Metadata-Version: 2.1
Name: optuna-dashboard
Version: 0.9.0b4
Summary: Real-time dashboard for Optuna
Author-email: Masashi Shibata <mshibata@preferred.jp>
License: MIT License
Project-URL: Homepage, https://github.com/optuna/optuna-dashboard
Project-URL: Sources, https://github.com/optuna/optuna-dashboard
Project-URL: Bug Tracker, https://github.com/optuna/optuna-dashboard/issues
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# optuna-dashboard

![Software License](https://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat-square) [![PyPI - Downloads](https://img.shields.io/pypi/dm/optuna-dashboard)](https://pypistats.org/packages/optuna-dashboard)


Real-time dashboard for [Optuna](https://github.com/optuna/optuna).
Code files were originally taken from [Goptuna](https://github.com/c-bata/goptuna).

## Installation

You can install optuna-dashboard via [PyPI](https://pypi.org/project/optuna-dashboard/) or [Anaconda Cloud](https://anaconda.org/conda-forge/optuna-dashboard).

```
$ pip install optuna-dashboard
```

Also you can install following optional dependencies to make optuna-dashboard faster.

```console
$ pip install optuna-fast-fanova gunicorn
```

## Getting Started

First, please specify the storage URL to persistent your study using the [RDB backend](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/001_rdb.html).

```python
import optuna

def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])
    return x**2 + y

if __name__ == "__main__":
    study = optuna.create_study(
        storage="sqlite:///db.sqlite3",  # Specify the storage URL here.
        study_name="quadratic-simple"
    )
    study.optimize(objective, n_trials=100)
    print(f"Best value: {study.best_value} (params: {study.best_params})")
```

After running the above script, please execute the `optuna-dashboard` command with Optuna storage URL.

```
$ optuna-dashboard sqlite:///db.sqlite3
Listening on http://localhost:8080/
Hit Ctrl-C to quit.
```

<details>

<summary>More command line options</summary>

```console
$ optuna-dashboard -h
usage: optuna-dashboard [-h] [--port PORT] [--host HOST] [--version] [--quiet] storage

Real-time dashboard for Optuna.

positional arguments:
  storage        DB URL (e.g. sqlite:///example.db)

optional arguments:
  -h, --help            show this help message and exit
  --port PORT           port number (default: 8080)
  --host HOST           hostname (default: 127.0.0.1)
  --server {wsgiref,gunicorn}
                        server (default: auto)
  --artifact-dir ARTIFACT_DIR
                        directory to store artifact files
  --version, -v         show program's version number and exit
  --quiet, -q           quiet
```

</details>

<details>

<summary>Python Interface</summary>

**`run_server(storage: Union[str, BaseStorage], host: str = 'localhost', port: int = 8080) -> None`**

Start running optuna-dashboard and blocks until the server terminates.
This function uses wsgiref module which is not intended for the production use.

**`wsgi(storage: Union[str, BaseStorage]) -> WSGIApplication`**

This function exposes WSGI interface for people who want to run on the
production-class WSGI servers like Gunicorn or uWSGI.

**`save_note(study_or_trial: Union[Study, Trial], body: str) -> None`**

Save the note (Markdown format) to the Study or the Trial.

**`set_objective_names(study: Study, names: list[str]) -> None`**

Set the names of objectives.

</details>

## Using an official Docker image

You can also use [an official Docker image](https://github.com/optuna/optuna-dashboard/pkgs/container/optuna-dashboard) instead of setting up your Python environment.
The Docker image only supports SQLite3, MySQL(PyMySQL), and PostgreSQL(Psycopg2).

```
$ docker run -it --rm -p 8080:8080 -v `pwd`:/app -w /app \
> ghcr.io/optuna/optuna-dashboard sqlite:///db.sqlite3
```

<details>
<summary>MySQL (PyMySQL)</summary>

```
$ docker run -it --rm -p 8080:8080 ghcr.io/optuna/optuna-dashboard mysql+pymysql://username:password@hostname:3306/dbname
```

</details>

<details>
<summary>PostgreSQL (Psycopg2)</summary>

```
$ docker run -it --rm -p 8080:8080 ghcr.io/optuna/optuna-dashboard postgresql+psycopg2://username:password@hostname:5432/dbname
```

</details>


## Features

### Manage Studies

You can create and delete studies from Dashboard.

![optuna-dashboard-create-delete-study](https://user-images.githubusercontent.com/5564044/205545958-305f2354-c7cd-4687-be2f-9e46e7401838.gif)

### Visualize with Interactive Graphs & Rich Trials Data Grid

You can check the optimization history, hyperparameter importances, etc. in graphs and tables.

![optuna-dashboard-realtime-graph](https://user-images.githubusercontent.com/5564044/205545965-278cd7f4-da7d-4e2e-ac31-6d81b106cada.gif)

## Submitting patches

If you want to contribute, please check [Developers Guide](./CONTRIBUTING.md).
