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)