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Update `tests/tuner/*.py` to use `devices` instead of `gpus` or `ipus` (#11520) 

Co-authored-by: Jirka <jirka.borovec@seznam.cz>
This commit is contained in:
Jv Kyle Eclarin 2022-02-03 09:28:13 -06:00 committed by GitHub
parent d132a9c3b7
commit a34930b772
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2 changed files with 8 additions and 3 deletions
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1
tests/tuner/test_auto_gpu_select.py
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@ -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"],

10
tests/tuner/test_scale_batch_size.py
View File

@ -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