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

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}")

Examples for Diverse Problem Setups

Here are the URLs to the example codeblocks to the corresponding setups.

• Quadratic Function
• Quadratic Multi-Objective Function
• Quadratic Function with Constraints

• Optimization with BoTorch
• Optimization of Multi-Layer Perceptron with PyTorch

• Catalyst
• CatBoost
• Dask-ML
• FastAI
• Haiku
• Keras
• LightGBM
• LightGBM Tuner
• PyTorch
• PyTorch Ignite
• PyTorch Lightning
• PyTorch Lightning (DDP)
• RAPIDS
• Scikit-learn
• Scikit-learn OptunaSearchCV
• Scikit-image
• SKORCH
• Hugging Face Transformers
• Tensorflow
• Tensorflow (eager)
• XGBoost

If you are looking for an example of reinforcement learning, please take a look at the following:

• Optimization of Hyperparameters for Stable-Baslines Agent

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 FastAI 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 Version)

• Warm Starting CMA-ES

If you are interested in defining a user-defined sampler, here is an example:

• SimulatedAnnealingSampler

• Optuna Terminator
• OptunaSearchCV with Terminator

• Visualizing Study
• Visualizing Study with HiPlot

• Optimizing on Dask Cluster
• Optimizing on Kubernetes
• Optimizing with Ray's Joblib Backend

• Tracking Optimization Process with aim
• Optimization with Hydra

• 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
• Property-guided molecular optimization using MolMIM with CMA-ES

Running with Optuna's Docker images?

Our Docker images for most examples are available with the tag ending with -dev. For example, PyTorch Simple can be run via:

$ docker run --rm -v $(pwd):/prj -w /prj optuna/optuna:py3.11-dev python pytorch/pytorch_simple.py

Additionally, our visualization example can also be run on Jupyter Notebook by opening localhost:8888 in your browser after executing the following:

$ docker run -p 8888:8888 --rm optuna/optuna:py3.11-dev jupyter notebook --allow-root --no-browser --port 8888 --ip 0.0.0.0 --NotebookApp.token='' --NotebookApp.password=''