def train_and_evaluate(data_path, total_epochs, gpu_count=1):
# Create distributed communicator for 1-bit SGD for better scaling to multiple GPUs
# If you'd like to avoid quantization loss, use simple one instead
quantization_bit = 1
if (quantization_bit == 32):
communicator = distributed.mpi_communicator()
else:
communicator = distributed.quantized_mpi_communicator(quantization_bit)
workers = communicator.workers()
current_worker = communicator.current_worker()
print("List all distributed workers")
for wk in workers:
if current_worker.global_rank == wk.global_rank:
print("* {} {}".format(wk.global_rank, wk.host_id))
else:
print(" {} {}".format(wk.global_rank, wk.host_id))
if gpu_count == 1 and len(workers) > 1:
print("Warning: running distributed training on 1-GPU will be slow")
device.set_default_device(gpu(0))
print("Training on device type:{} id:{}".format(
'gpu' if device.default().type() else 'cpu',
device.default().id()))
start_model = "start_model.bin"
num_start_epochs = 1
num_parallel_epochs = total_epochs - num_start_epochs
# training the start model only in one worker
if communicator.current_worker().global_rank == 0:
cifar_resnet_distributed(data_path,
save_model_filename=start_model,
communicator=None,
run_test=False,
num_epochs=num_start_epochs)
communicator.barrier()
# train in parallel
error = cifar_resnet_distributed(data_path,
load_model_filename=start_model,
communicator=communicator,
run_test=True,
num_epochs=num_parallel_epochs)
distributed.Communicator.finalize()
return error
def test_get_data_type():
assert get_data_type(constant(value=2), constant(value=1)) == np.float32
assert get_data_type(input_variable(shape=(2,3)), constant(value=1)) == np.float32
ndav32 = create_NDArrayView_from_NumPy(np.asarray([[1,2]], dtype=np.float32))
assert get_data_type(input_variable(shape=(2,3), data_type=np.float64),
ndav32) == np.float64
ndav64 = create_NDArrayView_from_NumPy(np.asarray([[1,2]],
dtype=np.float64))
assert get_data_type(input_variable(shape=(2,3), data_type=np.float64),
ndav64) == np.float64
val32 = create_Value_from_NumPy(np.asarray([[1,2]], dtype=np.float32),
dev = default())
assert get_data_type(val32, ndav64) == np.float64
def test_get_data_type():
assert get_data_type(constant(value=2), constant(value=1)) == np.float32
assert get_data_type(input_variable(shape=(2, 3)),
constant(value=1)) == np.float32
ndav32 = create_NDArrayView_from_NumPy(
np.asarray([[1, 2]], dtype=np.float32))
assert get_data_type(input_variable(shape=(2, 3), data_type=np.float64),
ndav32) == np.float64
ndav64 = create_NDArrayView_from_NumPy(
np.asarray([[1, 2]], dtype=np.float64))
assert get_data_type(input_variable(shape=(2, 3), data_type=np.float64),
ndav64) == np.float64
val32 = create_Value_from_NumPy(np.asarray([[1, 2]], dtype=np.float32),
dev=default())
assert get_data_type(val32, ndav64) == np.float64
def train_and_evaluate(data_path, total_epochs, gpu_count=1):
# Create distributed communicator for 1-bit SGD for better scaling to multiple GPUs
# If you'd like to avoid quantization loss, use simple one instead
quantization_bit = 1
if (quantization_bit == 32):
communicator = distributed.mpi_communicator()
else:
communicator = distributed.quantized_mpi_communicator(quantization_bit)
workers = communicator.workers()
current_worker = communicator.current_worker()
print("List all distributed workers")
for wk in workers:
if current_worker.global_rank == wk.global_rank:
print("* {} {}".format(wk.global_rank, wk.host_id))
else:
print(" {} {}".format(wk.global_rank, wk.host_id))
if gpu_count == 1 and len(workers) > 1 :
print("Warning: running distributed training on 1-GPU will be slow")
device.set_default_device(gpu(0))
print("Training on device type:{} id:{}".format('gpu' if device.default().type() else 'cpu', device.default().id()))
start_model = "start_model.bin"
num_start_epochs = 1
num_parallel_epochs = total_epochs - num_start_epochs
# training the start model only in one worker
if communicator.current_worker().global_rank == 0:
cifar_resnet_distributed(data_path, save_model_filename=start_model, communicator=None, run_test=False, num_epochs=num_start_epochs)
communicator.barrier()
# train in parallel
error = cifar_resnet_distributed(data_path, load_model_filename=start_model, communicator=communicator, run_test=True, num_epochs=num_parallel_epochs)
distributed.Communicator.finalize()
return error
*"../../../../Examples/Image/DataSets/CIFAR-10/".split("/"))))
os.chdir(data_path)
# Create distributed communicator for 1-bit SGD
communicator = distributed.communicator(distributed.quantized_mpi_communicator(1))
workers = communicator.workers()
current_worker = communicator.current_worker()
print("List all distributed workers")
for wk in workers:
if current_worker.global_rank == wk.global_rank:
print("* {} {}".format(wk.global_rank, wk.host_id))
else:
print(" {} {}".format(wk.global_rank, wk.host_id))
print("Training on device type:{} id:{}".format('gpu' if device.default().type() else 'cpu', device.default().id()))
start_model = "start_model.bin"
num_start_epochs = 1
num_parallel_epochs = 10
# training the start model only in one worker
if communicator.current_worker().global_rank == 0:
cifar_resnet(data_path, save_model_filename=start_model, communicator=None, run_test=False, num_epochs=num_start_epochs)
communicator.barrier()
# train in parallel
error = cifar_resnet(data_path, load_model_filename=start_model, communicator=communicator, run_test=True, num_epochs=num_parallel_epochs)
print("Error: %f" % error)
os.chdir(data_path)
# Create distributed communicator for 1-bit SGD
communicator = distributed.communicator(
distributed.quantized_mpi_communicator(1))
workers = communicator.workers()
current_worker = communicator.current_worker()
print("List all distributed workers")
for wk in workers:
if current_worker.global_rank == wk.global_rank:
print("* {} {}".format(wk.global_rank, wk.host_id))
else:
print(" {} {}".format(wk.global_rank, wk.host_id))
print("Training on device type:{} id:{}".format(
'gpu' if device.default().type() else 'cpu',
device.default().id()))
start_model = "start_model.bin"
num_start_epochs = 1
num_parallel_epochs = 10
# training the start model only in one worker
if communicator.current_worker().global_rank == 0:
cifar_resnet(data_path,
save_model_filename=start_model,
communicator=None,
run_test=False,
num_epochs=num_start_epochs)
communicator.barrier()
