def finalize(self): if self._distributed: Communicator.finalize()
train_data = os.path.join(data_path, 'train_map.txt') test_data = os.path.join(data_path, 'test_map.txt') num_quantization_bits = args['quantized_bits'] epochs = args['epochs'] warm_up = args['distributed_after'] network_name = args['network'] scale_up = bool(args['scale_up']) # Create distributed trainer factory print( "Start training: quantize_bit = {}, epochs = {}, distributed_after = {}" .format(num_quantization_bits, epochs, warm_up)) try: resnet_cifar10(train_data, test_data, mean_data, network_name, epoch_size, num_quantization_bits, block_size=args['block_samples'], warm_up=args['distributed_after'], max_epochs=epochs, scale_up=scale_up, log_to_file=args['logdir'], profiling=args['profile']) finally: # Must call MPI finalize when process exit Communicator.finalize()
if args['epoch_size'] is not None: epoch_size = args['epoch_size'] 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') num_quantization_bits = args['quantized_bits'] epochs = args['epochs'] warm_up = args['distributed_after'] network_name = args['network'] scale_up = bool(args['scale_up']) # Create distributed trainer factory print("Start training: quantize_bit = {}, epochs = {}, distributed_after = {}".format(num_quantization_bits, epochs, warm_up)) try: resnet_cifar10(train_data, test_data, mean_data, network_name, epoch_size, num_quantization_bits, block_size=args['block_samples'], warm_up=args['distributed_after'], max_epochs=epochs, scale_up=scale_up, log_to_file=args['logdir'], profiling=args['profile']) finally: # Must call MPI finalize when process exit Communicator.finalize()