mirror of
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151 lines
4.4 KiB
Markdown
151 lines
4.4 KiB
Markdown
# optuna-dashboard
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 [](https://pypistats.org/packages/optuna-dashboard)
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Real-time dashboard for [Optuna](https://github.com/optuna/optuna).
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Code files were originally taken from [Goptuna](https://github.com/c-bata/goptuna).
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## Installation
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You can install optuna-dashboard via [PyPI](https://pypi.org/project/optuna-dashboard/) or [Anaconda Cloud](https://anaconda.org/conda-forge/optuna-dashboard).
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```
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$ pip install optuna-dashboard
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```
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Also you can install following optional dependencies to make optuna-dashboard faster.
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```console
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$ pip install optuna-fast-fanova gunicorn
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```
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## Getting Started
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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).
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```python
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import optuna
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def objective(trial):
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x = trial.suggest_float("x", -100, 100)
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y = trial.suggest_categorical("y", [-1, 0, 1])
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return x**2 + y
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if __name__ == "__main__":
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study = optuna.create_study(
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storage="sqlite:///db.sqlite3", # Specify the storage URL here.
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study_name="quadratic-simple"
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)
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study.optimize(objective, n_trials=100)
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print(f"Best value: {study.best_value} (params: {study.best_params})")
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```
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After running the above script, please execute the `optuna-dashboard` command with Optuna storage URL.
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```
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$ optuna-dashboard sqlite:///db.sqlite3
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Listening on http://localhost:8080/
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Hit Ctrl-C to quit.
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```
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<details>
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<summary>More command line options</summary>
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```console
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$ optuna-dashboard -h
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usage: optuna-dashboard [-h] [--port PORT] [--host HOST] [--version] [--quiet] storage
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Real-time dashboard for Optuna.
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positional arguments:
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storage DB URL (e.g. sqlite:///example.db)
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optional arguments:
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-h, --help show this help message and exit
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--port PORT port number (default: 8080)
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--host HOST hostname (default: 127.0.0.1)
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--server {wsgiref,gunicorn}
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server (default: auto)
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--version, -v show program's version number and exit
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--quiet, -q quiet
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```
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</details>
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<details>
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<summary>Python Interface</summary>
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**`run_server(storage: Union[str, BaseStorage], host: str = 'localhost', port: int = 8080) -> None`**
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Start running optuna-dashboard and blocks until the server terminates.
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This function uses wsgiref module which is not intended for the production use.
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**`wsgi(storage: Union[str, BaseStorage]) -> WSGIApplication`**
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This function exposes WSGI interface for people who want to run on the
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production-class WSGI servers like Gunicorn or uWSGI.
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**`save_study_note(study: Study, body: str) -> None`**
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Save the note (Markdown format) to the Study.
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**`save_trial_note(trial: Trial, body: str) -> None`**
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Save the note (Markdown format) to the Trial.
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**`set_objective_names(study: Study, names: list[str]) -> None`**
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Set the names of objectives.
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</details>
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## Using an official Docker image
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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.
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The Docker image only supports SQLite3, MySQL(PyMySQL), and PostgreSQL(Psycopg2).
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```
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$ docker run -it --rm -p 8080:8080 -v `pwd`:/app -w /app \
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> ghcr.io/optuna/optuna-dashboard sqlite:///db.sqlite3
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```
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<details>
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<summary>MySQL (PyMySQL)</summary>
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```
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$ docker run -it --rm -p 8080:8080 ghcr.io/optuna/optuna-dashboard mysql+pymysql://username:password@hostname:3306/dbname
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```
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</details>
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<details>
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<summary>PostgreSQL (Psycopg2)</summary>
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```
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$ docker run -it --rm -p 8080:8080 ghcr.io/optuna/optuna-dashboard postgresql+psycopg2://username:password@hostname:5432/dbname
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```
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</details>
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## Features
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### Manage Studies
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You can create and delete studies from Dashboard.
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### Visualize with Interactive Graphs & Rich Trials Data Grid
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You can check the optimization history, hyperparameter importances, etc. in graphs and tables.
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## Submitting patches
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If you want to contribute, please check [Developers Guide](./CONTRIBUTING.md).
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