diff --git a/tests/tuner/test_auto_gpu_select.py b/tests/tuner/test_auto_gpu_select.py
index c8b510f65e..f57a88c787 100644
--- a/tests/tuner/test_auto_gpu_select.py
+++ b/tests/tuner/test_auto_gpu_select.py
@@ -22,6 +22,7 @@ from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers.runif import RunIf
 
 
+# TODO: add pytest.deprecated_call @daniellepintz
 @RunIf(min_gpus=2)
 @pytest.mark.parametrize(
     ["auto_select_gpus", "gpus", "expected_error"],
diff --git a/tests/tuner/test_scale_batch_size.py b/tests/tuner/test_scale_batch_size.py
index 31d3dd3dd3..13112ee9f4 100644
--- a/tests/tuner/test_scale_batch_size.py
+++ b/tests/tuner/test_scale_batch_size.py
@@ -127,7 +127,9 @@ def test_auto_scale_batch_size_trainer_arg(tmpdir, scale_arg):
     tutils.reset_seed()
     before_batch_size = 2
     model = BatchSizeModel(batch_size=before_batch_size)
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, auto_scale_batch_size=scale_arg, gpus=1)
+    trainer = Trainer(
+        default_root_dir=tmpdir, max_epochs=1, auto_scale_batch_size=scale_arg, accelerator="gpu", devices=1
+    )
     trainer.tune(model)
     after_batch_size = model.batch_size
     assert before_batch_size != after_batch_size, "Batch size was not altered after running auto scaling of batch size"
@@ -169,7 +171,7 @@ def test_auto_scale_batch_size_set_model_attribute(tmpdir, use_hparams):
     model_class = HparamsBatchSizeModel if use_hparams else BatchSizeModel
     model = model_class(**hparams)
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, auto_scale_batch_size=True, gpus=1)
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, auto_scale_batch_size=True, accelerator="gpu", devices=1)
     trainer.tune(model, datamodule_fit)
     after_batch_size = model.hparams.batch_size if use_hparams else model.batch_size
     assert trainer.datamodule == datamodule_fit
@@ -239,7 +241,9 @@ def test_error_on_dataloader_passed_to_fit(tmpdir):
 def test_auto_scale_batch_size_with_amp(tmpdir):
     before_batch_size = 2
     model = BatchSizeModel(batch_size=before_batch_size)
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, auto_scale_batch_size=True, gpus=1, precision=16)
+    trainer = Trainer(
+        default_root_dir=tmpdir, max_steps=1, auto_scale_batch_size=True, accelerator="gpu", devices=1, precision=16
+    )
     trainer.tune(model)
     after_batch_size = model.batch_size
     assert trainer.amp_backend == AMPType.NATIVE