Source code for mlflow.entities.experiment

from mlflow.entities._mlflow_object import _MLflowObject
from mlflow.entities.experiment_tag import ExperimentTag
from mlflow.protos.service_pb2 import (
    Experiment as ProtoExperiment,
    ExperimentTag as ProtoExperimentTag,
)


[docs]class Experiment(_MLflowObject): """ Experiment object. """ DEFAULT_EXPERIMENT_NAME = "Default" def __init__(self, experiment_id, name, artifact_location, lifecycle_stage, tags=None): super().__init__() self._experiment_id = experiment_id self._name = name self._artifact_location = artifact_location self._lifecycle_stage = lifecycle_stage self._tags = {tag.key: tag.value for tag in (tags or [])} @property def experiment_id(self): """String ID of the experiment.""" return self._experiment_id @property def name(self): """String name of the experiment.""" return self._name def _set_name(self, new_name): self._name = new_name @property def artifact_location(self): """String corresponding to the root artifact URI for the experiment.""" return self._artifact_location @property def lifecycle_stage(self): """Lifecycle stage of the experiment. Can either be 'active' or 'deleted'.""" return self._lifecycle_stage @property def tags(self): """Tags that have been set on the experiment.""" return self._tags def _add_tag(self, tag): self._tags[tag.key] = tag.value
[docs] @classmethod def from_proto(cls, proto): experiment = cls( proto.experiment_id, proto.name, proto.artifact_location, proto.lifecycle_stage ) for proto_tag in proto.tags: experiment._add_tag(ExperimentTag.from_proto(proto_tag)) return experiment
[docs] def to_proto(self): experiment = ProtoExperiment() experiment.experiment_id = self.experiment_id experiment.name = self.name experiment.artifact_location = self.artifact_location experiment.lifecycle_stage = self.lifecycle_stage experiment.tags.extend( [ProtoExperimentTag(key=key, value=val) for key, val in self._tags.items()] ) return experiment