def system_check():
import traceback
try:
import torch
from olympus.models import Model
from olympus.optimizers import Optimizer
batch = torch.randn((32, 3, 64, 64)).cuda()
model = Model('resnet18', input_size=(3, 64, 64),
output_size=(10, )).cuda()
model.init()
optimizer = Optimizer('sgd', params=model.parameters())
optimizer.init(**optimizer.defaults)
optimizer.zero_grad()
loss = model(batch).sum()
optimizer.backward(loss)
optimizer.step()
return True
except:
error(traceback.format_exc())
return False
from olympus.optimizers import Optimizer, LRSchedule
from olympus.models import Model
from olympus.observers import ObserverList, ProgressView, Speed
from olympus.utils import fetch_device, option
epochs = 2
device = fetch_device()
base = option('base_path', '/tmp/olympus')
# Model
model = Model('resnet18', input_size=(1, 28, 28), output_size=(10, ))
# Optimizer
optimizer = Optimizer('sgd',
params=model.parameters(),
weight_decay=0.001,
lr=1e-5,
momentum=1e-5)
# Schedule
lr_schedule = LRSchedule('exponential', optimizer=optimizer, gamma=0.99)
data = Dataset('fake_mnist', path=f'{base}/data')
splits = SplitDataset(data, split_method='original')
# Dataloader
loader = DataLoader(splits, sampler_seed=1, batch_size=32)
# event handler
return [base_lr * self.gamma ** self.last_epoch
for base_lr in self.base_lrs]
@staticmethod
def get_space():
return {'gamma': 'loguniform(0.97, 1)'}
if __name__ == '__main__':
model = Model(
'logreg',
input_size=(290,),
output_size=(10,)
)
optimizer = Optimizer('sgd', params=model.parameters())
# If you use an hyper parameter optimizer, it will generate this for you
optimizer.init(lr=1e-4, momentum=0.02, weight_decay=1e-3)
schedule = LRSchedule(schedule=MyExponentialLR)
schedule.init(optimizer=optimizer, gamma=0.97)
optimizer.zero_grad()
input = torch.randn((10, 290))
output = model(input)
loss = output.sum()
loss.backward()
optimizer.step()
def setup():
model = Model('logreg', input_size=(28, ), output_size=(10, ))
optimizer = Optimizer('sgd', params=model.parameters())
optimizer.init(**optimizer.defaults)
return model, optimizer