default=False,
action='store_true',
help="set small or large embedding size")
group.add_argument('--use-ipu-model',
default=False,
action='store_true',
help="use IPU model or not.")
return parser
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="CTR Model Training in Tensorflow")
parser = add_model_arguments(parser)
parser = add_dataset_arguments(parser)
parser = add_training_arguments(parser)
parser = logger.add_arguments(parser)
args, unknown = parser.parse_known_args()
args = vars(args)
seed = args['seed']
if seed is not None:
tf.compat.v1.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
utils.reset_ipu_seed(seed)
logger.print_setting(args)
setup_logger(logging.INFO, tf_log)
train_process(args)
help="Replicate graph over N workers to increase batch to batch-size*N"
)
group.add_argument('--model-path',
type=str,
default='./dnn_save_path/ckpt_noshuffDIEN3',
help='Place to store and restore model')
group.add_argument('--use-ipu-model',
default=False,
action='store_true',
help="use IPU model or not.")
group.add_argument('--use-ipu-emb',
default=False,
action='store_true',
help="Use host embeddig or put embedding on ipu.")
return parser
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="CTR Model Training in Tensorflow", add_help=False)
parser = add_model_arguments(parser)
parser = add_dataset_arguments(parser)
parser = add_training_arguments(parser)
parser = logger.add_arguments(parser)
args, _ = parser.parse_known_args()
args = vars(args)
logger.print_setting(args, is_dien=False, is_training=False)
setup_logger(logging.DEBUG, tf_log, name='dien_log.txt')
inference(args)