add warning when Trainer(log_every_n_steps) not well chosen

CHANGELOG.md

@@ -50,6 +50,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `should_rank_save_checkpoint` property to Training Plugins ([#7684](https://github.com/PyTorchLightning/pytorch-lightning/pull/7684))
 
 
+- Added a warning if `Trainer(log_every_n_steps)` is a value too high for the training dataloader ([#X](https://github.com/PyTorchLightning/pytorch-lightning/pull/X))
+
+
 ### Changed
 
 - Changed calling of `untoggle_optimizer(opt_idx)` out of the closure function ([#7563](https://github.com/PyTorchLightning/pytorch-lightning/pull/7563)

pytorch_lightning/trainer/data_loading.py

@@ -51,6 +51,7 @@ class TrainerDataLoadingMixin(ABC):
     test_dataloaders: Optional[List[DataLoader]]
     num_test_batches: List[Union[int, float]]
     limit_train_batches: Union[int, float]
+    log_every_n_steps: int
     overfit_batches: Union[int, float]
     distributed_sampler_kwargs: dict
     accelerator: Accelerator
@@ -299,6 +300,13 @@ def reset_train_dataloader(self, model: LightningModule) -> None:
                 self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
                 self.val_check_batch = max(1, self.val_check_batch)
 
+        if self.logger is not None and self.num_training_batches < self.log_every_n_steps:
+            rank_zero_warn(
+                f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
+                f" Trainer(log_every_n_steps={self.log_every_n_steps}). Set a lower value for log_every_n_steps if"
+                f" you want to see logs for the training epoch."
+            )
+
     def _reset_eval_dataloader(
         self,
         model: LightningModule,

tests/trainer/test_dataloaders.py

@@ -895,6 +895,24 @@ def test_auto_add_worker_init_fn_distributed(tmpdir, monkeypatch):
     trainer.fit(model, train_dataloader=dataloader)
 
 
+def test_warning_with_small_dataloader_and_logging_interval(tmpdir):
+    model = BoringModel()
+    dataloader = DataLoader(RandomDataset(32, length=10))
+    model.train_dataloader = lambda: dataloader
+
+    with pytest.warns(UserWarning, match=r"The number of training samples \(10\) is smaller than the logging interval"):
+        trainer = Trainer(
+            default_root_dir=tmpdir,
+            max_epochs=1,
+            log_every_n_steps=11,
+        )
+        trainer.fit(model)
+
+    with pytest.warns(UserWarning, match=r"The number of training samples \(1\) is smaller than the logging interval"):
+        trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, log_every_n_steps=2, limit_train_batches=1)
+        trainer.fit(model)
+
+
 def test_warning_with_iterable_dataset_and_len(tmpdir):
     """ Tests that a warning message is shown when an IterableDataset defines `__len__`. """
     model = BoringModel()