fix Logger tests for Win (#605)

* fix mlflow test

* fix mlflow test

* update logger / mlflow

* flake8

* fix appveyor

appveyor.yml

@@ -59,7 +59,7 @@ before_test:
 
 # to run your custom scripts instead of automatic tests
 test_script:
-  - py.test pytorch_lightning tests pl_examples -v --doctest-modules --flake8
+  - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules --flake8
   #- python setup.py sdist
   #- twine check dist/*
 

pytorch_lightning/logging/base.py

@@ -1,8 +1,9 @@
+from abc import ABC
 from functools import wraps
 
 
 def rank_zero_only(fn):
-    """Decorate a logger method to run it only on the process with rank 0
+    """Decorate a logger method to run it only on the process with rank 0.
 
     :param fn: Function to decorate
     """
@@ -15,12 +16,16 @@ def rank_zero_only(fn):
     return wrapped_fn
 
 
-class LightningLoggerBase(object):
-    """Base class for experiment loggers"""
+class LightningLoggerBase(ABC):
+    """Base class for experiment loggers."""
 
     def __init__(self):
         self._rank = 0
 
+    @property
+    def experiment(self):
+        raise NotImplementedError()
+
     def log_metrics(self, metrics, step):
         """Record metrics.
 
@@ -30,46 +35,43 @@ class LightningLoggerBase(object):
         raise NotImplementedError()
 
     def log_hyperparams(self, params):
-        """Record hyperparameters
+        """Record hyperparameters.
 
         :param params: argparse.Namespace containing the hyperparameters
         """
         raise NotImplementedError()
 
     def save(self):
-        """Save log data"""
+        """Save log data."""
         pass
 
     def finalize(self, status):
-        """Do any processing that is necessary to finalize an experiment
+        """Do any processing that is necessary to finalize an experiment.
 
         :param status: Status that the experiment finished with (e.g. success, failed, aborted)
         """
         pass
 
     def close(self):
-        """Do any cleanup that is necessary to close an experiment"""
+        """Do any cleanup that is necessary to close an experiment."""
         pass
 
     @property
     def rank(self):
-        """
-        Process rank. In general, metrics should only be logged by the process
-        with rank 0
-        """
+        """Process rank. In general, metrics should only be logged by the process with rank 0."""
         return self._rank
 
     @rank.setter
     def rank(self, value):
-        """Set the process rank"""
+        """Set the process rank."""
         self._rank = value
 
     @property
     def name(self):
-        """Return the experiment name"""
+        """Return the experiment name."""
         raise NotImplementedError("Sub-classes must provide a name property")
 
     @property
     def version(self):
-        """Return the experiment version"""
+        """Return the experiment version."""
         raise NotImplementedError("Sub-classes must provide a version property")

pytorch_lightning/logging/comet.py

@@ -67,8 +67,8 @@ logger = getLogger(__name__)
 class CometLogger(LightningLoggerBase):
     def __init__(self, api_key=None, save_dir=None, workspace=None,
                  rest_api_key=None, project_name=None, experiment_name=None, **kwargs):
-        """
-        Initialize a Comet.ml logger. Requires either an API Key (online mode) or a local directory path (offline mode)
+        """Initialize a Comet.ml logger.
+        Requires either an API Key (online mode) or a local directory path (offline mode)
 
         :param str api_key: Required in online mode. API key, found on Comet.ml
         :param str save_dir: Required in offline mode. The path for the directory to save local comet logs

pytorch_lightning/logging/mlflow.py

@@ -40,25 +40,29 @@ logger = getLogger(__name__)
 class MLFlowLogger(LightningLoggerBase):
     def __init__(self, experiment_name, tracking_uri=None, tags=None):
         super().__init__()
-        self.experiment = mlflow.tracking.MlflowClient(tracking_uri)
+        self._mlflow_client = mlflow.tracking.MlflowClient(tracking_uri)
         self.experiment_name = experiment_name
         self._run_id = None
         self.tags = tags
 
+    @property
+    def experiment(self):
+        return self._mlflow_client
+
     @property
     def run_id(self):
         if self._run_id is not None:
             return self._run_id
 
-        experiment = self.experiment.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.experiment.create_experiment(self.experiment_name)
-            experiment = self.experiment.get_experiment_by_name(self.experiment_name)
+        expt = self._mlflow_client.get_experiment_by_name(self.experiment_name)
 
-        run = self.experiment.create_run(experiment.experiment_id, tags=self.tags)
+        if expt:
+            self._expt_id = expt.experiment_id
+        else:
+            logger.warning(f"Experiment with name f{self.experiment_name} not found. Creating it.")
+            self._expt_id = self._mlflow_client.create_experiment(name=self.experiment_name)
+
+        run = self._mlflow_client.create_run(experiment_id=self._expt_id, tags=self.tags)
         self._run_id = run.info.run_id
         return self._run_id
 

tests/test_logging.py

@@ -63,15 +63,13 @@ def test_mlflow_logger(tmpdir):
     model = LightningTestModel(hparams)
 
     mlflow_dir = os.path.join(tmpdir, "mlruns")
-
-    logger = MLFlowLogger("test", f"file://{mlflow_dir}")
+    logger = MLFlowLogger("test", tracking_uri=f"file:{os.sep * 2}{mlflow_dir}")
 
     trainer_options = dict(
         max_epochs=1,
         train_percent_check=0.01,
         logger=logger
     )
-
     trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
 
@@ -88,13 +86,11 @@ def test_mlflow_pickle(tmpdir):
     except ModuleNotFoundError:
         return
 
-    hparams = tutils.get_hparams()
-    model = LightningTestModel(hparams)
+    # hparams = tutils.get_hparams()
+    # model = LightningTestModel(hparams)
 
     mlflow_dir = os.path.join(tmpdir, "mlruns")
-
-    logger = MLFlowLogger("test", f"file://{mlflow_dir}")
-
+    logger = MLFlowLogger("test", tracking_uri=f"file:{os.sep * 2}{mlflow_dir}")
     trainer_options = dict(
         max_epochs=1,
         logger=logger