update metadata notation (#101)

Author: Borda

src/litmodels/integrations/checkpoints.py

@@ -193,7 +193,7 @@ def _upload_model(self, trainer: "pl.Trainer", filepath: Union[str, Path], metad
         mro = inspect.getmro(type(self))
         abc_index = mro.index(LitModelCheckpointMixin)
         ckpt_class = mro[abc_index - 1]
-        metadata.update({"litModels_integration": ckpt_class.__name__})
+        metadata.update({"litModels.integration": ckpt_class.__name__})
         # Add to queue instead of uploading directly
         get_model_manager().queue_upload(registry_name=model_registry, filepath=filepath, metadata=metadata)
         if self._clear_all_local:

src/litmodels/integrations/duplicate.py

@@ -64,6 +64,6 @@ def duplicate_hf_model(
         lit_model = model_name
     if not metadata:
         metadata = {}
-    metadata.update({"litModels_integration": "duplicate_hf_model", "hf_model": hf_model})
+    metadata.update({"litModels.integration": "duplicate_hf_model", "hf_model": hf_model})
     model = upload_model_files(name=lit_model, path=local_workdir / model_name, verbose=verbose, metadata=metadata)
     return model.name

src/litmodels/integrations/mixins.py

@@ -65,7 +65,7 @@ def _upload_model_files(
         mro = inspect.getmro(type(self))
         abc_index = mro.index(ModelRegistryMixin)
         mixin_class = mro[abc_index - 1]
-        metadata.update({"litModels_integration": mixin_class.__name__})
+        metadata.update({"litModels.integration": mixin_class.__name__})
         upload_model_files(name=name, path=path, metadata=metadata)
 
 

src/litmodels/io/gateway.py

@@ -103,7 +103,7 @@ def save_model(
 
     if not metadata:
         metadata = {}
-    metadata.update({"litModels_integration": "save_model"})
+    metadata.update({"litModels.integration": "save_model"})
 
     return upload_model(
         model=path,

tests/integrations/test_checkpoints.py

@@ -104,7 +104,7 @@ def test_lightning_checkpoint_callback(
             path=mock.ANY,
             progress_bar=True,
             cloud_account=None,
-            metadata={"litModels_integration": LitModelCheckpoint.__name__, "litModels": litmodels.__version__},
+            metadata={"litModels.integration": LitModelCheckpoint.__name__, "litModels": litmodels.__version__},
         )
         for v in ("epoch=0-step=64", "epoch=1-step=128")
     ]

tests/integrations/test_duplicate.py

@@ -26,6 +26,6 @@ def test_duplicate_hf_model(mock_upload_model, mock_snapshot_download, tmp_path)
     mock_upload_model.assert_called_with(
         name=f"{model_name}",
         path=tmp_path / hf_model.replace("/", "_"),
-        metadata={"hf_model": hf_model, "litModels_integration": "duplicate_hf_model"},
+        metadata={"hf_model": hf_model, "litModels.integration": "duplicate_hf_model"},
         verbose=1,
     )

tests/integrations/test_mixins.py

@@ -24,7 +24,7 @@ def test_pickle_push_and_pull(mock_download_model, mock_upload_model, tmp_path):
     # The expected registry name is "dummy_model:v1" and the file should be placed in the temp folder.
     expected_path = tmp_path / "DummyModel.pkl"
     mock_upload_model.assert_called_once_with(
-        name="DummyModel:v1", path=expected_path, metadata={"litModels_integration": "PickleRegistryMixin"}
+        name="DummyModel:v1", path=expected_path, metadata={"litModels.integration": "PickleRegistryMixin"}
     )
 
     # Set the mock to return the full path to the pickle file.
@@ -78,7 +78,7 @@ def test_pytorch_push_and_pull(mock_download_model, mock_upload_model, torch_cla
     mock_upload_model.assert_called_once_with(
         name=torch_class.__name__,
         path=[tmp_path / f"{torch_class.__name__}.pth", json_path],
-        metadata={"litModels_integration": "PyTorchRegistryMixin"},
+        metadata={"litModels.integration": "PyTorchRegistryMixin"},
     )
 
     # Prepare mocking for pull_from_registry.

tests/test_io_cloud.py

@@ -84,7 +84,7 @@ def test_upload_model(mock_upload_model, tmp_path, model, model_path, verbose):
         name="org-name/teamspace/model-name",
         cloud_account="cluster_id",
         progress_bar=True,
-        metadata={"litModels": litmodels.__version__, "litModels_integration": "save_model"},
+        metadata={"litModels": litmodels.__version__, "litModels.integration": "save_model"},
     )