Add warning for few workers

CHANGELOG.md

@@ -26,6 +26,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added model configuration checking ([#1199](https://github.com/PyTorchLightning/pytorch-lightning/pull/1199))
 - Added support for optimizer frequencies through `LightningModule.configure_optimizers()` ([#1269](https://github.com/PyTorchLightning/pytorch-lightning/pull/1269))
 - Added option to run without an optimizer by returning `None` from `configure_optimizers`. ([#1279](https://github.com/PyTorchLightning/pytorch-lightning/pull/1279))
+- Added a warning when the number of data loader workers is small. ([#1378](https://github.com/PyTorchLightning/pytorch-lightning/pull/1378))
 
 ### Changed
 

pytorch_lightning/trainer/data_loading.py

@@ -1,8 +1,9 @@
+import warnings
 from abc import ABC, abstractmethod
 from typing import Union, List, Tuple, Callable
 
 import torch.distributed as torch_distrib
-from torch.utils.data import SequentialSampler, DataLoader
+from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_lightning.core import LightningModule
@@ -73,6 +74,12 @@ def _percent_range_check(self, name: str) -> None:
         if not 0. <= value <= 1.:
             raise ValueError(msg)
 
+    def _worker_check(self, dataloader: DataLoader, name: str) -> None:
+        if isinstance(dataloader, DataLoader) and dataloader.num_workers <= 2:
+            warnings.warn(f'The dataloader, {name}, does not have many workers which may be a bottleneck.'
+                          ' Consider increasing the value of the `num_workers` argument`'
+                          ' in the `DataLoader` init to improve performance.')
+
     def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:
 
         # don't do anything if it's not a dataloader
@@ -112,11 +119,13 @@ def reset_train_dataloader(self, model: LightningModule) -> None:
             model: The current `LightningModule`
         """
         self.train_dataloader = self.request_dataloader(model.train_dataloader)
+
         self.num_training_batches = 0
 
         # automatically add samplers
         self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)
 
+        self._worker_check(self.train_dataloader, 'train dataloader')
         self._percent_range_check('train_percent_check')
 
         if not _has_len(self.train_dataloader):
@@ -176,10 +185,10 @@ def _reset_eval_dataloader(self, model: LightningModule,
         # determine number of batches
         # datasets could be none, 1 or 2+
         if len(dataloaders) != 0:
-            for dataloader in dataloaders:
+            for i, dataloader in enumerate(dataloaders):
+                self._worker_check(dataloader, f'{mode} dataloader {i}')
                 if not _has_len(dataloader):
                     num_batches = float('inf')
-                    break
 
             percent_check = getattr(self, f'{mode}_percent_check')
 

tests/trainer/test_dataloaders.py

@@ -15,6 +15,7 @@
     LightValStepFitMultipleDataloadersMixin,
     LightValStepFitSingleDataloaderMixin,
     LightTrainDataloader,
+    LightValidationDataloader,
     LightInfTrainDataloader,
     LightInfValDataloader,
     LightInfTestDataloader,
@@ -485,6 +486,47 @@ class CurrentTestModel(
         trainer.fit(model)
 
 
+def test_warning_with_few_workers(tmpdir):
+    """ Test that error is raised if dataloader with only a few workers is used """
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightTrainDataloader,
+        LightValStepFitSingleDataloaderMixin,
+        LightTestFitSingleTestDataloadersMixin,
+        LightEmptyTestStep,
+        TestModelBase,
+    ):
+        pass
+
+    hparams = tutils.get_default_hparams()
+    model = CurrentTestModel(hparams)
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloaders=model._dataloader(train=False),
+                       test_dataloaders=model._dataloader(train=False))
+
+    trainer = Trainer(**trainer_options)
+
+    # fit model
+    with pytest.warns(UserWarning, match='train'):
+        trainer.fit(model, **fit_options)
+
+    with pytest.warns(UserWarning, match='val'):
+        trainer.fit(model, **fit_options)
+
+    with pytest.warns(UserWarning, match='test'):
+        trainer.test()
+
+
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason='Test requires multiple GPUs')
 def test_dataloader_reinit_for_subclass():