diff --git a/docs/source/tpu.rst b/docs/source/tpu.rst
index 7a7ece8cd7..119ec34f24 100644
--- a/docs/source/tpu.rst
+++ b/docs/source/tpu.rst
@@ -101,7 +101,6 @@ train_dataloader (and val, train) code as follows.
 
     import torch_xla.core.xla_model as xm
 
-    @pl.data_loader
     def train_dataloader(self):
         dataset = MNIST(
             os.getcwd(),
diff --git a/pl_examples/domain_templates/gan.py b/pl_examples/domain_templates/gan.py
index 864ace68b9..6214dc03bb 100644
--- a/pl_examples/domain_templates/gan.py
+++ b/pl_examples/domain_templates/gan.py
@@ -167,7 +167,6 @@ class GAN(LightningModule):
         opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
         return [opt_g, opt_d], []
 
-    @data_loader
     def train_dataloader(self):
         transform = transforms.Compose([transforms.ToTensor(),
                                         transforms.Normalize([0.5], [0.5])])
diff --git a/pl_examples/full_examples/imagenet/imagenet_example.py b/pl_examples/full_examples/imagenet/imagenet_example.py
index ae9e07198e..e82a696db5 100644
--- a/pl_examples/full_examples/imagenet/imagenet_example.py
+++ b/pl_examples/full_examples/imagenet/imagenet_example.py
@@ -20,7 +20,6 @@ import torchvision.transforms as transforms
 
 import pytorch_lightning as pl
 from pytorch_lightning.core import LightningModule
-from pytorch_lightning.core import data_loader
 
 # pull out resnet names from torchvision models
 MODEL_NAMES = sorted(
@@ -132,7 +131,6 @@ class ImageNetLightningModel(LightningModule):
         scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
         return [optimizer], [scheduler]
 
-    @data_loader
     def train_dataloader(self):
         normalize = transforms.Normalize(
             mean=[0.485, 0.456, 0.406],
@@ -163,7 +161,6 @@ class ImageNetLightningModel(LightningModule):
         )
         return train_loader
 
-    @data_loader
     def val_dataloader(self):
         normalize = transforms.Normalize(
             mean=[0.485, 0.456, 0.406],
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 97852ce647..2fc83e5d40 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -11,7 +11,6 @@ import torch
 import torch.distributed as dist
 from torch.optim import Adam
 
-from pytorch_lightning.core.decorators import data_loader
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
 from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
@@ -1139,7 +1138,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
         """
         return None
 
-    @data_loader
     def tng_dataloader(self):  # todo: remove in v1.0.0
         """Implement a PyTorch DataLoader.
 
@@ -1239,7 +1237,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
             .. code-block:: python
 
-                @pl.data_loader
                 def val_dataloader(self):
                     transform = transforms.Compose([transforms.ToTensor(),
                                 transforms.Normalize((0.5,), (1.0,))])
@@ -1254,7 +1251,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                     return loader
 
                 # can also return multiple dataloaders
-                @pl.data_loader
                 def val_dataloader(self):
                     return [loader_a, loader_b, ..., loader_n]
 
diff --git a/tests/models/debug.py b/tests/models/debug.py
index 0154daf61a..3c200a52f2 100644
--- a/tests/models/debug.py
+++ b/tests/models/debug.py
@@ -41,14 +41,11 @@ class CoolModel(pl.LightningModule):
     def configure_optimizers(self):
         return [torch.optim.Adam(self.parameters(), lr=0.02)]
 
-    @pl.data_loader
     def train_dataloader(self):
         return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
 
-    @pl.data_loader
     def val_dataloader(self):
         return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
 
-    @pl.data_loader
     def test_dataloader(self):
         return DataLoader(MNIST('path/to/save', train=False), batch_size=32)