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提交 748a8e41 编写于 作者: M MRXLT 提交者： GitHub
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Merge pull request #650 from MRXLT/ce-script 

CE script
上级 6c9aeae1 66a1c4ce
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Showing with 118 addition and 138 deletion
python/examples/bert/benchmark.py
浏览文件 @ 748a8e41
......@@ -21,11 +21,7 @@ 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
from batching import pad_batch_data
import tokenization
import requests
import json
from paddle_serving_client.utils import benchmark_args, show_latency
from paddle_serving_app.reader import ChineseBertReader
args = benchmark_args()
......@@ -36,42 +32,75 @@ def single_func(idx, resource):
dataset = []
for line in fin:
dataset.append(line.strip())
profile_flags = False
latency_flags = False
if os.getenv("FLAGS_profile_client"):
profile_flags = True
if os.getenv("FLAGS_serving_latency"):
latency_flags = True
latency_list = []
if args.request == "rpc":
reader = ChineseBertReader(vocab_file="vocab.txt", max_seq_len=20)
reader = ChineseBertReader({"max_seq_len": 128})
fetch = ["pooled_output"]
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:
feed_dict = reader.process(dataset[i])
result = client.predict(feed=feed_dict, fetch=fetch)
for i in range(turns):
if args.batch_size >= 1:
l_start = time.time()
feed_batch = []
b_start = time.time()
for bi in range(args.batch_size):
feed_batch.append(reader.process(dataset[bi]))
b_end = time.time()
if profile_flags:
sys.stderr.write(
"PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}\n".format(
os.getpid(),
int(round(b_start * 1000000)),
int(round(b_end * 1000000))))
result = client.predict(feed=feed_batch, fetch=fetch)
l_end = time.time()
if latency_flags:
latency_list.append(l_end * 1000 - l_start * 1000)
else:
print("unsupport batch size {}".format(args.batch_size))
elif args.request == "http":
start = time.time()
header = {"Content-Type": "application/json"}
for i in range(1000):
dict_data = {"words": dataset[i], "fetch": ["pooled_output"]}
r = requests.post(
'http://{}/bert/prediction'.format(resource["endpoint"][
idx % len(resource["endpoint"])]),
data=json.dumps(dict_data),
headers=header)
raise ("not implemented")
end = time.time()
return [[end - start]]
if latency_flags:
return [[end - start], latency_list]
else:
return [[end - start]]
if __name__ == '__main__':
multi_thread_runner = MultiThreadRunner()
endpoint_list = ["127.0.0.1:9292"]
result = multi_thread_runner.run(single_func, args.thread,
{"endpoint": endpoint_list})
endpoint_list = [
"127.0.0.1:9292", "127.0.0.1:9293", "127.0.0.1:9294", "127.0.0.1:9295"
]
turns = 10
start = time.time()
result = multi_thread_runner.run(
single_func, args.thread, {"endpoint": endpoint_list,
"turns": turns})
end = time.time()
total_cost = end - start
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))
print("total cost :{} s".format(total_cost))
print("each thread cost :{} s. ".format(avg_cost))
print("qps :{} samples/s".format(args.batch_size * args.thread * turns /
total_cost))
if os.getenv("FLAGS_serving_latency"):
show_latency(result[1])
python/examples/bert/benchmark.sh
浏览文件 @ 748a8e41
rm profile_log
for thread_num in 1 2 4 8 16
export CUDA_VISIBLE_DEVICES=0,1,2,3
export FLAGS_profile_server=1
export FLAGS_profile_client=1
export FLAGS_serving_latency=1
python3 -m paddle_serving_server_gpu.serve --model $1 --port 9292 --thread 4 --gpu_ids 0,1,2,3 --mem_optim False --ir_optim True 2> elog > stdlog &
sleep 5
#warm up
python3 benchmark.py --thread 8 --batch_size 1 --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
for thread_num in 4 8 16
do
$PYTHONROOT/bin/python benchmark.py --thread $thread_num --model serving_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
echo "========================================"
echo "batch size : $batch_size" >> profile_log
$PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
tail -n 1 profile >> profile_log
for batch_size in 1 4 16 64 256
do
python3 benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo "model name :" $1
echo "thread num :" $thread_num
echo "batch size :" $batch_size
echo "=================Done===================="
echo "model name :$1" >> profile_log_$1
echo "batch size :$batch_size" >> profile_log_$1
python3 ../util/show_profile.py profile $thread_num >> profile_log_$1
tail -n 8 profile >> profile_log_$1
echo "" >> profile_log_$1
done
done
ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/bert/benchmark_batch.py 已删除 100644 → 0
浏览文件 @ 6c9aeae1
# -*- 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
from batching import pad_batch_data
import tokenization
import requests
import json
from bert_reader import BertReader
args = benchmark_args()
def single_func(idx, resource):
fin = open("data-c.txt")
dataset = []
for line in fin:
dataset.append(line.strip())
profile_flags = False
if os.environ["FLAGS_profile_client"]:
profile_flags = True
if args.request == "rpc":
reader = BertReader(vocab_file="vocab.txt", max_seq_len=20)
fetch = ["pooled_output"]
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:
feed_batch = []
b_start = time.time()
for bi in range(args.batch_size):
feed_batch.append(reader.process(dataset[bi]))
b_end = time.time()
if profile_flags:
print("PROFILE\tpid:{}\tbert_pre_0:{} bert_pre_1:{}".format(
os.getpid(),
int(round(b_start * 1000000)),
int(round(b_end * 1000000))))
result = client.predict(feed=feed_batch, fetch=fetch)
else:
print("unsupport batch size {}".format(args.batch_size))
elif args.request == "http":
raise ("no batch predict for http")
end = time.time()
return [[end - start]]
if __name__ == '__main__':
multi_thread_runner = MultiThreadRunner()
endpoint_list = ["127.0.0.1:9292"]
result = multi_thread_runner.run(single_func, args.thread,
{"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))
python/examples/bert/benchmark_batch.sh 已删除 100644 → 0
浏览文件 @ 6c9aeae1
rm profile_log
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle_serving_server_gpu.serve --model bert_seq20_model/ --port 9295 --thread 4 --gpu_ids 0,1,2,3 2> elog > stdlog &
sleep 5
for thread_num in 1 2 4 8 16
do
for batch_size in 1 2 4 8 16 32 64 128 256 512
do
$PYTHONROOT/bin/python benchmark_batch.py --thread $thread_num --batch_size $batch_size --model serving_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
echo "========================================"
echo "thread num: ", $thread_num
echo "batch size: ", $batch_size
echo "batch size : $batch_size" >> profile_log
$PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
tail -n 1 profile >> profile_log
done
done
python/examples/imagenet/benchmark.py
浏览文件 @ 748a8e41
......@@ -93,7 +93,7 @@ def single_func(idx, resource):
if __name__ == '__main__':
multi_thread_runner = MultiThreadRunner()
endpoint_list = ["127.0.0.1:9696"]
endpoint_list = ["127.0.0.1:9393"]
#endpoint_list = endpoint_list + endpoint_list + endpoint_list
result = multi_thread_runner.run(single_func, args.thread,
{"endpoint": endpoint_list})
......
python/examples/imagenet/benchmark.sh
浏览文件 @ 748a8e41
rm profile_log
for thread_num in 1 2 4 8
export CUDA_VISIBLE_DEVICES=0,1,2,3
export FLAGS_profile_server=1
export FLAGS_profile_client=1
python -m paddle_serving_server_gpu.serve --model $1 --port 9292 --thread 4 --gpu_ids 0,1,2,3 2> elog > stdlog &
sleep 5
#warm up
$PYTHONROOT/bin/python benchmark.py --thread 8 --batch_size 1 --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
for thread_num in 4 8 16
do
for batch_size in 1 2 4 8 16 32 64 128
for batch_size in 1 4 16 64 256
do
$PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model ResNet50_vd_client_config/serving_client_conf.prototxt --request rpc > profile 2>&1
echo "========================================"
echo "batch size : $batch_size" >> profile_log
$PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo "model name :" $1
echo "thread num :" $thread_num
echo "batch size :" $batch_size
echo "=================Done===================="
echo "model name :$1" >> profile_log
echo "batch size :$batch_size" >> profile_log
$PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
tail -n 1 profile >> profile_log
tail -n 8 profile >> profile_log
done
done
ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/util/show_profile.py
浏览文件 @ 748a8e41
......@@ -31,7 +31,7 @@ with open(profile_file) as f:
if line[0] == "PROFILE":
prase(line[2])
print("thread num {}".format(thread_num))
print("thread num :{}".format(thread_num))
for name in time_dict:
print("{} cost {} s in each thread ".format(name, time_dict[name] / (
print("{} cost :{} s in each thread ".format(name, time_dict[name] / (
1000000.0 * float(thread_num))))
python/paddle_serving_client/utils/__init__.py
浏览文件 @ 748a8e41
......@@ -17,6 +17,7 @@ import sys
import subprocess
import argparse
from multiprocessing import Pool
import numpy as np
def benchmark_args():
......@@ -35,6 +36,17 @@ def benchmark_args():
return parser.parse_args()
def show_latency(latency_list):
latency_array = np.array(latency_list)
info = "latency:\n"
info += "mean :{} ms\n".format(np.mean(latency_array))
info += "median :{} ms\n".format(np.median(latency_array))
info += "80 percent :{} ms\n".format(np.percentile(latency_array, 80))
info += "90 percent :{} ms\n".format(np.percentile(latency_array, 90))
info += "99 percent :{} ms\n".format(np.percentile(latency_array, 99))
sys.stderr.write(info)
class MultiThreadRunner(object):
def __init__(self):
pass
......
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