def test_pytorch_parity(tmpdir, cls_model, max_diff):
"""
Verify that the same pytorch and lightning models achieve the same results
"""
num_epochs = 4
num_rums = 3
lightning_outs, pl_times = lightning_loop(cls_model, num_rums, num_epochs)
manual_outs, pt_times = vanilla_loop(cls_model, num_rums, num_epochs)
# make sure the losses match exactly to 5 decimal places
for pl_out, pt_out in zip(lightning_outs, manual_outs):
np.testing.assert_almost_equal(pl_out, pt_out, 5)
# the fist run initialize dataset (download & filter)
tutils.assert_speed_parity_absolute(pl_times[1:], pt_times[1:],
nb_epochs=num_epochs, max_diff=max_diff)
def test_pytorch_parity(tmpdir,
cls_model,
max_diff: float,
num_epochs: int = 4,
num_runs: int = 3):
"""
Verify that the same pytorch and lightning models achieve the same results
"""
lightning = lightning_loop(cls_model, num_runs, num_epochs)
vanilla = vanilla_loop(cls_model, num_runs, num_epochs)
# make sure the losses match exactly to 5 decimal places
for pl_out, pt_out in zip(lightning['losses'], vanilla['losses']):
np.testing.assert_almost_equal(pl_out, pt_out, 5)
# the fist run initialize dataset (download & filter)
tutils.assert_speed_parity_absolute(lightning['durations'][1:],
vanilla['durations'][1:],
nb_epochs=num_epochs,
max_diff=max_diff)
