test: integration Trainer's default ckpt with Prod

src/litmodels/integrations/__init__.py

@@ -10,6 +10,6 @@
     __all__ += ["LightningModelCheckpoint"]
 
 if _PYTORCHLIGHTNING_AVAILABLE:
-    from litmodels.integrations.checkpoints import PTLightningModelCheckpoint
+    from litmodels.integrations.checkpoints import PytorchLightningModelCheckpoint
 
-    __all__ += ["PTLightningModelCheckpoint"]
+    __all__ += ["PytorchLightningModelCheckpoint"]

src/litmodels/integrations/checkpoints.py

@@ -67,7 +67,7 @@ def _save_checkpoint(self, trainer: "Trainer", filepath: str) -> None:
 
 if _PYTORCHLIGHTNING_AVAILABLE:
 
-    class PTLightningModelCheckpoint(LitModelCheckpointMixin, _PytorchLightningModelCheckpoint):
+    class PytorchLightningModelCheckpoint(LitModelCheckpointMixin, _PytorchLightningModelCheckpoint):
         """PyTorch Lightning ModelCheckpoint with LitModel support.
 
         Args:

src/litmodels/integrations/imports.py

@@ -1,4 +1,8 @@
-from lightning_utilities import module_available
+import operator
+
+from lightning_utilities import compare_version, module_available
 
 _LIGHTNING_AVAILABLE = module_available("lightning")
+_LIGHTNING_GREATER_EQUAL_2_5_1 = compare_version("lightning", operator.ge, "2.5.1")
 _PYTORCHLIGHTNING_AVAILABLE = module_available("pytorch_lightning")
+_PYTORCHLIGHTNING_GREATER_EQUAL_2_5_1 = compare_version("pytorch_lightning", operator.ge, "2.5.1")

tests/integrations/__init__.py

@@ -1,7 +1,18 @@
 import pytest
-from litmodels.integrations.imports import _LIGHTNING_AVAILABLE, _PYTORCHLIGHTNING_AVAILABLE
+from litmodels.integrations.imports import (
+    _LIGHTNING_AVAILABLE,
+    _LIGHTNING_GREATER_EQUAL_2_5_1,
+    _PYTORCHLIGHTNING_AVAILABLE,
+    _PYTORCHLIGHTNING_GREATER_EQUAL_2_5_1,
+)
 
 _SKIP_IF_LIGHTNING_MISSING = pytest.mark.skipif(not _LIGHTNING_AVAILABLE, reason="Lightning not available")
+_SKIP_IF_LIGHTNING_BELLOW_2_5_1 = pytest.mark.skipif(
+    not _LIGHTNING_GREATER_EQUAL_2_5_1, reason="Lightning without integration introduced in 2.5.1"
+)
 _SKIP_IF_PYTORCHLIGHTNING_MISSING = pytest.mark.skipif(
     not _PYTORCHLIGHTNING_AVAILABLE, reason="PyTorch Lightning not available"
 )
+_SKIP_IF_PYTORCHLIGHTNING_BELLOW_2_5_1 = pytest.mark.skipif(
+    not _PYTORCHLIGHTNING_GREATER_EQUAL_2_5_1, reason="PyTorch Lightning without integration introduced in 2.5.1"
+)

tests/integrations/test_checkpoints.py

@@ -23,7 +23,7 @@ def test_lightning_checkpoint_callback(mock_auth, mock_upload_model, importing,
         from lightning.pytorch.demos.boring_classes import BoringModel
         from litmodels.integrations.checkpoints import LightningModelCheckpoint as LitModelCheckpoint
     elif importing == "pytorch_lightning":
-        from litmodels.integrations.checkpoints import PTLightningModelCheckpoint as LitModelCheckpoint
+        from litmodels.integrations.checkpoints import PytorchLightningModelCheckpoint as LitModelCheckpoint
         from pytorch_lightning import Trainer
         from pytorch_lightning.callbacks import ModelCheckpoint
         from pytorch_lightning.demos.boring_classes import BoringModel
@@ -64,7 +64,7 @@ def test_lightning_checkpointing_pickleable(mock_auth, importing):
     if importing == "lightning":
         from litmodels.integrations.checkpoints import LightningModelCheckpoint as LitModelCheckpoint
     elif importing == "pytorch_lightning":
-        from litmodels.integrations.checkpoints import PTLightningModelCheckpoint as LitModelCheckpoint
+        from litmodels.integrations.checkpoints import PytorchLightningModelCheckpoint as LitModelCheckpoint
 
     ckpt = LitModelCheckpoint(model_name="org-name/teamspace/model-name")
     pickle.dumps(ckpt)

tests/integrations/test_cloud.py

@@ -3,14 +3,29 @@
 from io import StringIO
 
 import pytest
+from lightning_sdk import Teamspace
 from lightning_sdk.lightning_cloud.rest_client import GridRestClient
 from lightning_sdk.utils.resolve import _resolve_teamspace
 from litmodels import download_model, upload_model
 
+from tests.integrations import _SKIP_IF_LIGHTNING_BELLOW_2_5_1, _SKIP_IF_PYTORCHLIGHTNING_BELLOW_2_5_1
+
 LIT_ORG = "lightning-ai"
 LIT_TEAMSPACE = "LitModels"
 
 
+def _cleanup_model(teamspace: Teamspace, model_name: str) -> None:
+    """Cleanup model from the teamspace."""
+    client = GridRestClient()
+    # cleaning created models as each test run shall have unique model name
+    model = client.models_store_get_model_by_name(
+        project_owner_name=teamspace.owner.name,
+        project_name=teamspace.name,
+        model_name=model_name,
+    )
+    client.models_store_delete_model(project_id=teamspace.id, model_id=model.id)
+
+
 @pytest.mark.cloud()
 def test_upload_download_model(tmp_path):
     """Verify that the model is uploaded to the teamspace"""
@@ -41,11 +56,38 @@ def test_upload_download_model(tmp_path):
     for file in model_files:
         assert os.path.isfile(os.path.join(tmp_path, file))
 
-    client = GridRestClient()
-    # cleaning created models with todo: also consider how to delete just this version of the model
-    model = client.models_store_get_model_by_name(
-        project_owner_name=teamspace.owner.name,
-        project_name=teamspace.name,
-        model_name=model_name,
+    # CLEANING
+    _cleanup_model(teamspace, model_name)
+
+
+@pytest.mark.parametrize(
+    "importing",
+    [
+        pytest.param("lightning", marks=_SKIP_IF_LIGHTNING_BELLOW_2_5_1),
+        pytest.param("pytorch_lightning", marks=_SKIP_IF_PYTORCHLIGHTNING_BELLOW_2_5_1),
+    ],
+)
+@pytest.mark.cloud()
+# todo: mock env variables as it would run in studio
+def test_lightning_default_checkpointing(importing, tmp_path):
+    if importing == "lightning":
+        from lightning import Trainer
+        from lightning.pytorch.demos.boring_classes import BoringModel
+    elif importing == "pytorch_lightning":
+        from pytorch_lightning import Trainer
+        from pytorch_lightning.demos.boring_classes import BoringModel
+
+    # model name with random hash
+    model_name = f"litmodels_test_integrations+{os.urandom(8).hex()}"
+    teamspace = _resolve_teamspace(org=LIT_ORG, teamspace=LIT_TEAMSPACE, user=None)
+    org_team = f"{teamspace.owner.name}/{teamspace.name}"
+
+    trainer = Trainer(
+        max_epochs=2,
+        default_root_dir=tmp_path,
+        model_registry=f"{org_team}/{model_name}",
     )
-    client.models_store_delete_model(project_id=teamspace.id, model_id=model.id)
+    trainer.fit(BoringModel())
+
+    # CLEANING
+    _cleanup_model(teamspace, model_name)