diff --git a/tests/core/callbacks/test_load_best_model_callback_torch.py b/tests/core/callbacks/test_load_best_model_callback_torch.py
index c2be7f7a..07576599 100644
--- a/tests/core/callbacks/test_load_best_model_callback_torch.py
+++ b/tests/core/callbacks/test_load_best_model_callback_torch.py
@@ -73,7 +73,6 @@ def model_and_optimizers(request):
 
 
 @pytest.mark.torch
-@pytest.mark.temp
 @pytest.mark.parametrize("driver,device", [("torch", [0, 1]), ("torch", 1), ("torch", "cpu")])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @magic_argv_env_context
 def test_load_best_model_callback(
diff --git a/tests/core/dataloaders/torch_dataloader/test_fdl.py b/tests/core/dataloaders/torch_dataloader/test_fdl.py
index 6d20754a..8ed7441b 100644
--- a/tests/core/dataloaders/torch_dataloader/test_fdl.py
+++ b/tests/core/dataloaders/torch_dataloader/test_fdl.py
@@ -147,13 +147,14 @@ class TestFdl:
         assert 'Parameter:prefetch_factor' in out[0]
 
     @recover_logger
+    @pytest.mark.temp
     def test_version_111(self):
         if parse_version(torch.__version__) <= parse_version('1.7'):
             pytest.skip("Torch version smaller than 1.7")
         logger.set_stdout()
         ds = DataSet({"x": [[1, 2], [2, 3, 4], [4, 5, 6, 7]] * 10, "y": [1, 0, 1] * 10})
         with Capturing() as out:
-            dl = TorchDataLoader(ds, num_workers=2, prefetch_factor=3, shuffle=False)
+            dl = TorchDataLoader(ds, num_workers=0, prefetch_factor=2, generator=torch.Generator(), shuffle=False)
             for idx, batch in enumerate(dl):
                 assert len(batch['x'])==1
                 assert batch['x'][0].tolist() == ds[idx]['x']