_DEFAULT_KERNEL_SIZES, _DEFAULT_NUM_LAYERS,
_DEFAULT_SKIPS)
trainer = PyTorchImageClassificationTrainer(
base_model,
dataset_cls="CIFAR10",
dataset_kwargs={
"root": "data/cifar10",
"download": True
},
dataloader_kwargs={"batch_size": 32},
optimizer_kwargs={"lr": 1e-3},
trainer_kwargs={"max_epochs": 1})
# new interface
applied_mutators = []
applied_mutators.append(BlockMutator('mutable_0'))
applied_mutators.append(BlockMutator('mutable_1'))
simple_startegy = TPEStrategy()
exp = RetiariiExperiment(base_model, trainer, applied_mutators,
simple_startegy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnasnet_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8081)
test_dataset = serialize(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)
trainer = cgo.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)
applied_mutators = [BlockMutator('mutable_0'), BlockMutator('mutable_1')]
simple_strategy = TPEStrategy()
exp = RetiariiExperiment(base_model, trainer, applied_mutators,
simple_strategy)
exp_config = RetiariiExeConfig('remote')
exp_config.experiment_name = 'darts_search'
exp_config.trial_concurrency = 3
exp_config.max_trial_number = 10
exp_config.trial_gpu_number = 1
exp_config.training_service.reuse_mode = True
rm_conf = RemoteMachineConfig()
rm_conf.host = '127.0.0.1'
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
valid_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
train_dataset = serialize(CIFAR10, root='data/cifar10', train=True, download=True, transform=train_transform)
test_dataset = serialize(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)
applied_mutators = [
BlockMutator('mutable_0'),
BlockMutator('mutable_1')
]
simple_strategy = TPEStrategy()
exp = RetiariiExperiment(base_model, trainer, applied_mutators, simple_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnasnet_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8097)