optuna-dashboard 0.9.0

Real-time dashboard for Optuna

optuna-dashboard

Real-time dashboard for Optuna. Code files were originally taken from Goptuna.

Installation

You can install optuna-dashboard via PyPI or Anaconda Cloud.

$ pip install optuna-dashboard

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

$ pip install optuna-fast-fanova gunicorn

Getting Started

First, please specify the storage URL to persistent your study using the RDB backend.

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.

More command line options

$ 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

Python Interface

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.

Using an official Docker image

You can also use an official Docker image 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

MySQL (PyMySQL)

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

PostgreSQL (Psycopg2)

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

Features

Manage Studies

You can create and delete studies from Dashboard.

Visualize with Interactive Graphs & Rich Trials Data Grid

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

Submitting patches

If you want to contribute, please check Developers Guide.