diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4015fc1829..6bfcbaff45 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -24,6 +24,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Does not interfere with a default sampler ([#1318](https://github.com/PyTorchLightning/pytorch-lightning/pull/1318))
 - Enhanced load_from_checkpoint to also forward params to the model ([#1307](https://github.com/PyTorchLightning/pytorch-lightning/pull/1307))
 - Made `evalaute` method private >> `Trainer._evaluate(...)`. ([#1260](https://github.com/PyTorchLightning/pytorch-lightning/pull/1260))
 
diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
index 83b59d21a7..7715d5e270 100644
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -74,6 +74,15 @@ class TrainerDataLoadingMixin(ABC):
             raise ValueError(msg)
 
     def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
+
+        # don't do anything if it's not a dataloader
+        if not isinstance(dataloader, DataLoader):
+            return dataloader
+
+        # don't add sampler when user gives one
+        if dataloader.sampler is not None:
+            return dataloader
+
         if self.use_ddp or self.use_ddp2 or self.use_tpu:
             dl_args = {
                 'dataset': dataloader.dataset,
diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
index 57e460feb6..f48b919bc2 100644
--- a/pytorch_lightning/trainer/distrib_parts.py
+++ b/pytorch_lightning/trainer/distrib_parts.py
@@ -250,8 +250,11 @@ You must configure your job submission script correctly for the trainer to work.
 .. note:: When running in DDP mode, any errors in your code will show up as an NCCL issue.
  Set the `NCCL_DEBUG=INFO` flag to see the ACTUAL error.
 
-Finally, make sure to add a distributed sampler to your dataset. The distributed sampler copies a
- portion of your dataset onto each GPU. (World_size = gpus_per_node * nb_nodes).
+Normally now you would need to add a distributed sampler to your dataset, however
+Lightning automates this for you. But if you still need to set a sampler Lightning will
+not interfere nor automate it.
+
+Here's an example of how to add your own sampler (again no need with Lightning).
 
 .. code-block:: python