optuna.integration

The integration module contains classes used to integrate Optuna with external machine learning frameworks.

Note

Optuna’s integration modules for third-party libraries have started migrating from Optuna itself to a package called optuna-integration. Please check the repository and the documentation.

For most of the ML frameworks supported by Optuna, the corresponding Optuna integration class serves only to implement a callback object and functions, compliant with the framework’s specific callback API, to be called with each intermediate step in the model training. The functionality implemented in these callbacks across the different ML frameworks includes:

1. Reporting intermediate model scores back to the Optuna trial using optuna.trial.Trial.report(),

2. According to the results of optuna.trial.Trial.should_prune(), pruning the current model by raising optuna.TrialPruned(), and

3. Reporting intermediate Optuna data such as the current trial number back to the framework, as done in MLflowCallback.

For scikit-learn, an integrated OptunaSearchCV estimator is available that combines scikit-learn BaseEstimator functionality with access to a class-level Study object.

Dependencies of each integration

We summarize the necessary dependencies for each integration.

Integration	Dependencies
AllenNLP	allennlp, torch, psutil, jsonnet
BoTorch	botorch, gpytorch, torch
CatBoost	catboost
ChainerMN	chainermn
Chainer	chainer
pycma	cma
Dask	distributed
FastAI	fastai
Keras	keras
LightGBMTuner	lightgbm, scikit-learn
LightGBMPruningCallback	lightgbm
MLflow	mlflow
MXNet	mxnet
PyTorch Distributed	torch
PyTorch (Ignite)	pytorch-ignite
PyTorch (Lightning)	pytorch-lightning
SHAP	scikit-learn, shap
Scikit-learn	pandas, scipy, scikit-learn
SKorch	skorch
TensorBoard	tensorboard, tensorflow
TensorFlow	tensorflow, tensorflow-estimator
TensorFlow + Keras	tensorflow
Weights & Biases	wandb
XGBoost	xgboost