written in python.

Automatic architecture search and hyperparameter optimization for PyTorch

AutoGluon is an automated machine learning framework designed to optimize model selection and hyperparameter tuning across tabular, text, image, and time series data. It functions as an ensemble learning library and a tabular data prediction engine, aiming to build high-accuracy predictive models without manual algorithm selection. The framework integrates multimodal machine learning pipelines that combine disparate data types into a single representation using specialized encoders. It also includes a probabilistic time series forecaster that fits multiple statistical and deep learning models

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a closed-loop system to programmatically rewrite training and architecture source code to discover optimal language model configurations. The system iteratively modifies code and evaluates performance metrics to improve model quality based on a target objective. It optimizes model performance and training efficiency by tracking validation bits per byte, which allows for a fair comparison of architectural changes independently of vocabulary size. The framework manages the full training

Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code. The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i

This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling. The tool is specifically designed for hyperparameter tuning in machine learning, where it maximizes model performance while minimizing the number of required training runs. It treats the target function as a black box, selecting optimal input parameters based on statistical priors to reduce manual trial and error. The system utilizes G

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

optimization routines for hyperparameter tuning

Collection of hyperparameter optimization benchmark problems

A hyperparameter optimization framework, inspired by Optuna.

This is a Python scientific computing library for finding the global maximum of expensive black-box functions. It operates as a global optimization framework that identifies optimal input parameters within defined bounds to maximize a target output. The library utilizes Gaussian process regression to predict function values and uncertainty, guiding the search for optimal parameters. It employs a surrogate-model optimization approach to approximate high-cost objective functions, reducing the total number of required evaluations. The system manages the trade-off between exploration and exploit

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

a distributed Hyperband implementation on Steroids

TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes machine learning algorithms and hyperparameters to identify the most effective model architecture. The system utilizes genetic programming to optimize these pipelines through evolutionary algorithms. To accelerate the search process, it functions as a multi-core evaluator that runs parallel training workflows across multiple processor cores. The framework supports the definition of custom objective functions to optimize pipelines based on specific performance metrics.

An open source python library for scalable Bayesian optimisation.

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

A toolset for black-box hyperparameter optimisation.

A simple OpenAI Gym environment for Neural Architecture Search (NAS)

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i

Spearmint Bayesian optimization codebase

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