test: validate scenario being in studio on cloud (#91)

* test: validate scenario being in studio on cloud

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: Borda

tests/integrations/test_cloud.py

@@ -141,26 +141,38 @@ def test_lightning_default_checkpointing(importing, in_studio, monkeypatch, tmp_
         pytest.param("pytorch_lightning", marks=_SKIP_IF_PYTORCHLIGHTNING_BELLOW_2_5_1),
     ],
 )
+@pytest.mark.parametrize(
+    "in_studio",
+    [
+        False,
+        pytest.param(True, marks=pytest.mark.skipif(platform.system() == "Windows", reason="studio is not Windows")),
+    ],
+)
 @pytest.mark.cloud
-# todo: mock env variables as it would run in studio
-def test_lightning_plain_resume(trainer_method, registry, importing, tmp_path):
+def test_lightning_plain_resume(trainer_method, registry, importing, in_studio, tmp_path, monkeypatch):
     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
 
+    if in_studio:
+        # mock env variables as it would run in studio
+        monkeypatch.setenv("LIGHTNING_ORG", LIT_ORG)
+        monkeypatch.setenv("LIGHTNING_TEAMSPACE", LIT_TEAMSPACE)
+
     trainer = Trainer(max_epochs=1, limit_train_batches=50, limit_val_batches=20, default_root_dir=tmp_path)
     trainer.fit(BoringModel())
     checkpoint_path = getattr(trainer.checkpoint_callback, "best_model_path")
 
     # model name with random hash
     teamspace, org_team, model_name = _prepare_variables(f"resume_{trainer_method}")
-    upload_model(model=checkpoint_path, name=f"{org_team}/{model_name}")
+    model_registry = f"{org_team}/{model_name}" if not in_studio else model_name
+    upload_model(model=checkpoint_path, name=model_registry)
     expected_num_versions = 1
 
-    trainer_kwargs = {"model_registry": f"{org_team}/{model_name}"} if "<model>" not in registry else {}
+    trainer_kwargs = {"model_registry": model_registry} if "<model>" not in registry else {}
     trainer = Trainer(
         max_epochs=2,
         default_root_dir=tmp_path,
@@ -170,7 +182,7 @@ def test_lightning_plain_resume(trainer_method, registry, importing, tmp_path):
         limit_predict_batches=10,
         **trainer_kwargs,
     )
-    registry = registry.replace("<model>", f"{org_team}/{model_name}")
+    registry = registry.replace("<model>", model_registry)
     if trainer_method == "fit":
         trainer.fit(BoringModel(), ckpt_path=registry)
         if trainer_kwargs: