linter

Author: Borda

src/lightning/pytorch/loops/training_epoch_loop.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import math
 from collections import OrderedDict
+from contextlib import suppress
 from dataclasses import dataclass
 from typing import Any, Optional, Union
 
@@ -279,7 +280,7 @@ def advance(self, data_fetcher: _DataFetcher) -> None:
         from lightning.pytorch.utilities.exceptions import SIGTERMException
 
         if dist.is_available() and dist.is_initialized() and self.trainer.world_size > 1:
-            try:
+            with suppress(Exception):  # never crash CI
                 sigterm_tensor = torch.tensor(
                     [1 if getattr(self.trainer, "received_sigterm", False) else 0],
                     device=self.trainer.strategy.root_device,
@@ -289,8 +290,6 @@ def advance(self, data_fetcher: _DataFetcher) -> None:
                 if sigterm_tensor.item() == 1:
                     dist.barrier()
                     raise SIGTERMException()
-            except Exception:
-                pass  # never crash CI
         # =====================================================================
 
         if using_dataloader_iter := isinstance(data_fetcher, _DataLoaderIterDataFetcher):

src/lightning/pytorch/trainer/connectors/callback_connector.py

@@ -106,8 +106,9 @@ def _configure_checkpoint_callbacks(self, enable_checkpointing: bool) -> None:
                 model_checkpoint = LitModelCheckpoint(model_registry=self.trainer._model_registry)
             else:
                 rank_zero_info(
-                    "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable"
-                    " `LitModelCheckpoint` for automatic upload to the Lightning model registry."
+                    "💡 Tip: For seamless cloud uploads and versioning,"
+                    " try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint,"
+                    " which syncs automatically with the Lightning model registry."
                 )
                 model_checkpoint = ModelCheckpoint()
             self.trainer.callbacks.append(model_checkpoint)