OptunaHub: Feature-sharing platform for Optuna
🔗 OptunaHub | 📃 Docs | 📖 Tutorials | ❓ FAQ | Optuna.org
OptunaHub is a platform for sharing algorithms in Optuna, a powerful and flexible hyperparameter optimization framework. OptunaHub provides implementations of state-of-the-art optimization algorithms and visualization of optimization results for analysis. You can also publish your algorithm implementation on the platform and make it available to Optuna users around the world.
This is the repository of the optunahub Python Library to use packages published in OptunaHub. If you would like to register your package in OptunaHub, please contribute by creating a pull request to the optunahub-registry repository.
📢 News
- Jan 22, 2025: A new article OptunaHub Benchmarks: A New Feature to Use/Register Various Benchmark Problems has been published.
- Nov 6, 2024: A new article AutoSampler: Automatic Selection of Optimization Algorithms in Optuna has been published.
- Oct 21, 2024: We posted an article introducing MOEA/D and an example comparison with other optimization methods.
- Aug 30, 2024: New Medium article "OptunaHub, a Feature-Sharing Platform for Optuna, Now Available in Official Release!" was published!
- Jul 16, 2024: We posted an article Announcing OptunaHub 0.1.0-β that describes what is, how to use, and how to contribute to OptunaHub.
Installation
OptunaHub is available at the Python Package Index.
pip install optunahub
Example
You only need to search for the desired function on the OptunaHub website and call the optunahub.load_module function in your code to incorporate it.
import optuna
import optunahub
def objective(trial: optuna.Trial) -> float:
x = trial.suggest_float("x", -5, 5)
y = trial.suggest_float("y", -5, 5)
return x**2 + y**2
module = optunahub.load_module(package="samplers/auto_sampler")
study = optuna.create_study(sampler=module.AutoSampler())
study.optimize(objective, n_trials=10)
print(study.best_trial.value, study.best_trial.params)
Contribution
Any contributions to OptunaHub are more than welcome!
OptunaHub is composed of the following three related repositories. Please contribute to the appropriate repository for your purposes.
- optunahub (this repository)
- The python library to use OptunaHub. If you find issues and/or bugs in the optunahub library, please report it here via Github issues.
- optunahub-registry
- The registry of the OptunaHub packages. If you are interested in registering your package with OptunaHub, please contribute to this repository. For general guidelines on how to contribute to the repository, take a look at CONTRIBUTING.md.
- optunahub-web
- The web frontend for OptunaHub. If you find issues and/or bugs on the website, please report it here via GitHub issues.
License
MIT License (see LICENSE).
Metadata
Release files for optunahub 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| optunahub-0.3.0.tar.gz | 13.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| optunahub-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.3 kB
Release files / optunahub-0.3.0.tar.gz
| Download URL | optunahub-0.3.0.tar.gz |
|---|---|
| Size | 13.9 kB |
| Tags | Source |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
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Release files / optunahub-0.3.0-py3-none-any.whl
| Download URL | optunahub-0.3.0-py3-none-any.whl |
|---|---|
| Size | 12.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|