pytorch-lightning/tests/loggers/test_mlflow.py

import os
import pickle

import tests.base.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import MLFlowLogger
from tests.base import LightningTestModel


def test_mlflow_logger(tmpdir):
    """Verify that basic functionality of mlflow logger works."""
    tutils.reset_seed()

    hparams = tutils.get_default_hparams()
    model = LightningTestModel(hparams)

    mlflow_dir = os.path.join(tmpdir, 'mlruns')
    logger = MLFlowLogger('test', tracking_uri=f'file:{os.sep * 2}{mlflow_dir}')

    # Test already exists
    logger2 = MLFlowLogger('test', tracking_uri=f'file:{os.sep * 2}{mlflow_dir}')
    _ = logger2.run_id

    # Try logging string
    logger.log_metrics({'acc': 'test'})

    trainer_options = dict(
        default_save_path=tmpdir,
        max_epochs=1,
        train_percent_check=0.05,
        logger=logger
    )
    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    assert result == 1, 'Training failed'


def test_mlflow_pickle(tmpdir):
    """Verify that pickling trainer with mlflow logger works."""
    tutils.reset_seed()

    mlflow_dir = os.path.join(tmpdir, 'mlruns')
    logger = MLFlowLogger('test', tracking_uri=f'file:{os.sep * 2}{mlflow_dir}')
    trainer_options = dict(
        default_save_path=tmpdir,
        max_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})