def main():
prepare_dir() # create the vocab dir and model dir
network = create_model(vocab_sqrt)
if opt.pre_model:
network['model'].restore(opt.pre_model)
log_number_of_parameters(network['model'])
location_path = os.path.join(opt.vocabdir, opt.alloc_file)
for i in range(len(opt.epochs)):
train(network, location_path, i)
location_path = get_k_round_location_path(i + 1)
Communicator.finalize()
def main():
prepare_dir() # create the vocab dir and model dir
network = create_model(vocab_sqrt)
if opt.pre_model:
network['model'].restore(opt.pre_model)
log_number_of_parameters(network['model'])
location_path = os.path.join(opt.vocabdir, opt.alloc_file)
for i in range(len(opt.epochs)):
train(network, location_path, i)
location_path = get_k_round_location_path(i + 1)
Communicator.finalize()
model_path = args['outputdir'] + "/models"
if args['logdir'] is not None:
log_dir = args['logdir']
if args['device'] is not None:
cntk.device.try_set_default_device(cntk.device.gpu(args['device']))
data_path = args['datadir']
if not os.path.isdir(data_path):
raise RuntimeError("Directory %s does not exist" % data_path)
os.chdir(data_path)
mean_data = os.path.join(data_path, 'ImageNet1K_mean.xml')
train_data = os.path.join(data_path, 'train_map.txt')
test_data = os.path.join(data_path, 'val_map.txt')
bn_inception_train_and_eval(train_data, test_data, mean_data,
epoch_size=args['epoch_size'],
num_quantization_bits=args['quantized_bits'],
max_epochs=args['num_epochs'],
minibatch_size=args["minibatch_size"],
restore=not args['restart'],
log_to_file=args['logdir'],
num_mbs_per_log=100,
gen_heartbeat=True,
scale_up=bool(args['scale_up']))
# Must call MPI finalize when process exit without exceptions
Communicator.finalize()
if args['outputdir'] is not None:
model_path = args['outputdir'] + "/models"
if args['logdir'] is not None:
log_dir = args['logdir']
if args['device'] is not None:
cntk.device.try_set_default_device(cntk.device.gpu(args['device']))
data_path = args['datadir']
if not os.path.isdir(data_path):
raise RuntimeError("Directory %s does not exist" % data_path)
os.chdir(data_path)
mean_data = os.path.join(data_path, 'CIFAR-10_mean.xml')
train_data = os.path.join(data_path, 'train_map.txt')
test_data = os.path.join(data_path, 'test_map.txt')
bn_inception_train_and_eval(train_data, test_data, mean_data,
epoch_size=args['epoch_size'],
num_quantization_bits=args['quantized_bits'],
max_epochs=args['num_epochs'],
minibatch_size=args["minibatch_size"],
restore=not args['restart'],
log_to_file=args['logdir'],
num_mbs_per_log=100,
gen_heartbeat=True,
scale_up=bool(args['scale_up']),
profiling=args['profile'])
Communicator.finalize()
def finalize(self):
if self._distributed:
Communicator.finalize()