Add checkpoint artifact path prefix to MLflow logger

docs/source-pytorch/visualize/loggers.rst

@@ -54,3 +54,37 @@ Track and Visualize Experiments
 
         </div>
     </div>
+
+.. _mlflow_logger:
+
+MLflow Logger
+-------------
+
+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
+
+    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 = L.Trainer(logger=mlf_logger)
+
+    # Your LightningModule definition
+    class LitModel(L.LightningModule):
+        def training_step(self, batch, batch_idx):
+            # example
+            self.logger.experiment.whatever_ml_flow_supports(...)
+
+        def any_lightning_module_function_or_hook(self):
+            self.logger.experiment.whatever_ml_flow_supports(...)
+
+    # Train your model
+    model = LitModel()
+    trainer.fit(model)

src/lightning/pytorch/CHANGELOG.md

@@ -4,6 +4,8 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+- Added a new `checkpoint_artifact_path_prefix` parameter to the MLflow logger which can control the path to where the MLflow artifacts for the model checkpoints are stored.
+
 ## [2.5.0] - 2024-12-19
 
 ### Added

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_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.
@@ -121,6 +121,7 @@ def __init__(
         tags: Optional[dict[str, Any]] = None,
         save_dir: Optional[str] = "./mlruns",
         log_model: Literal[True, False, "all"] = False,
+        checkpoint_path_prefix: str = "",
         prefix: str = "",
         artifact_location: Optional[str] = None,
         run_id: Optional[str] = None,
@@ -147,6 +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_path_prefix = checkpoint_path_prefix
 
         from mlflow.tracking import MlflowClient
 
@@ -361,7 +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(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

@@ -427,3 +427,33 @@ def test_set_tracking_uri(mlflow_mock):
     mlflow_mock.set_tracking_uri.assert_not_called()
     _ = logger.experiment
     mlflow_mock.set_tracking_uri.assert_called_with("the_tracking_uri")
+
+
+@mock.patch("lightning.pytorch.loggers.mlflow._get_resolve_tags", Mock())
+def test_mlflow_log_model_with_checkpoint_path_prefix(mlflow_mock, tmp_path):
+    """Test that the logger creates the folders and files in the right place with a prefix."""
+    client = mlflow_mock.tracking.MlflowClient
+
+    # Get model, logger, trainer and train
+    model = BoringModel()
+    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(
+        default_root_dir=tmp_path,
+        logger=logger,
+        max_epochs=2,
+        limit_train_batches=3,
+        limit_val_batches=3,
+    )
+    trainer.fit(model)
+
+    # Checkpoint log
+    assert client.return_value.log_artifact.call_count == 2
+    # Metadata and aliases log
+    assert client.return_value.log_artifacts.call_count == 2
+
+    # Check that the prefix is used in the artifact path
+    for call in client.return_value.log_artifact.call_args_list:
+        args, _ = call
+        assert str(args[2]).startswith("my_prefix")