feat: add suggest_integrations flag to toggle integration suggestions in Trainer logs (default True) (#21632)

feat: add `suggest_integrations` flag to `Trainer` for optional integration suggestions

Author: deependujha

src/lightning/pytorch/CHANGELOG.md

@@ -10,6 +10,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added `suggest_integrations` flag to `Trainer` to control whether optional integration suggestions (e.g., litmodels, litlogger) are shown in logs ([#21632](https://github.com/Lightning-AI/pytorch-lightning/pull/21632))
+
 - Added `log_key_prefix` parameter to `LearningRateMonitor` callback for prefixing logged metric names ([#21612](https://github.com/Lightning-AI/pytorch-lightning/issues/21612))
 
 ### Changed

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

@@ -175,7 +175,7 @@ def _maybe_show_litmodels_tip(self) -> None:
         This is called after loggers are set up, so we can reliably check for LitLogger.
 
         """
-        if not self._pending_litmodels_tip:
+        if not self._pending_litmodels_tip or not self.trainer.suggest_integrations:
             return
         self._pending_litmodels_tip = False  # Only show once
 

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

@@ -87,7 +87,10 @@ def configure_logger(self, logger: Union[bool, Logger, Iterable[Logger]]) -> Non
         else:
             self.trainer.loggers = [logger]
 
-        if not any(isinstance(logger, LitLogger) for logger in self.trainer.loggers):
+        if (
+            not any(isinstance(logger, LitLogger) for logger in self.trainer.loggers)
+            and self.trainer.suggest_integrations
+        ):
             rank_zero_info(
                 "💡 Tip: For seamless cloud logging and experiment tracking,"
                 " try installing [litlogger](https://pypi.org/project/litlogger/) to enable LitLogger,"

src/lightning/pytorch/trainer/trainer.py

@@ -132,6 +132,7 @@ def __init__(
         default_root_dir: Optional[_PATH] = None,
         enable_autolog_hparams: bool = True,
         model_registry: Optional[str] = None,
+        suggest_integrations: bool = True,
     ) -> None:
         r"""Customize every aspect of training via flags.
 
@@ -308,6 +309,10 @@ def __init__(
 
             model_registry: The name of the model being uploaded to Model hub.
 
+            suggest_integrations: Whether to display suggestions for optional Lightning integrations.
+                Default: ``True``.
+
+
         Raises:
             TypeError:
                 If ``gradient_clip_val`` is not an int or float.
@@ -324,6 +329,7 @@ def __init__(
 
         # remove version if accidentally passed
         self._model_registry = model_registry.split(":")[0] if model_registry else None
+        self.suggest_integrations = suggest_integrations
 
         self.barebones = barebones
         if barebones:

tests/tests_pytorch/trainer/test_trainer.py

@@ -2138,3 +2138,23 @@ def test_expand_home_trainer():
     assert trainer.default_root_dir == str(home_root / "trainer")
     trainer = Trainer(default_root_dir=Path("~/trainer"))
     assert trainer.default_root_dir == str(home_root / "trainer")
+
+
+@pytest.mark.parametrize("suggest_integrations", [True, False])
+def test_trainer_integration_suggestions(tmp_path, caplog, suggest_integrations):
+    caplog.set_level("INFO", logger="lightning.pytorch.utilities.rank_zero")
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        max_epochs=1,
+        enable_progress_bar=False,
+        suggest_integrations=suggest_integrations,
+    )
+
+    trainer.fit(BoringModel())
+
+    messages = [r.getMessage() for r in caplog.records if r.name == "lightning.pytorch.utilities.rank_zero"]
+
+    has_suggestion = any("litmodels" in m or "litlogger" in m for m in messages)
+
+    assert has_suggestion == suggest_integrations