def test_mlflow_logger():
"""
verify that basic functionality of mlflow logger works
"""
reset_seed()
try:
from pytorch_lightning.logging import MLFlowLogger
except ModuleNotFoundError:
return
hparams = get_hparams()
model = LightningTestModel(hparams)
root_dir = os.path.dirname(os.path.realpath(__file__))
mlflow_dir = os.path.join(root_dir, "mlruns")
logger = MLFlowLogger("test", f"file://{mlflow_dir}")
logger.log_hyperparams(hparams)
logger.save()
trainer_options = dict(max_nb_epochs=1,
train_percent_check=0.01,
logger=logger)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1, "Training failed"
n = RANDOM_FILE_PATHS.pop()
shutil.move(mlflow_dir, mlflow_dir + f'_{n}')
def test_mlflow_pickle():
"""
verify that pickling trainer with mlflow logger works
"""
reset_seed()
try:
from pytorch_lightning.logging import MLFlowLogger
except ModuleNotFoundError:
return
hparams = get_hparams()
model = LightningTestModel(hparams)
root_dir = os.path.dirname(os.path.realpath(__file__))
mlflow_dir = os.path.join(root_dir, "mlruns")
logger = MLFlowLogger("test", f"file://{mlflow_dir}")
logger.log_hyperparams(hparams)
logger.save()
trainer_options = dict(max_nb_epochs=1, logger=logger)
trainer = Trainer(**trainer_options)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
n = RANDOM_FILE_PATHS.pop()
shutil.move(mlflow_dir, mlflow_dir + f'_{n}')