def resnet_cifar10(train_data,
test_data,
mean_data,
network_name,
epoch_size,
num_quantization_bits=32,
block_size=3200,
warm_up=0,
max_epochs=5,
restore=True,
log_to_file=None,
num_mbs_per_log=None,
gen_heartbeat=False,
scale_up=False,
profiling=False):
set_computation_network_trace_level(0)
# NOTE: scaling up minibatch_size increases sample throughput. In 8-GPU machine,
# ResNet110 samples-per-second is ~7x of single GPU, comparing to ~3x without scaling
# up. However, bigger minimatch size on the same number of samples means less updates,
# thus leads to higher training error. This is a trade-off of speed and accuracy
minibatch_size = 128 * (Communicator.num_workers() if scale_up else 1)
progress_printer = ProgressPrinter(freq=num_mbs_per_log,
tag='Training',
log_to_file=log_to_file,
rank=Communicator.rank(),
gen_heartbeat=gen_heartbeat,
num_epochs=max_epochs)
network = create_resnet_network(network_name)
trainer = create_trainer(network, minibatch_size, epoch_size,
num_quantization_bits, block_size, warm_up,
progress_printer)
train_source = create_image_mb_source(train_data,
mean_data,
train=True,
total_number_of_samples=max_epochs *
epoch_size)
test_source = create_image_mb_source(
test_data,
mean_data,
train=False,
total_number_of_samples=cntk.io.FULL_DATA_SWEEP)
train_and_test(network, trainer, train_source, test_source, minibatch_size,
epoch_size, profiling)
def resnet_cifar10(train_data, test_data, mean_data, network_name, epoch_size, num_quantization_bits=32, block_size=3200, warm_up=0, max_epochs=5, log_to_file=None, num_mbs_per_log=None, gen_heartbeat=False, scale_up=False, profiling=False):
set_computation_network_trace_level(0)
# NOTE: scaling up minibatch_size increases sample throughput. In 8-GPU machine,
# ResNet110 samples-per-second is ~7x of single GPU, comparing to ~3x without scaling
# up. However, bigger minimatch size on the same number of samples means less updates,
# thus leads to higher training error. This is a trade-off of speed and accuracy
minibatch_size = 128 * (Communicator.num_workers() if scale_up else 1)
progress_printer = ProgressPrinter(
freq=num_mbs_per_log,
tag='Training',
log_to_file=log_to_file,
rank=Communicator.rank(),
gen_heartbeat=gen_heartbeat,
num_epochs=max_epochs)
network = create_resnet_network(network_name)
trainer = create_trainer(network, minibatch_size, epoch_size, num_quantization_bits, block_size, warm_up)
train_source = create_image_mb_source(train_data, mean_data, train=True, total_number_of_samples=max_epochs * epoch_size)
test_source = create_image_mb_source(test_data, mean_data, train=False, total_number_of_samples=cntk.io.FULL_DATA_SWEEP)
train_and_test(network, trainer, train_source, test_source, progress_printer, minibatch_size, epoch_size, profiling)
