Add a custom PossibleUserWarning category (#10675)

docs/source/guides/speed.rst

@@ -153,6 +153,11 @@ For debugging purposes or for dataloaders that load very small datasets, it is d
 
     warnings.filterwarnings("ignore", ".*Consider increasing the value of the `num_workers` argument*")
 
+    # or to ignore all warnings which could be false positives
+    from pytorch_lightning.utilities.warnings import PossibleUserWarning
+
+    warnings.filterwarnings("ignore", category=PossibleUserWarning)
+
 Spawn
 """""
 When using ``strategy=ddp_spawn`` or training on TPUs, the way multiple GPUs/TPU cores are used is by calling ``.spawn()`` under the hood.

pytorch_lightning/trainer/data_loading.py

@@ -40,6 +40,7 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
 from pytorch_lightning.utilities.model_helpers import is_overridden
+from pytorch_lightning.utilities.warnings import PossibleUserWarning
 
 
 class TrainerDataLoadingMixin(ABC):
@@ -109,7 +110,8 @@ def _worker_check(self, dataloader: DataLoader, name: str) -> None:
                 f"The dataloader, {name}, does not have many workers which may be a bottleneck."
                 " Consider increasing the value of the `num_workers` argument`"
                 f" (try {num_cpus} which is the number of cpus on this machine)"
-                " in the `DataLoader` init to improve performance."
+                " in the `DataLoader` init to improve performance.",
+                category=PossibleUserWarning,
             )
 
     def _requires_distributed_sampler(self, dataloader) -> bool:
@@ -267,7 +269,8 @@ def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -
             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"
-                " you want to see logs for the training epoch."
+                " you want to see logs for the training epoch.",
+                category=PossibleUserWarning,
             )
 
     def _reset_eval_dataloader(

pytorch_lightning/trainer/trainer.py

@@ -96,6 +96,7 @@
     LRSchedulerTypeUnion,
     TRAIN_DATALOADERS,
 )
+from pytorch_lightning.utilities.warnings import PossibleUserWarning
 
 log = logging.getLogger(__name__)
 # warnings to ignore in trainer
@@ -1531,7 +1532,8 @@ def _log_device_info(self) -> None:
 
         if torch.cuda.is_available() and self._device_type != _AcceleratorType.GPU:
             rank_zero_warn(
-                "GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`."
+                "GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.",
+                category=PossibleUserWarning,
             )
 
         if _TPU_AVAILABLE and self._device_type != _AcceleratorType.TPU:

pytorch_lightning/utilities/warnings.py

@@ -27,8 +27,12 @@ def rank_zero_warn(*args, stacklevel: int = 4, **kwargs):
     _warn(*args, stacklevel=stacklevel, **kwargs)
 
 
+class PossibleUserWarning(UserWarning):
+    """Warnings that could be false positives."""
+
+
 class LightningDeprecationWarning(DeprecationWarning):
-    ...
+    """Deprecation warnings raised by PyTorch Lightning."""
 
 
 # enable our warnings