optuna/optunahub
Python library to use and implement packages in OptunaHub
This tool helps machine learning engineers and researchers discover and apply advanced optimization algorithms to their models. It takes your existing Optuna studies, allowing you to easily experiment with various state-of-the-art samplers and pruners. The output is a more efficiently optimized model, finding better hyperparameters faster.
Used by 2 other packages. Available on PyPI.
Use this if you are a machine learning practitioner looking to improve the hyperparameter optimization of your models by leveraging a wider range of algorithms.
Not ideal if you are not already using Optuna for your black-box optimization tasks, as it is built on top of that framework.
Stars
55
Forks
14
Language
Python
License
MIT
Category
Last pushed
Mar 02, 2026
Commits (30d)
0
Dependencies
3
Reverse dependents
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/optuna/optunahub"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
optuna/optuna
A hyperparameter optimization framework
keras-team/keras-tuner
A Hyperparameter Tuning Library for Keras
KernelTuner/kernel_tuner
Kernel Tuner
syne-tune/syne-tune
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
deephyper/deephyper
DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning