Optuna Examples

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

Simple Black-box Optimization

• Quadratic function

Examples with ML Libraries

• Scikit-learn
• Scikit-image
• Chainer
• ChainerMN
• Dask-ML
• LightGBM
• LightGBM Tuner
• CatBoost
• MXNet
• PyTorch
• PyTorch Ignite
• PyTorch Lightning
• XGBoost
• Tensorflow
• Tensorflow (eager)
• Keras
• FastAI
• AllenNLP
• AllenNLP (Jsonnet)

An example where an objective function uses additional arguments

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

• Scikit-learn (callable class version)

Examples of Pruning

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 Chainer integration module
• Pruning with XGBoost integration module
• Pruning with XGBoost integration module (cross validation, XGBoost.cv)
• Pruning with LightGBM integration module
• Pruning with ChainerMN integration module
• Pruning with Tensorflow integration module
• Pruning with Keras integration module
• Pruning with MXNet integration module
• Pruning with PyTorch Ignite integration module
• Pruning with PyTorch Lightning integration module
• Pruning with FastAI integration module

Examples of User-Defined Sampler

• SimulatedAnnealingSampler

Examples of Visualization

• Visualizing study

Examples of MLflow

• Tracking optimization process with MLflow

Examples of Distributed Optimization

• Optimizing on Kubernetes

External libraries using Optuna

• Analyzing optimization results using Neptune
• OptGBM: A scikit-learn compatible LightGBM estimator with Optuna

Running with Optuna's Docker images?

You can use our docker images with the tag ending with -dev to run most of the examples. For example, you can run PyTorch Lightning Simple via docker run --rm optuna/optuna:py3.7-dev python examples/pytorch_lightning_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=''