The Best Optuna Alternatives in 2026

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

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

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

FEDOT automates machine learning model development through its modeling framework.

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

AutoKeras automates development of deep learning models through a machine learning library.

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

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

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

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

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.

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

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.

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

ParaMonte provides parallel Monte Carlo and machine learning tools across several programming languages.

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

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

Edward provides a library for probabilistic modeling, inference, and model criticism.

DeepSpeed optimizes distributed training and inference for deep learning models.

PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.