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A hyperparameter optimization framework

Project description

Optuna: A hyperparameter optimization framework

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:link: Website | :page_with_curl: Docs | :gear: Install Guide | :pencil: Tutorial | :bulb: Examples

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.

:fire: Key Features

Optuna has modern functionalities as follows:

Basic Concepts

We use the terms study and trial as follows:

  • Study: optimization based on an objective function
  • Trial: a single execution of the objective function

Please refer to the sample code below. The goal of a study is to find out the optimal set of hyperparameter values (e.g., regressor and svr_c) through multiple trials (e.g., n_trials=100). Optuna is a framework designed for automation and acceleration of optimization studies.

Sample code with scikit-learn

Open in Colab

import ...

# Define an objective function to be minimized.
def objective(trial):

    # Invoke suggest methods of a Trial object to generate hyperparameters.
    regressor_name = trial.suggest_categorical('regressor', ['SVR', 'RandomForest'])
    if regressor_name == 'SVR':
        svr_c = trial.suggest_float('svr_c', 1e-10, 1e10, log=True)
        regressor_obj = sklearn.svm.SVR(C=svr_c)
        rf_max_depth = trial.suggest_int('rf_max_depth', 2, 32)
        regressor_obj = sklearn.ensemble.RandomForestRegressor(max_depth=rf_max_depth)

    X, y = sklearn.datasets.fetch_california_housing(return_X_y=True)
    X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0), y_train)
    y_pred = regressor_obj.predict(X_val)

    error = sklearn.metrics.mean_squared_error(y_val, y_pred)

    return error  # An objective value linked with the Trial object.

study = optuna.create_study()  # Create a new study.
study.optimize(objective, n_trials=100)  # Invoke optimization of the objective function.

[!NOTE] More examples can be found in optuna/optuna-examples.

The examples cover diverse problem setups such as multi-objective optimization, constrained optimization, pruning, and distributed optimization.


Optuna is available at the Python Package Index and on Anaconda Cloud.

# PyPI
$ pip install optuna
# Anaconda Cloud
$ conda install -c conda-forge optuna

[!IMPORTANT] Optuna supports Python 3.7 or newer.

Also, we provide Optuna docker images on DockerHub.


Optuna has integration features with various third-party libraries. Integrations can be found in optuna/optuna-integration and the document is available here.

Supported integration libraries

Web Dashboard

Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importance, etc. in graphs and tables. You don't need to create a Python script to call Optuna's visualization functions. Feature requests and bug reports are welcome!


optuna-dashboard can be installed via pip:

$ pip install optuna-dashboard

[!TIP] Please check out the convenience of Optuna Dashboard using the sample code below.

Sample code to launch Optuna Dashboard

Save the following code as

import optuna

def objective(trial):
    x1 = trial.suggest_float("x1", -100, 100)
    x2 = trial.suggest_float("x2", -100, 100)
    return x1 ** 2 + 0.01 * x2 ** 2

study = optuna.create_study(storage="sqlite:///db.sqlite3")  # Create a new study with database.
study.optimize(objective, n_trials=100)

Then try the commands below:

# Run the study specified above
$ python

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



Any contributions to Optuna are more than welcome!

If you are new to Optuna, please check the good first issues. They are relatively simple, well-defined, and often good starting points for you to get familiar with the contribution workflow and other developers.

If you already have contributed to Optuna, we recommend the other contribution-welcome issues.

For general guidelines on how to contribute to the project, take a look at


If you use Optuna in one of your research projects, please cite our KDD paper "Optuna: A Next-generation Hyperparameter Optimization Framework":

  title={{O}ptuna: A Next-Generation Hyperparameter Optimization Framework},
  author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
  booktitle={The 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},

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