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Merged
Borda merged 4 commits into from
Apr 14, 2025
Merged

test: validate scenario being in studio on cloud #91

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test: validate scenario being in studio on cloud
Borda Apr 14, 2025
f99bfae
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 14, 2025
146de84
Merge branch 'main' into test/in-studio-2
Borda Apr 14, 2025
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Merge branch 'main' into test/in-studio-2
Borda Apr 14, 2025
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22 changes: 17 additions & 5 deletions tests/integrations/test_cloud.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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:
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