# optuna.integration.MLflowCallback¶

class optuna.integration.MLflowCallback(tracking_uri=None, metric_name='value', nest_trials=False, tag_study_user_attrs=False)[source]

Callback to track Optuna trials with MLflow.

This callback adds relevant information that is tracked by Optuna to MLflow. The MLflow experiment will be named after the Optuna study name.

Example

Add MLflow callback to Optuna optimization.

import optuna
from optuna.integration.mlflow import MLflowCallback

def objective(trial):
x = trial.suggest_uniform("x", -10, 10)
return (x - 2) ** 2

mlflc = MLflowCallback(
tracking_uri=YOUR_TRACKING_URI,
metric_name="my metric score",
)

study = optuna.create_study(study_name="my_study")
study.optimize(objective, n_trials=10, callbacks=[mlflc])

Parameters
• tracking_uri

The URI of the MLflow tracking server.

Please refer to mlflow.set_tracking_uri for more details.

• metric_name – Name of the metric. Since the metric itself is just a number, metric_name can be used to give it a name. So you know later if it was roc-auc or accuracy.

• nest_trials – Flag indicating whether or not trials should be logged as nested runs. This is often helpful for aggregating trials to a particular study, under a given experiment.

• tag_study_user_attrs – Flag indicating whether or not to add the study’s user attrs to the mlflow trial as tags. Please note that when this flag is set, key value pairs in study.user_attrs will supersede existing tags.

Note

Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.