feat: add support for cloud files and non-local checkpoints

src/lightning/pytorch/loggers/wandb.py

@@ -27,6 +27,7 @@
 from torch import Tensor
 from typing_extensions import override
 
+from lightning.fabric.utilities.cloud_io import _is_local_file_protocol
 from lightning.fabric.utilities.logger import (
     _add_prefix,
     _convert_json_serializable,
@@ -258,7 +259,9 @@ def any_lightning_module_function_or_hook(self):
 
     Args:
         name: Display name for the run.
-        save_dir: Path where data is saved.
+        save_dir: Path where data is saved. Can be:
+            - A local directory path
+            - A remote storage path (S3, GCS, Azure Blob Storage)
         version: Sets the version, mainly used to resume a previous run.
         offline: Run offline (data can be streamed later to wandb servers).
         dir: Same as save_dir.
@@ -348,6 +351,7 @@ def __init__(
         self._name = self._wandb_init.get("name")
         self._id = self._wandb_init.get("id")
         self._checkpoint_name = checkpoint_name
+        self._is_local = _is_local_file_protocol(save_dir)
 
     def __getstate__(self) -> dict[str, Any]:
         import wandb
@@ -676,7 +680,12 @@ def _scan_and_log_checkpoints(self, checkpoint_callback: ModelCheckpoint) -> Non
             if not self._checkpoint_name:
                 self._checkpoint_name = f"model-{self.experiment.id}"
             artifact = wandb.Artifact(name=self._checkpoint_name, type="model", metadata=metadata)
-            artifact.add_file(p, name="model.ckpt")
+
+            if not self._is_local:
+                artifact.add_reference(p, name="model.ckpt")
+            else:
+                artifact.add_file(p, name="model.ckpt")
+
             aliases = ["latest", "best"] if p == checkpoint_callback.best_model_path else ["latest"]
             self.experiment.log_artifact(artifact, aliases=aliases)
             # remember logged models - timestamp needed in case filename didn't change (lastkckpt or custom name)