def test_diabetes():
_reset()
nni.trial._params = {'foo': 'bar', 'parameter_id': 0}
nni.runtime.platform.test._last_metric = None
train_dataset = DiabetesDataset(train=True)
test_dataset = DiabetesDataset(train=False)
lightning = pl.Regression(
optimizer=torch.optim.SGD,
train_dataloader=pl.DataLoader(train_dataset, batch_size=20),
val_dataloaders=pl.DataLoader(test_dataset, batch_size=20),
max_epochs=100,
progress_bar_refresh_rate=progress_bar_refresh_rate)
lightning._execute(FCNet(train_dataset.x.shape[1], 1))
assert _get_final_result() < 2e4
_reset()
def test_mnist():
_reset()
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
train_dataset = bm(MNIST)(root='data/mnist',
train=True,
download=True,
transform=transform)
test_dataset = bm(MNIST)(root='data/mnist',
train=False,
download=True,
transform=transform)
lightning = pl.Classification(
train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
max_epochs=2,
limit_train_batches=0.25, # for faster training
progress_bar_refresh_rate=progress_bar_refresh_rate)
lightning._execute(MNISTModel)
assert _get_final_result() > 0.7
_reset()
])
valid_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_dataset = bm(CIFAR10)(root='data/cifar10',
train=True,
download=True,
transform=train_transform)
test_dataset = bm(CIFAR10)(root='data/cifar10',
train=False,
download=True,
transform=valid_transform)
trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset,
batch_size=100),
val_dataloaders=pl.DataLoader(test_dataset,
batch_size=100),
max_epochs=1,
limit_train_batches=0.2)
simple_startegy = RandomStrategy()
exp = RetiariiExperiment(base_model, trainer, [], simple_startegy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'darts_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.trial_gpu_number = 1
exp_config.training_service.use_active_gpu = True
