def main():
from bert_serving.server import BertServer
from bert_serving.server.helper import get_run_args
args = get_run_args()
server = BertServer(args)
server.start()
server.join()
def main():
from bert_serving.server import BertServer
from bert_serving.server.helper import get_run_args
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
with BertServer(get_run_args()) as server:
server.join()
def optimize():
logger = set_logger(colored('OPTIMIZER', 'magenta'))
arg = get_run_args(get_optimizer_args_parser)
temporary_file_name, config = optimize_graph(arg)
optimized_graph_filepath = os.path.join(arg.model_dir,
"optimized_graph.pbtxt")
tf.gfile.Rename(temporary_file_name,
optimized_graph_filepath,
overwrite=True)
logger.info(f"Serialized graph to {optimized_graph_filepath}")
import sys
from bert_serving.server import BertServer
from bert_serving.server.helper import get_run_args
if __name__ == '__main__':
args = get_run_args()
server = BertServer(args)
server.start()
server.join()
def main():
from bert_serving.server import BertServer
from bert_serving.server.helper import get_run_args
with BertServer(get_run_args()) as server:
server.join()
def terminate():
from bert_serving.server import BertServer
from bert_serving.server.helper import get_run_args, get_shutdown_parser
args = get_run_args(get_shutdown_parser)
BertServer.shutdown(args)
def benchmark():
from bert_serving.server.benchmark import run_benchmark
from bert_serving.server.helper import get_run_args, get_benchmark_parser
args = get_run_args(get_benchmark_parser)
run_benchmark(args)
def terminate():
args = get_run_args(get_shutdown_parser)
BertServer.shutdown(args)
def benchmark():
args = get_run_args(get_benchmark_parser)
run_benchmark(args)
def main():
with BertServer(get_run_args()) as server:
server.join()
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
sys.path.append("..")
from bert_serving.server import BertServer
from bert_serving.server.helper import get_run_args
if __name__ == "__main__":
with BertServer(get_run_args()) as server:
server.join()
for vec in bc.encode(buffer):
features = {'features': create_float_feature(vec)}
tf_example = tf.train.Example(features=tf.train.Features(
feature=features))
writer.write(tf_example.SerializeToString())
buffer.clear()
num_examples = 0
with open(args.in_file) as fp, tf.python_io.TFRecordWriter(
args.out_file) as writer:
bc = BertClient(args.ip, args.port, args.port_out)
buffer = []
for v in fp:
if v.strip():
buffer.append(v.strip())
if len(buffer) > args.batch_size:
encode_write()
num_examples += len(buffer)
if num_examples >= args.max_num_line:
break
if buffer and num_examples + len(buffer) < args.max_num_line:
encode_write()
if __name__ == '__main__':
args = get_run_args(get_args_parser)
run(args)
