diff --git a/.run_local_tests.sh b/.run_local_tests.sh
index 06d491be8f..23e96865ec 100644
--- a/.run_local_tests.sh
+++ b/.run_local_tests.sh
@@ -1,3 +1,3 @@
 rm -rf tests/save_dir*
-rm -rf tests/mlruns_9964541/mlruns/
+rm -rf tests/mlruns_*
 coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
diff --git a/pytorch_lightning/root_module/memory.py b/pytorch_lightning/root_module/memory.py
index 73cea905c1..818e96432d 100644
--- a/pytorch_lightning/root_module/memory.py
+++ b/pytorch_lightning/root_module/memory.py
@@ -35,7 +35,7 @@ class ModelSummary(object):
         out_sizes = []
         input_ = self.model.example_input_array
 
-        if self.model.use_ddp:
+        if self.model.use_ddp or self.model.use_dp:
             input_ = input_.cuda(0)
 
         if self.model.trainer.use_amp:
diff --git a/pytorch_lightning/root_module/root_module.py b/pytorch_lightning/root_module/root_module.py
index 0e0259635d..844a9e5dc8 100644
--- a/pytorch_lightning/root_module/root_module.py
+++ b/pytorch_lightning/root_module/root_module.py
@@ -130,17 +130,16 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
         return None
 
     @classmethod
-    def load_from_metrics(cls, weights_path, tags_csv, on_gpu):
+    def load_from_metrics(cls, weights_path, tags_csv):
         """
         Primary way of loading model from csv weights path
         :param weights_path:
         :param tags_csv:
-        :param on_gpu:
         :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
         :return:
         """
         hparams = load_hparams_from_tags_csv(tags_csv)
-        hparams.__setattr__('on_gpu', on_gpu)
+        hparams.__setattr__('on_gpu', False)
 
         # load on CPU only to avoid OOM issues
         # then its up to user to put back on GPUs
diff --git a/tests/test_models.py b/tests/test_models.py
index d5bf3b6e9b..b7a2201d49 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -128,8 +128,8 @@ def test_dp_resume():
         dp_model = new_trainer.model
         dp_model.eval()
 
-        for dataloader in trainer.get_train_dataloader():
-            run_prediction(dataloader, dp_model, dp=True)
+        dataloader = trainer.get_train_dataloader()
+        run_prediction(dataloader, dp_model, dp=True)
 
     # new model
     model = LightningTestModel(hparams)
@@ -593,7 +593,7 @@ def test_no_val_module():
     tags_path = logger.experiment.get_data_path(logger.experiment.name, logger.experiment.version)
     tags_path = os.path.join(tags_path, 'meta_tags.csv')
     model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
-                                                   tags_csv=tags_path, on_gpu=False)
+                                                   tags_csv=tags_path)
     model_2.eval()
 
     # make prediction
@@ -1448,8 +1448,8 @@ def load_model(exp, save_dir, on_gpu, module_class=LightningTemplateModel):
     weights_dir = os.path.join(save_dir, checkpoints[0])
 
     trained_model = module_class.load_from_metrics(weights_path=weights_dir,
-                                                   tags_csv=tags_path,
-                                                   on_gpu=on_gpu)
+                                                   tags_csv=tags_path
+                                                   )
 
     assert trained_model is not None, 'loading model failed'