diff --git a/pytorch_lightning/logging/test_tube_logger.py b/pytorch_lightning/logging/test_tube_logger.py
index 18270304..530f32ff 100644
--- a/pytorch_lightning/logging/test_tube_logger.py
+++ b/pytorch_lightning/logging/test_tube_logger.py
@@ -54,8 +54,10 @@ class TestTubeLogger(LightningLoggerBase):
         self.save()
         self.close()
 
+    @rank_zero_only
     def close(self):
-        self.experiment.close()
+        exp = self.experiment
+        exp.close()
 
     @property
     def rank(self):
diff --git a/tests/test_models.py b/tests/test_models.py
index 5c517a51..3b41501b 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -142,7 +142,6 @@ def test_dp_resume():
 
     # get logger
     logger = get_test_tube_logger(debug=False)
-    logger.log_hyperparams(hparams)
 
     # exp file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -218,8 +217,6 @@ def test_running_test_pretrained_model_ddp():
 
     # exp file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # exp file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -265,8 +262,6 @@ def test_running_test_after_fitting():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # logger file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -308,8 +303,6 @@ def test_running_test_without_val():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # logger file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -349,8 +342,6 @@ def test_running_test_pretrained_model():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # logger file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -396,8 +387,6 @@ def test_running_test_pretrained_model_dp():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # logger file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -648,8 +637,6 @@ def test_no_val_module():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     trainer_options = dict(
         max_nb_epochs=1,
@@ -695,8 +682,6 @@ def test_no_val_end_module():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     trainer_options = dict(
         max_nb_epochs=1,
@@ -831,8 +816,6 @@ def test_cpu_restore_training():
     # logger file to get meta
     test_logger_version = 10
     logger = get_test_tube_logger(False, version=test_logger_version)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     trainer_options = dict(
         max_nb_epochs=2,
@@ -924,8 +907,6 @@ def test_cpu_slurm_save_load():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     version = logger.version
 
@@ -962,8 +943,6 @@ def test_cpu_slurm_save_load():
 
     # new logger file to get meta
     logger = get_test_tube_logger(False, version=version)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     trainer_options = dict(
         max_nb_epochs=1,
@@ -1050,8 +1029,6 @@ def test_model_saving_loading():
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     trainer_options = dict(
         max_nb_epochs=1,
@@ -1136,8 +1113,6 @@ def test_amp_gpu_ddp_slurm_managed():
 
     # exp file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # exp file to get weights
     checkpoint = ModelCheckpoint(save_dir)
@@ -1358,7 +1333,6 @@ def test_ddp_sampler_error():
     model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
 
     logger = get_test_tube_logger(True)
-    logger.save()
 
     trainer = Trainer(
         logger=logger,
@@ -1483,8 +1457,6 @@ def run_gpu_model_test(trainer_options, model, hparams, on_gpu=True):
 
     # logger file to get meta
     logger = get_test_tube_logger(False)
-    logger.log_hyperparams(hparams)
-    logger.save()
 
     # logger file to get weights
     checkpoint = ModelCheckpoint(save_dir)