lightning/pytorch_lightning/logging/mlflow_logger.py

57 lines
1.7 KiB
Python

from time import time
from logging import getLogger
import mlflow
from .base import LightningLoggerBase, rank_zero_only
logger = getLogger(__name__)
class MLFlowLogger(LightningLoggerBase):
def __init__(self, experiment_name, tracking_uri=None):
super().__init__()
self.client = mlflow.tracking.MlflowClient(tracking_uri)
self.experiment_name = experiment_name
self._run_id = None
@property
def run_id(self):
if self._run_id is not None:
return self._run_id
experiment = self.client.get_experiment_by_name(self.experiment_name)
if experiment is None:
logger.warning(
f"Experiment with name f{self.experiment_name} not found. Creating it."
)
self.client.create_experiment(self.experiment_name)
experiment = self.client.get_experiment_by_name(self.experiment_name)
run = self.client.create_run(experiment.experiment_id)
self._run_id = run.info.run_id
return self._run_id
@rank_zero_only
def log_hyperparams(self, params):
for k, v in vars(params).items():
self.client.log_param(self.run_id, k, v)
@rank_zero_only
def log_metrics(self, metrics, step_num=None):
timestamp_ms = int(time() * 1000)
for k, v in metrics.items():
if isinstance(v, str):
logger.warning(
f"Discarding metric with string value {k}={v}"
)
continue
self.client.log_metric(self.run_id, k, v, timestamp_ms, step_num)
def save(self):
pass
@rank_zero_only
def finalize(self, status="FINISHED"):
self.client.set_terminated(self.run_id, status)