提交 76ae32df 编写于 作者: G guru4elephant

refine benchmark scripts

上级 b9ab5a9b
# 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.
import sys
from paddle_serving_client import Client
from paddle_serving_client.metric import auc
from paddle_serving_client.utils import MultiThreadRunner
import time
def predict(thr_id, resource):
client = Client()
client.load_client_config(resource["conf_file"])
client.connect(resource["server_endpoint"])
thread_num = resource["thread_num"]
file_list = resource["filelist"]
line_id = 0
prob = []
label_list = []
dataset = []
for fn in file_list:
fin = open(fn)
for line in fin:
if line_id % thread_num == thr_id - 1:
group = line.strip().split()
words = [int(x) for x in group[1:int(group[0])]]
label = [int(group[-1])]
feed = {"words": words, "label": label}
dataset.append(feed)
line_id += 1
fin.close()
start = time.time()
fetch = ["acc", "cost", "prediction"]
infer_time_list = []
for inst in dataset:
fetch_map = client.predict(feed=inst, fetch=fetch, debug=True)
prob.append(fetch_map["prediction"][1])
label_list.append(label[0])
infer_time_list.append(fetch_map["infer_time"])
end = time.time()
client.release()
return [prob, label_list, [sum(infer_time_list)], [end - start]]
if __name__ == '__main__':
conf_file = sys.argv[1]
data_file = sys.argv[2]
resource = {}
resource["conf_file"] = conf_file
resource["server_endpoint"] = ["127.0.0.1:9292"]
resource["filelist"] = [data_file]
resource["thread_num"] = int(sys.argv[3])
thread_runner = MultiThreadRunner()
result = thread_runner.run(predict, int(sys.argv[3]), resource)
print(result[-1])
print("{}\t{}".format(sys.argv[3], sum(result[-1]) / len(result[-1])))
print("{}\t{}".format(sys.argv[3], sum(result[2]) / 1000.0 / 1000.0 / len(result[2])))
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