import textwrap
from typing import Optional
import optuna
from optuna._experimental import experimental
from optuna._imports import try_import
from optuna.study 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
<https://www.mlflow.org/docs/latest/python_api/mlflow.html#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: optuna.study.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 https://github.com/optuna/optuna/issues/1340
# see https://github.com/mlflow/mlflow/issues/2931
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)