This page contains a list of example codes written with Optuna.

The simplest codeblock looks like this:

import optuna


def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    return x ** 2


if __name__ == "__main__":
    study = optuna.create_study()
    # The optimization finishes after evaluating 1000 times or 3 seconds.
    study.optimize(objective, n_trials=1000, timeout=3)
    print(f"Best params is {study.best_params} with value {study.best_value}")

The examples below provide codeblocks similar to the example above for various different scenarios.

• Quadratic function
• Quadratic multi-objective function
• Quadratic function with constraints

• AllenNLP
• AllenNLP (Jsonnet)
• Catalyst
• CatBoost
• Chainer
• ChainerMN
• Dask-ML
• FastAI V1
• FastAI V2
• Haiku
• Keras
• LightGBM
• LightGBM Tuner
• PyTorch
• PyTorch Ignite
• PyTorch Lightning
• PyTorch Lightning (DDP)
• RAPIDS
• Scikit-learn
• Scikit-learn OptunaSearchCV
• Scikit-image
• SKORCH
• Tensorflow
• Tensorflow (eager)
• XGBoost

The following example demonstrates how to use Optuna Dashboard.

• Starting Optuna Dashboard with in-memory storage

The following example demonstrates how to implement an objective function that uses additional arguments other than trial.

• Scikit-learn (callable class version)

The following example demonstrates how to implement pruning logic with Optuna.

• Simple pruning (scikit-learn)

In addition, integration modules are available for the following libraries, providing simpler interfaces to utilize pruning.

• Pruning with Catalyst integration module
• Pruning with CatBoost integration module
• Pruning with Chainer integration module
• Pruning with ChainerMN integration module
• Pruning with FastAI V1 integration module
• Pruning with FastAI V2 integration module
• Pruning with Keras integration module
• Pruning with LightGBM integration module
• Pruning with PyTorch integration module
• Pruning with PyTorch Ignite integration module
• Pruning with PyTorch Lightning integration module
• Pruning with PyTorch Lightning integration module (DDP)
• Pruning with Tensorflow integration module
• Pruning with XGBoost integration module
• Pruning with XGBoost integration module (cross validation, XGBoost.cv)

• Warm Starting CMA-ES

• SimulatedAnnealingSampler

• Optuna Terminator
• OptunaSearchCV with Terminator

• Optimization with BoTorch
• Optimization of MLP with PyTorch

• Visualizing study
• Visualizing study with HiPlot

• Enqueuing trials with given parameters

• Tracking optimization process with aim

• Tracking optimization process with MLflow

• Tracking optimization process with Weights & Biases

• Optimization with Hydra

• Optimizing on a Dask cluster
• Optimizing on Kubernetes
• Optimizing with Ray's joblib backend

• Optimization of Hyperparameters for Stable-Baslines Agent

• Hugging Face Trainer's hyperparameter search
• Allegro Trains
• BBO-Rietveld: Automated crystal structure refinement
• Catalyst
• CuPy
• Hydra's Optuna Sweeper plugin
• Mozilla Voice STT
• neptune.ai
• OptGBM: A scikit-learn compatible LightGBM estimator with Optuna
• Optuna-distributed
• PyKEEN
• RL Baselines Zoo
• Hyperparameter Optimization for Machine Learning, code repository for online course

PRs to add additional projects welcome!

You can use our docker images with the tag ending with -dev to run most of the examples. For example, you can run PyTorch Simple via docker run --rm -v $(pwd):/prj -w /prj optuna/optuna:py3.7-dev python pytorch/pytorch_simple.py. Also, you can try our visualization example in Jupyter Notebook by opening localhost:8888 in your browser after executing this:

docker run -p 8888:8888 --rm optuna/optuna:py3.7-dev jupyter notebook --allow-root --no-browser --port 8888 --ip 0.0.0.0 --NotebookApp.token='' --NotebookApp.password=''