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benchmark.py
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# -*- 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))