# -*- coding: utf-8 -*- # # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=doc-string-missing from __future__ import unicode_literals, absolute_import import os import sys import time from paddle_serving_client import Client from paddle_serving_client.utils import MultiThreadRunner from paddle_serving_client.utils import benchmark_args import requests import json import criteo_reader as criteo args = benchmark_args() def single_func(idx, resource): batch = 1 buf_size = 100 dataset = criteo.CriteoDataset() dataset.setup(1000001) test_filelists = [ "./raw_data/part-%d" % x for x in range(len(os.listdir("./raw_data"))) ] reader = dataset.infer_reader(test_filelists[len(test_filelists) - 40:], batch, buf_size) if args.request == "rpc": fetch = ["prob"] client = Client() client.load_client_config(args.model) client.connect([resource["endpoint"][idx % len(resource["endpoint"])]]) start = time.time() for i in range(1000): if args.batch_size == 1: data = reader().next() feed_dict = {} for i in range(1, 27): feed_dict["sparse_{}".format(i - 1)] = data[0][i] result = client.predict(feed=feed_dict, fetch=fetch) else: print("unsupport batch size {}".format(args.batch_size)) elif args.request == "http": raise ("Not support http service.") end = time.time() return [[end - start]] if __name__ == '__main__': multi_thread_runner = MultiThreadRunner() endpoint_list = ["127.0.0.1:9292"] #endpoint_list = endpoint_list + endpoint_list + endpoint_list result = multi_thread_runner.run(single_func, args.thread, {"endpoint": endpoint_list}) #result = single_func(0, {"endpoint": endpoint_list}) avg_cost = 0 for i in range(args.thread): avg_cost += result[0][i] avg_cost = avg_cost / args.thread print("average total cost {} s.".format(avg_cost))