diff --git a/docs/source/examples/example_model.py b/docs/source/examples/example_model.py
index f608a02dbe..9f48e6f363 100644
--- a/docs/source/examples/example_model.py
+++ b/docs/source/examples/example_model.py
@@ -63,6 +63,7 @@ class ExampleModel(RootModule):
         :param data_batch:
         :return:
         """
+        pdb.set_trace()
         # forward pass
         x, y = data_batch
         x = x.view(x.size(0), -1)
@@ -80,6 +81,7 @@ class ExampleModel(RootModule):
         :param data_batch:
         :return:
         """
+        pdb.set_trace()
         x, y = data_batch
         x = x.view(x.size(0), -1)
         y_hat = self.forward(x)
diff --git a/pytorch_lightning/models/trainer.py b/pytorch_lightning/models/trainer.py
index 1ec1d47fd9..a9c7462b59 100644
--- a/pytorch_lightning/models/trainer.py
+++ b/pytorch_lightning/models/trainer.py
@@ -5,7 +5,7 @@ from pytorch_lightning.root_module.memory import get_gpu_memory_map
 import traceback
 from pytorch_lightning.root_module.model_saving import TrainerIO
 from torch.optim.lr_scheduler import MultiStepLR
-from torch.nn import DataParallel
+from pytorch_lightning.pt_overrides.override_data_parallel import LightningDataParallel
 import pdb
 
 try:
@@ -250,7 +250,7 @@ class Trainer(TrainerIO):
 
         # put on gpu if needed
         if self.on_gpu:
-            model = DataParallel(model, device_ids=self.data_parallel_device_ids)
+            model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)
 
         # run tiny validation to make sure program won't crash during val
         _ = self.validate(model, self.val_dataloader, max_batches=self.nb_sanity_val_steps)