def _new_trainer():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
train_dataset = serialize(MNIST,
root='data/mnist',
train=True,
download=True,
transform=transform)
test_dataset = serialize(MNIST,
root='data/mnist',
train=False,
download=True,
transform=transform)
multi_module = MultiModelSupervisedLearningModule(
nn.CrossEntropyLoss, {'acc': pl._AccuracyWithLogits})
lightning = pl.Lightning(multi_module,
cgo_trainer.Trainer(use_cgo=True,
max_epochs=1,
limit_train_batches=0.25,
progress_bar_refresh_rate=0),
train_dataloader=pl.DataLoader(train_dataset,
batch_size=100),
val_dataloaders=pl.DataLoader(test_dataset,
batch_size=100))
return lightning
def test_multi_model_trainer_gpu(self):
_reset()
if not (torch.cuda.is_available() and torch.cuda.device_count() >= 2):
pytest.skip('test requires GPU and torch+cuda')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = serialize(MNIST,
root='data/mnist',
train=True,
download=True,
transform=transform)
test_dataset = serialize(MNIST,
root='data/mnist',
train=False,
download=True,
transform=transform)
multi_module = _MultiModelSupervisedLearningModule(
nn.CrossEntropyLoss, {'acc': pl._AccuracyWithLogits}, n_models=2)
lightning = pl.Lightning(multi_module,
cgo_trainer.Trainer(use_cgo=True,
max_epochs=1,
limit_train_batches=0.25),
train_dataloader=pl.DataLoader(
train_dataset, batch_size=100),
val_dataloaders=pl.DataLoader(test_dataset,
batch_size=100))
lightning._execute(_model_gpu)
result = _get_final_result()
assert len(result) == 2
for _ in result:
assert _ > 0.8