def main():
conf = Configs()
experiment.create(name='probabilities_fixed_cards')
experiment.calculate_configs(conf, {}, ['run'])
experiment.add_pytorch_models(dict(model=conf.model))
experiment.start()
conf.run()
def main():
conf = Configs()
experiment.create(name='who_won')
experiment.calculate_configs(conf, {}, ['run'])
experiment.add_pytorch_models(dict(model=conf.model))
experiment.start()
conf.run()
def main():
conf = Configs()
experiment.create(name='mnist_latest')
conf.optimizer = 'adam_optimizer'
experiment.calculate_configs(conf, {}, ['set_seed', 'run'])
experiment.add_pytorch_models(dict(model=conf.model))
experiment.start()
conf.run()
def main():
conf = Configs()
experiment.create(name='cifar_10', writers={'sqlite'})
conf.optimizer = 'adam_optimizer'
experiment.calculate_configs(conf, {}, ['set_seed', 'run'])
experiment.add_pytorch_models(dict(model=conf.model))
experiment.start()
conf.run()
def search(conf: Configs):
tracker.set_global_step(0)
experiment.create(name='mnist_hyperparam_tuning')
experiment.calculate_configs(conf, {}, ['set_seed', 'run'])
experiment.add_pytorch_models(dict(model=conf.model))
experiment.start()
conf.run()
tracker.reset()
def main():
conf = Configs()
experiment.create(name='sklearn', writers={'sqlite'})
experiment.calculate_configs(conf)
experiment.add_sklearn_models(dict(model=conf.model))
experiment.start()
conf.run()
experiment.save_checkpoint()
def main():
conf = Configs()
experiment.create(name='mnist_configs', writers={'sqlite'})
conf.optimizer = 'sgd_optimizer'
experiment.calculate_configs(conf,
{},
['set_seed', 'loop'])
experiment.add_pytorch_models(dict(model=conf.model))
experiment.start()
conf.loop()
def main():
conf = Configs()
experiment.create(name='configs')
experiment.calculate_configs(conf, {'optimizer': 'sgd_optimizer'},
['set_seed', 'run'])
experiment.start()
conf.run()
# save the model
experiment.save_checkpoint()
def main():
conf = Configs()
experiment.create(name='Battleship_DQN')
experiment.calculate_configs(conf,
{},
['set_seed', 'policy', 'target', 'run'])
experiment.add_pytorch_models(dict(model=conf.policy))
experiment.start()
conf.run()
if conf.is_save_models:
experiment.save_checkpoint()
def main():
experiment.create()
conf = Configs()
conf.learning_rate = 1e-4
conf.epochs = 500
conf.conv_sizes = [(128, 2), (256, 4)]
# conf.conv_sizes = [(128, 1), (256, 2)]
conf.activation = 'relu'
conf.dropout = 0.1
conf.train_batch_size = 32
experiment.calculate_configs(conf, 'run')
experiment.start()
with tracker.namespace('valid'):
conf.valid_dataset.save_artifacts()
conf.run()
def main():
conf = Configs()
experiment.create(name='test_artifacts', writers={'sqlite'})
experiment.calculate_configs(conf, 'run')
experiment.start()
conf.run()
def main():
# set indicator types
tracker.set_queue("train_loss", 20, True)
tracker.set_histogram("valid_loss", True)
tracker.set_scalar("valid_accuracy", True)
epochs = 10
train_batch_size = 64
test_batch_size = 1000
use_cuda = True
cuda_device = 0
seed = 5
train_log_interval = 10
learning_rate = 0.01
# get device
is_cuda = use_cuda and torch.cuda.is_available()
if not is_cuda:
device = torch.device("cpu")
else:
if cuda_device < torch.cuda.device_count():
device = torch.device(f"cuda:{cuda_device}")
else:
print(f"Cuda device index {cuda_device} higher than "
f"device count {torch.cuda.device_count()}")
device = torch.device(f"cuda:{torch.cuda.device_count() - 1}")
# data transform
data_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
# train loader
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
str(lab.get_data_path()),
train=True,
download=True,
transform=data_transform),
batch_size=train_batch_size,
shuffle=True)
# test loader
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
str(lab.get_data_path()),
train=False,
download=True,
transform=data_transform),
batch_size=test_batch_size,
shuffle=False)
# model
model = Net().to(device)
# optimizer
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# set seeds
torch.manual_seed(seed)
# only for logging purposes
configs = {
'epochs': epochs,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'use_cuda': use_cuda,
'cuda_device': cuda_device,
'seed': seed,
'train_log_interval': train_log_interval,
'learning_rate': learning_rate,
'device': device,
'train_loader': train_loader,
'test_loader': test_loader,
'model': model,
'optimizer': optimizer,
}
# create the experiment
experiment.create(name='tracker')
# experiment configs
experiment.calculate_configs(configs)
# pyTorch model
experiment.add_pytorch_models(dict(model=model))
experiment.start()
# training loop
for epoch in range(1, epochs + 1):
train(model, optimizer, train_loader, device, train_log_interval)
test(model, test_loader, device)
logger.log()
# save the model
experiment.save_checkpoint()
def main():
conf = Configs()
experiment.create(name='mnist_gan', writers={'sqlite'})
experiment.calculate_configs(conf, {}, ['set_seed', 'main'])
experiment.start()
conf.main()
def main():
conf = Configs()
experiment.create(name='rnn', writers={'sqlite', 'tensorboard'})
experiment.calculate_configs(conf, {}, run_order=['set_seed', 'main'])
experiment.start()
conf.main()