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Decision brief
Optuna automates hyperparameter tuning in Python, integrating seamlessly with major ML frameworks.
Good fit when
- When you need to streamline the hyperparameter tuning process for machine learning models built in Python.
- For optimizing model performance where automation and a streamlined workflow are crucial.
Avoid when
- If your project is not compatible with Python, as Optuna does not support other languages directly out of box.
- Projects requiring manual control over every aspect of hyperparameter tuning might find Optuna too automated for their needs.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install optunaSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Optuna is an open-source Python library used for automating the hyperparameter optimization process in machine learning tasks.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 4, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
Optuna is available at [the Python Package Index](https://pypi.org/project/optuna/) and on [Anaconda Cloud](https:/Source link
Tags
README
Installation
Optuna is available at the Python Package Index and on Anaconda Cloud.
---
# Install AutoSampler dependencies (CPU only is sufficient for PyTorch)
$ pip install cmaes scipy torch --extra-index-url https://download.pytorch.org/whl/cpu
You can load registered module with optunahub.load_module.
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")
---
## License
MIT License (see [LICENSE](./LICENSE)).
Optuna uses the codes from SciPy and fdlibm projects (see [LICENSE_THIRD_PARTY](./LICENSE_THIRD_PARTY)).
For agents
This page has a .md twin and JSON over the API.