The Best hyperopt Alternatives in 2026

numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.

This metric-learn repository contains Python implementations of metric learning algorithms.

metric-learn provides Python implementations of metric learning algorithms.

Optuna automates hyperparameter optimization for machine learning models.

Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

This Scikit-learn extension accelerates compatible applications through optimized implementations.

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.

Use Learnergy, a Python library for training and working with energy-based machine learning models.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

Auto_ViML builds multiple machine learning models with a single line of code.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.

Guild AI automates and optimizes machine learning experiments while tracking each run.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.