xadrianzetx/optuna-distributed
Distributed hyperparameter optimization made easy
This tool helps machine learning engineers and data scientists efficiently find the best settings (hyperparameters) for their models. You provide your model's training code and the ranges for its settings, and the tool outputs the optimal hyperparameter combination. It's designed for anyone regularly tuning complex machine learning models.
No commits in the last 6 months. Available on PyPI.
Use this if you need to speed up hyperparameter optimization for your machine learning models by running many trials in parallel, either on your local machine or across a cluster.
Not ideal if you require advanced Optuna features like callbacks, specific integration modules, or need to run local asynchronous optimization on a Windows machine, as these are not fully supported yet.
Stars
38
Forks
1
Language
Python
License
MIT
Category
Last pushed
Jun 11, 2024
Commits (30d)
0
Dependencies
3
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/xadrianzetx/optuna-distributed"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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