Source code for optuna.integration.mlflow

import textwrap
from typing import Optional

import optuna
from optuna._experimental import experimental
from optuna._imports import try_import
from import StudyDirection
from optuna.trial import TrialState
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from typing import Dict  # NOQA

with try_import() as _imports:
    import mlflow

[docs]@experimental("1.4.0") class MLflowCallback(object): """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. .. testsetup:: import pathlib import tempfile tempdir = tempfile.mkdtemp() YOUR_TRACKING_URI = pathlib.Path(tempdir).as_uri() .. testcode:: 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]) .. testcleanup:: import shutil shutil.rmtree(tempdir) .. testoutput:: :hide: :options: +NORMALIZE_WHITESPACE INFO: 'my_study' does not exist. Creating a new experiment Args: 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. """
[docs] def __init__(self, tracking_uri: Optional[str] = None, metric_name: str = "value") -> None: _imports.check() self._tracking_uri = tracking_uri self._metric_name = metric_name
def __call__(self, study:, trial: optuna.trial.FrozenTrial) -> None: # This sets the tracking_uri for MLflow. if self._tracking_uri is not None: mlflow.set_tracking_uri(self._tracking_uri) # This sets the experiment of MLflow. mlflow.set_experiment(study.study_name) with mlflow.start_run(run_name=str(trial.number)): # This sets the metric for MLflow. trial_value = trial.value if trial.value is not None else float("nan") mlflow.log_metric(self._metric_name, trial_value) # This sets the params for MLflow. mlflow.log_params(trial.params) # This sets the tags for MLflow. tags = {} # type: Dict[str, str] tags["number"] = str(trial.number) tags["datetime_start"] = str(trial.datetime_start) tags["datetime_complete"] = str(trial.datetime_complete) # Set state and convert it to str and remove the common prefix. trial_state = trial.state if isinstance(trial_state, TrialState): tags["state"] = str(trial_state).split(".")[-1] # Set direction and convert it to str and remove the common prefix. study_direction = study.direction if isinstance(study_direction, StudyDirection): tags["direction"] = str(study_direction).split(".")[-1] tags.update(trial.user_attrs) distributions = { (k + "_distribution"): str(v) for (k, v) in trial.distributions.items() } tags.update(distributions) # This is a temporary fix on Optuna side. It avoids an error with user # attributes that are too long. It should be fixed on MLflow side later. # When it is fixed on MLflow side this codeblock can be removed. # see # see max_mlflow_tag_length = 5000 for key, value in tags.items(): value = str(value) # make sure it is a string if len(value) > max_mlflow_tag_length: tags[key] = textwrap.shorten(value, max_mlflow_tag_length) mlflow.set_tags(tags)