def test_ddp_sampler_error():
"""
Make sure DDP + AMP work
:return:
"""
if not can_run_gpu_test():
return
os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
hparams = get_hparams()
model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
exp = get_exp(True)
exp.save()
trainer = Trainer(experiment=exp,
show_progress_bar=False,
max_nb_epochs=1,
gpus=[0, 1],
distributed_backend='ddp',
use_amp=True)
with pytest.warns(UserWarning):
trainer.get_dataloaders(model)
clear_save_dir()
def test_ddp_sampler_error():
"""
Make sure DDP + AMP work
:return:
"""
if not can_run_gpu_test():
return
reset_seed()
set_random_master_port()
hparams = get_hparams()
model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
logger = get_test_tube_logger(True)
trainer = Trainer(logger=logger,
show_progress_bar=False,
max_nb_epochs=1,
gpus=[0, 1],
distributed_backend='ddp',
use_amp=True)
with pytest.warns(UserWarning):
trainer.get_dataloaders(model)
clear_save_dir()
def test_ddp_sampler_error():
"""
Make sure DDP + AMP work
:return:
"""
if not torch.cuda.is_available():
warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
return
if not torch.cuda.device_count() > 1:
warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
return
os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
hparams = get_hparams()
model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
exp = get_exp(True)
exp.save()
trainer = Trainer(
experiment=exp,
progress_bar=False,
max_nb_epochs=1,
gpus=[0, 1],
distributed_backend='ddp',
use_amp=True
)
with pytest.warns(UserWarning):
trainer.get_dataloaders(model)
clear_save_dir()
def test_ddp_sampler_error(tmpdir):
"""Make sure DDP + AMP work."""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams, force_remove_distributed_sampler=True)
logger = tutils.get_test_tube_logger(tmpdir, True)
trainer = Trainer(logger=logger,
show_progress_bar=False,
max_epochs=1,
gpus=[0, 1],
distributed_backend='ddp',
precision=16)
with pytest.warns(UserWarning):
trainer.get_dataloaders(model)
