From 8322f1b039c890b8ccdbfe29bf42056e5273d74f Mon Sep 17 00:00:00 2001
From: Jirka Borovec <Borda@users.noreply.github.com>
Date: Wed, 15 Apr 2020 17:14:29 +0200
Subject: [PATCH] neptune online (#1499)

---
 pytorch_lightning/loggers/neptune.py |  4 ++--
 tests/loggers/test_all.py            | 21 +++++++++++++--------
 2 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/pytorch_lightning/loggers/neptune.py b/pytorch_lightning/loggers/neptune.py
index aac9977bd5..d71f44cec2 100644
--- a/pytorch_lightning/loggers/neptune.py
+++ b/pytorch_lightning/loggers/neptune.py
@@ -33,7 +33,7 @@ class NeptuneLogger(LightningLoggerBase):
                  api_key: Optional[str] = None,
                  project_name: Optional[str] = None,
                  close_after_fit: Optional[bool] = True,
-                 offline_mode: bool = True,
+                 offline_mode: bool = False,
                  experiment_name: Optional[str] = None,
                  upload_source_files: Optional[List[str]] = None,
                  params: Optional[Dict[str, Any]] = None,
@@ -140,7 +140,7 @@ class NeptuneLogger(LightningLoggerBase):
                 "namespace/project_name" for example "tom/minst-classification".
                 If None, the value of NEPTUNE_PROJECT environment variable will be taken.
                 You need to create the project in https://neptune.ai first.
-            offline_mode: Optional default True. If offline_mode=True no logs will be send
+            offline_mode: Optional default False. If offline_mode=True no logs will be send
                 to neptune. Usually used for debug and test purposes.
             close_after_fit: Optional default True. If close_after_fit=False the experiment
                 will not be closed after training and additional metrics,
diff --git a/tests/loggers/test_all.py b/tests/loggers/test_all.py
index 0065b358e9..3ae86495e9 100644
--- a/tests/loggers/test_all.py
+++ b/tests/loggers/test_all.py
@@ -10,6 +10,15 @@ from pytorch_lightning.loggers import (
 from tests.base import LightningTestModel
 
 
+def _get_logger_args(logger_class, save_dir):
+    logger_args = {}
+    if 'save_dir' in inspect.getfullargspec(logger_class).args:
+        logger_args.update(save_dir=str(save_dir))
+    if 'offline_mode' in inspect.getfullargspec(logger_class).args:
+        logger_args.update(offline_mode=True)
+    return logger_args
+
+
 @pytest.mark.parametrize("logger_class", [
     TensorBoardLogger,
     CometLogger,
@@ -40,10 +49,8 @@ def test_loggers_fit_test(tmpdir, monkeypatch, logger_class):
             super().log_metrics(metrics, step)
             self.history.append((step, metrics))
 
-    if 'save_dir' in inspect.getfullargspec(logger_class).args:
-        logger = StoreHistoryLogger(save_dir=str(tmpdir))
-    else:
-        logger = StoreHistoryLogger()
+    logger_args = _get_logger_args(logger_class, tmpdir)
+    logger = StoreHistoryLogger(**logger_args)
 
     trainer = Trainer(
         max_epochs=1,
@@ -80,10 +87,8 @@ def test_loggers_pickle(tmpdir, monkeypatch, logger_class):
     import atexit
     monkeypatch.setattr(atexit, 'register', lambda _: None)
 
-    if 'save_dir' in inspect.getfullargspec(logger_class).args:
-        logger = logger_class(save_dir=str(tmpdir))
-    else:
-        logger = logger_class()
+    logger_args = _get_logger_args(logger_class, tmpdir)
+    logger = logger_class(**logger_args)
 
     trainer = Trainer(
         max_epochs=1,