Fix PR comments

docs/source-pytorch/visualize/loggers.rst

@@ -60,24 +60,24 @@ Track and Visualize Experiments
 MLflow Logger
 -------------
 
-The MLflow logger in PyTorch Lightning now includes a `checkpoint_artifact_path_prefix` parameter. This parameter allows you to prefix the checkpoint artifact's path when logging checkpoints as artifacts.
+The MLflow logger in PyTorch Lightning now includes a `checkpoint_path_prefix` parameter. This parameter allows you to prefix the checkpoint artifact's path when logging checkpoints as artifacts.
 
 Example usage:
 
 .. code-block:: python
 
-    from lightning.pytorch import Trainer
+    import lightning as L
     from lightning.pytorch.loggers import MLFlowLogger
 
     mlf_logger = MLFlowLogger(
         experiment_name="lightning_logs",
         tracking_uri="file:./ml-runs",
         checkpoint_artifact_path_prefix="my_prefix"
     )
-    trainer = Trainer(logger=mlf_logger)
+    trainer = L.Trainer(logger=mlf_logger)
 
     # Your LightningModule definition
-    class LitModel(LightningModule):
+    class LitModel(L.LightningModule):
         def training_step(self, batch, batch_idx):
             # example
             self.logger.experiment.whatever_ml_flow_supports(...)

src/lightning/pytorch/loggers/mlflow.py

@@ -97,7 +97,7 @@ def any_lightning_module_function_or_hook(self):
               :paramref:`~lightning.pytorch.callbacks.Checkpoint.save_top_k` ``== -1``
               which also logs every checkpoint during training.
             * if ``log_model == False`` (default), no checkpoint is logged.
-        checkpoint_artifact_path_prefix: A string to prefix the checkpoint artifact's path.
+        checkpoint_path_prefix: A string to prefix the checkpoint artifact's path.
         prefix: A string to put at the beginning of metric keys.
         artifact_location: The location to store run artifacts. If not provided, the server picks an appropriate
             default.
@@ -121,7 +121,7 @@ def __init__(
         tags: Optional[dict[str, Any]] = None,
         save_dir: Optional[str] = "./mlruns",
         log_model: Literal[True, False, "all"] = False,
-        checkpoint_artifact_path_prefix: str = "",
+        checkpoint_path_prefix: str = "",
         prefix: str = "",
         artifact_location: Optional[str] = None,
         run_id: Optional[str] = None,
@@ -148,7 +148,7 @@ def __init__(
         self._artifact_location = artifact_location
         self._log_batch_kwargs = {} if synchronous is None else {"synchronous": synchronous}
         self._initialized = False
-        self._checkpoint_artifact_path_prefix = checkpoint_artifact_path_prefix
+        self._checkpoint_path_prefix = checkpoint_path_prefix
 
         from mlflow.tracking import MlflowClient
 
@@ -363,7 +363,7 @@ def _scan_and_log_checkpoints(self, checkpoint_callback: ModelCheckpoint) -> Non
             aliases = ["latest", "best"] if p == checkpoint_callback.best_model_path else ["latest"]
 
             # Artifact path on mlflow
-            artifact_path = Path(self._checkpoint_artifact_path_prefix) / Path(p).stem
+            artifact_path = Path(self._checkpoint_path_prefix) / Path(p).stem
 
             # Log the checkpoint
             self.experiment.log_artifact(self._run_id, p, artifact_path)

tests/tests_pytorch/loggers/test_mlflow.py

@@ -436,7 +436,7 @@ def test_mlflow_log_model_with_checkpoint_artifact_path_prefix(mlflow_mock, tmp_
 
     # Get model, logger, trainer and train
     model = BoringModel()
-    logger = MLFlowLogger("test", save_dir=str(tmp_path), log_model="all", checkpoint_artifact_path_prefix="my_prefix")
+    logger = MLFlowLogger("test", save_dir=str(tmp_path), log_model="all", checkpoint_path_prefix="my_prefix")
     logger = mock_mlflow_run_creation(logger, experiment_id="test-id")
 
     trainer = Trainer(