Skip to content

S
Serving

PaddlePaddle / Serving
1 年多 前同步成功

通知 186
Star 833
Fork 253
S
Serving
提交 f2e35fae 编写于 作者: B barrierye
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/Serving into develop

上级 256baca1 6b482837
隐藏空白更改
内联 并排
Showing with 275 addition and 125 deletion
core/cube/cube-api/src/cube_cli.cpp
浏览文件 @ f2e35fae
......@@ -31,8 +31,9 @@ DEFINE_bool(print_output, false, "print output flag");
DEFINE_int32(thread_num, 1, "thread num");
std::atomic<int> g_concurrency(0);
std::vector<uint64_t> time_list;
std::vector<std::vector<uint64_t>> time_list;
std::vector<uint64_t> request_list;
int turns = 1000000 / FLAGS_batch;
namespace {
inline uint64_t time_diff(const struct timeval& start_time,
......@@ -97,7 +98,7 @@ int run(int argc, char** argv, int thread_id) {
while (g_concurrency.load() >= FLAGS_thread_num) {
}
g_concurrency++;
time_list[thread_id].resize(turns);
while (index < file_size) {
// uint64_t key = strtoul(buffer, NULL, 10);
......@@ -121,47 +122,12 @@ int run(int argc, char** argv, int thread_id) {
}
++seek_counter;
uint64_t seek_cost = time_diff(seek_start, seek_end);
seek_cost_total += seek_cost;
if (seek_cost > seek_cost_max) {
seek_cost_max = seek_cost;
}
if (seek_cost < seek_cost_min) {
seek_cost_min = seek_cost;
}
time_list[thread_id][request - 1] = seek_cost;
keys.clear();
values.clear();
}
}
/*
if (keys.size() > 0) {
int ret = 0;
values.resize(keys.size());
TIME_FLAG(seek_start);
ret = cube->seek(FLAGS_dict, keys, &values);
TIME_FLAG(seek_end);
if (ret != 0) {
LOG(WARNING) << "cube seek failed";
} else if (FLAGS_print_output) {
for (size_t i = 0; i < keys.size(); ++i) {
fprintf(stdout,
"key:%lu value:%s\n",
keys[i],
string_to_hex(values[i].buff).c_str());
}
}
++seek_counter;
uint64_t seek_cost = time_diff(seek_start, seek_end);
seek_cost_total += seek_cost;
if (seek_cost > seek_cost_max) {
seek_cost_max = seek_cost;
}
if (seek_cost < seek_cost_min) {
seek_cost_min = seek_cost;
}
}
*/
g_concurrency--;
// fclose(key_file);
......@@ -171,12 +137,6 @@ int run(int argc, char** argv, int thread_id) {
LOG(WARNING) << "destroy cube api failed err=" << ret;
}
uint64_t seek_cost_avg = seek_cost_total / seek_counter;
LOG(INFO) << "seek cost avg = " << seek_cost_avg;
LOG(INFO) << "seek cost max = " << seek_cost_max;
LOG(INFO) << "seek cost min = " << seek_cost_min;
time_list[thread_id] = seek_cost_avg;
request_list[thread_id] = request;
return 0;
......@@ -188,6 +148,7 @@ int run_m(int argc, char** argv) {
request_list.resize(thread_num);
time_list.resize(thread_num);
std::vector<std::thread*> thread_pool;
TIME_FLAG(main_start);
for (int i = 0; i < thread_num; i++) {
thread_pool.push_back(new std::thread(run, argc, argv, i));
}
......@@ -195,27 +156,33 @@ int run_m(int argc, char** argv) {
thread_pool[i]->join();
delete thread_pool[i];
}
TIME_FLAG(main_end);
uint64_t sum_time = 0;
uint64_t max_time = 0;
uint64_t min_time = 1000000;
uint64_t request_num = 0;
for (int i = 0; i < thread_num; i++) {
sum_time += time_list[i];
if (time_list[i] > max_time) {
max_time = time_list[i];
}
if (time_list[i] < min_time) {
min_time = time_list[i];
for (int j = 0; j < request_list[i]; j++) {
sum_time += time_list[i][j];
if (time_list[i][j] > max_time) {
max_time = time_list[i][j];
}
if (time_list[i][j] < min_time) {
min_time = time_list[i][j];
}
}
request_num += request_list[i];
}
uint64_t mean_time = sum_time / thread_num;
LOG(INFO) << thread_num << " thread seek cost"
<< " avg = " << std::to_string(mean_time)
<< " max = " << std::to_string(max_time)
<< " min = " << std::to_string(min_time);
LOG(INFO) << " total_request = " << std::to_string(request_num) << " speed = "
<< std::to_string(1000000 * thread_num / mean_time) // mean_time us
uint64_t mean_time = sum_time / (thread_num * turns);
uint64_t main_time = time_diff(main_start, main_end);
LOG(INFO) << "\n"
<< thread_num << " thread seek cost"
<< "\navg = " << std::to_string(mean_time)
<< "\nmax = " << std::to_string(max_time)
<< "\nmin = " << std::to_string(min_time);
LOG(INFO) << "\ntotal_request = " << std::to_string(request_num)
<< "\nspeed = " << std::to_string(request_num * 1000000 /
main_time) // mean_time us
<< " query per second";
return 0;
}
......
core/general-server/op/general_dist_kv_infer_op.cpp
浏览文件 @ f2e35fae
......@@ -90,6 +90,9 @@ int GeneralDistKVInferOp::inference() {
keys.begin() + key_idx);
key_idx += dataptr_size_pairs[i].second;
}
Timer timeline;
int64_t cube_start = timeline.TimeStampUS();
timeline.Start();
rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
std::vector<std::string> table_names = cube->get_table_names();
if (table_names.size() == 0) {
......@@ -97,7 +100,7 @@ int GeneralDistKVInferOp::inference() {
return -1;
}
int ret = cube->seek(table_names[0], keys, &values);
int64_t cube_end = timeline.TimeStampUS();
if (values.size() != keys.size() || values[0].buff.size() == 0) {
LOG(ERROR) << "cube value return null";
}
......@@ -153,9 +156,7 @@ int GeneralDistKVInferOp::inference() {
VLOG(2) << "infer batch size: " << batch_size;
Timer timeline;
int64_t start = timeline.TimeStampUS();
timeline.Start();
if (InferManager::instance().infer(
engine_name().c_str(), &infer_in, out, batch_size)) {
......@@ -165,6 +166,8 @@ int GeneralDistKVInferOp::inference() {
int64_t end = timeline.TimeStampUS();
CopyBlobInfo(input_blob, output_blob);
AddBlobInfo(output_blob, cube_start);
AddBlobInfo(output_blob, cube_end);
AddBlobInfo(output_blob, start);
AddBlobInfo(output_blob, end);
return 0;
......
python/examples/bert/benchmark.sh
浏览文件 @ f2e35fae
rm profile_log
rm profile_log*
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 &
hostname=`echo $(hostname)|awk -F '.baidu.com' '{print $1}'`
sleep 5
gpu_id=0
#save cpu and gpu utilization log
if [ -d utilization ];then
rm -rf utilization
else
mkdir utilization
fi
#start server
$PYTHONROOT/bin/python3 -m paddle_serving_server_gpu.serve --model $1 --port 9292 --thread 4 --gpu_ids 0,1,2,3 --mem_optim --ir_optim > elog 2>&1 &
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
$PYTHONROOT/bin/python3 benchmark.py --thread 4 --batch_size 1 --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo -e "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
for thread_num in 1 4 8 16
do
for batch_size in 1 4 16 64 256
for batch_size in 1 4 16 64
do
job_bt=`date '+%Y%m%d%H%M%S'`
nvidia-smi --id=$gpu_id --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=0 --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=0 --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
gpu_memory_pid=$!
python3 benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
$PYTHONROOT/bin/python3 benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
kill ${gpu_memory_pid}
kill `ps -ef|grep used_memory|awk '{print $2}'`
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
$PYTHONROOT/bin/python3 cpu_utilization.py >> profile_log_$1
job_et=`date '+%Y%m%d%H%M%S'`
awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "MAX_GPU_MEMORY_USE:", max}' gpu_use.log >> profile_log_$1
monquery -n ${hostname} -i GPU_AVERAGE_UTILIZATION -s $job_bt -e $job_et -d 10 > gpu_log_file_${job_bt}
monquery -n ${hostname} -i CPU_USER -s $job_bt -e $job_et -d 10 > cpu_log_file_${job_bt}
cpu_num=$(cat /proc/cpuinfo | grep processor | wc -l)
gpu_num=$(nvidia-smi -L|wc -l)
python ../util/show_profile.py profile $thread_num >> profile_log_$1
awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$1
awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "GPU_UTILIZATION:", max}' gpu_utilization.log >> profile_log_$1
rm -rf gpu_use.log gpu_utilization.log
$PYTHONROOT/bin/python3 ../util/show_profile.py profile $thread_num >> profile_log_$1
tail -n 8 profile >> profile_log_$1
echo "" >> profile_log_$1
done
done
#Divided log
awk 'BEGIN{RS="\n\n"}{i++}{print > "bert_log_"i}' profile_log_$1
mkdir bert_log && mv bert_log_* bert_log
ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/criteo_ctr_with_cube/benchmark.py
浏览文件 @ f2e35fae
......@@ -24,6 +24,7 @@ from paddle_serving_client.utils import MultiThreadRunner
from paddle_serving_client.utils import benchmark_args
from paddle_serving_client.metric import auc
py_version = sys.version_info[0]
args = benchmark_args()
......@@ -49,7 +50,10 @@ def single_func(idx, resource):
if args.batch_size > 0:
feed_batch = []
for bi in range(args.batch_size):
data = reader().next()
if py_version == 2:
data = reader().next()
else:
data = reader().__next__()
feed_dict = {}
feed_dict['dense_input'] = data[0][0]
for i in range(1, 27):
......@@ -71,14 +75,17 @@ if __name__ == '__main__':
multi_thread_runner = MultiThreadRunner()
endpoint_list = ["127.0.0.1:9292"]
#result = single_func(0, {"endpoint": endpoint_list})
start = time.time()
result = multi_thread_runner.run(single_func, args.thread,
{"endpoint": endpoint_list})
print(result)
end = time.time()
total_cost = end - start
avg_cost = 0
qps = 0
for i in range(args.thread):
avg_cost += result[0][i * 2 + 0]
qps += result[0][i * 2 + 1]
avg_cost = avg_cost / args.thread
print("total cost: {}".format(total_cost))
print("average total cost {} s.".format(avg_cost))
print("qps {} ins/s".format(qps))
python/examples/criteo_ctr_with_cube/benchmark.sh
浏览文件 @ f2e35fae
rm profile_log
export FLAGS_profile_client=1
export FLAGS_profile_server=1
for thread_num in 1 2 4 8 16
wget https://paddle-serving.bj.bcebos.com/unittest/ctr_cube_unittest.tar.gz --no-check-certificate
tar xf ctr_cube_unittest.tar.gz
mv models/ctr_client_conf ./
mv models/ctr_serving_model_kv ./
mv models/data ./cube/
wget https://paddle-serving.bj.bcebos.com/others/cube_app.tar.gz --no-check-certificate
tar xf cube_app.tar.gz
mv cube_app/cube* ./cube/
sh cube_prepare.sh &
python test_server.py ctr_serving_model_kv > serving_log 2>&1 &
for thread_num in 1 4 16
do
for batch_size in 1 4 16 64 256
for batch_size in 1 4 16 64
do
$PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model serving_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
echo "batch size : $batch_size"
......@@ -11,6 +25,8 @@ do
echo "========================================"
echo "batch size : $batch_size" >> profile_log
$PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
tail -n 2 profile >> profile_log
tail -n 3 profile >> profile_log
done
done
ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/criteo_ctr_with_cube/benchmark_cube.sh 0 → 100755
浏览文件 @ f2e35fae
rm profile_log
wget https://paddle-serving.bj.bcebos.com/unittest/ctr_cube_unittest.tar.gz --no-check-certificate
tar xf ctr_cube_unittest.tar.gz
mv models/ctr_client_conf ./
mv models/ctr_serving_model_kv ./
mv models/data ./cube/
wget https://paddle-serving.bj.bcebos.com/others/cube_app.tar.gz --no-check-certificate
tar xf cube_app.tar.gz
mv cube_app/cube* ./cube/
sh cube_prepare.sh &
cp ../../../build_server/core/cube/cube-api/cube-cli .
python gen_key.py
for thread_num in 1 4 16 32
do
for batch_size in 1000
do
./cube-cli -config_file ./cube/conf/cube.conf -keys key -dict test_dict -thread_num $thread_num --batch $batch_size > profile 2>&1
echo "batch size : $batch_size"
echo "thread num : $thread_num"
echo "========================================"
echo "batch size : $batch_size" >> profile_log
echo "thread num : $thread_num" >> profile_log
tail -n 7 profile | head -n 4 >> profile_log
tail -n 2 profile >> profile_log
done
done
ps -ef|grep 'cube'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/criteo_ctr_with_cube/gen_key.py 0 → 100644
浏览文件 @ f2e35fae
# 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
import random
with open("key", "w") as f:
for i in range(1000000):
f.write("{}\n".format(random.randint(0, 999999)))
python/examples/criteo_ctr_with_cube/test_server.py
浏览文件 @ f2e35fae
......@@ -33,5 +33,9 @@ server = Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
server.set_num_threads(4)
server.load_model_config(sys.argv[1])
server.prepare_server(workdir="work_dir1", port=9292, device="cpu")
server.prepare_server(
workdir="work_dir1",
port=9292,
device="cpu",
cube_conf="./cube/conf/cube.conf")
server.run_server()
python/examples/criteo_ctr_with_cube/test_server_gpu.py
浏览文件 @ f2e35fae
......@@ -33,5 +33,9 @@ server = Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
server.set_num_threads(4)
server.load_model_config(sys.argv[1])
server.prepare_server(workdir="work_dir1", port=9292, device="cpu")
server.prepare_server(
workdir="work_dir1",
port=9292,
device="cpu",
cube_conf="./cube/conf/cube.conf")
server.run_server()
python/examples/grpc_impl_example/criteo_ctr_with_cube/test_server.py
浏览文件 @ f2e35fae
......@@ -33,5 +33,9 @@ server = Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
server.set_num_threads(4)
server.load_model_config(sys.argv[1], sys.argv[2])
server.prepare_server(workdir="work_dir1", port=9292, device="cpu")
server.prepare_server(
workdir="work_dir1",
port=9292,
device="cpu",
cube_conf="./cube/conf/cube.conf")
server.run_server()
python/examples/grpc_impl_example/criteo_ctr_with_cube/test_server_gpu.py
浏览文件 @ f2e35fae
......@@ -33,5 +33,9 @@ server = Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
server.set_num_threads(4)
server.load_model_config(sys.argv[1], sys.argv[2])
server.prepare_server(workdir="work_dir1", port=9292, device="cpu")
server.prepare_server(
workdir="work_dir1",
port=9292,
device="cpu",
cube_conf="./cube/conf/cube.conf")
server.run_server()
python/examples/imagenet/benchmark.py
浏览文件 @ f2e35fae
......@@ -24,7 +24,7 @@ import json
import base64
from paddle_serving_client import Client
from paddle_serving_client.utils import MultiThreadRunner
from paddle_serving_client.utils import benchmark_args
from paddle_serving_client.utils import benchmark_args, show_latency
from paddle_serving_app.reader import Sequential, File2Image, Resize
from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
......@@ -38,7 +38,11 @@ seq_preprocess = Sequential([
def single_func(idx, resource):
file_list = []
turns = 10
turns = resource["turns"]
latency_flags = False
if os.getenv("FLAGS_serving_latency"):
latency_flags = True
latency_list = []
for file_name in os.listdir("./image_data/n01440764"):
file_list.append(file_name)
img_list = []
......@@ -56,6 +60,7 @@ def single_func(idx, resource):
start = time.time()
for i in range(turns):
if args.batch_size >= 1:
l_start = time.time()
feed_batch = []
i_start = time.time()
for bi in range(args.batch_size):
......@@ -69,6 +74,9 @@ def single_func(idx, resource):
int(round(i_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))
......@@ -88,6 +96,8 @@ def single_func(idx, resource):
r = requests.post(
server, data=req, headers={"Content-Type": "application/json"})
end = time.time()
if latency_flags:
return [[end - start], latency_list]
return [[end - start]]
......@@ -96,11 +106,21 @@ if __name__ == '__main__':
endpoint_list = [
"127.0.0.1:9292", "127.0.0.1:9293", "127.0.0.1:9294", "127.0.0.1:9295"
]
result = multi_thread_runner.run(single_func, args.thread,
{"endpoint": endpoint_list})
turns = 100
start = time.time()
result = multi_thread_runner.run(
single_func, args.thread, {"endpoint": endpoint_list,
"turns": turns})
#result = single_func(0, {"endpoint": endpoint_list})
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(end - start))
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/imagenet/benchmark.sh
浏览文件 @ f2e35fae
rm profile_log
rm profile_log*
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 &
python -m paddle_serving_server_gpu.serve --model $1 --port 9292 --thread 4 --gpu_ids 0,1,2,3 --mem_optim --ir_optim 2> elog > stdlog &
sleep 5
gpu_id=0
#save cpu and gpu utilization log
if [ -d utilization ];then
rm -rf utilization
else
mkdir utilization
fi
#warm up
$PYTHONROOT/bin/python benchmark.py --thread 8 --batch_size 1 --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
$PYTHONROOT/bin/python3 benchmark.py --thread 4 --batch_size 1 --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo -e "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
for thread_num in 4 8 16
for thread_num in 1 4 8 16
do
for batch_size in 1 4 16 64
do
job_bt=`date '+%Y%m%d%H%M%S'`
nvidia-smi --id=0 --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=0 --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
gpu_memory_pid=$!
$PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
kill ${gpu_memory_pid}
kill `ps -ef|grep used_memory|awk '{print $2}'`
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
job_et=`date '+%Y%m%d%H%M%S'`
awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$1
awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "GPU_UTILIZATION:", max}' gpu_utilization.log >> profile_log_$1
rm -rf gpu_use.log gpu_utilization.log
$PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
tail -n 8 profile >> profile_log
echo "" >> profile_log_$1
done
done
#Divided log
awk 'BEGIN{RS="\n\n"}{i++}{print > "ResNet_log_"i}' profile_log_$1
mkdir $1_log && mv ResNet_log_* $1_log
ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/imdb/benchmark.sh
浏览文件 @ f2e35fae
rm profile_log
export CUDA_VISIBLE_DEVICES=0,1,2,3
rm profile_log*
export FLAGS_profile_server=1
export FLAGS_profile_client=1
export FLAGS_serving_latency=1
python -m paddle_serving_server_gpu.serve --model $1 --port 9292 --thread 4 --gpu_ids 0,1,2,3 --mem_optim --ir_optim 2> elog > stdlog &
$PYTHONROOT/bin/python3 -m paddle_serving_server.serve --model $1 --port 9292 --thread 4 --mem_optim --ir_optim 2> elog > stdlog &
hostname=`echo $(hostname)|awk -F '.baidu.com' '{print $1}'`
#save cpu and gpu utilization log
if [ -d utilization ];then
rm -rf utilization
else
mkdir utilization
fi
sleep 5
for thread_num in 4 8 16
#warm up
$PYTHONROOT/bin/python3 benchmark.py --thread 4 --batch_size 1 --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo -e "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
for thread_num in 1 4 8 16
do
for batch_size in 1 4 16 64 256
for batch_size in 1 4 16 64
do
job_bt=`date '+%Y%m%d%H%M%S'`
python benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1
$PYTHONROOT/bin/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
......@@ -21,15 +30,14 @@ do
echo "model_name:$1" >> profile_log_$1
echo "batch_size:$batch_size" >> profile_log_$1
job_et=`date '+%Y%m%d%H%M%S'`
awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "MAX_GPU_MEMORY_USE:", max}' gpu_use.log >> profile_log_$1
monquery -n ${hostname} -i GPU_AVERAGE_UTILIZATION -s $job_bt -e $job_et -d 10 > gpu_log_file_${job_bt}
monquery -n ${hostname} -i CPU_USER -s $job_bt -e $job_et -d 10 > cpu_log_file_${job_bt}
cpu_num=$(cat /proc/cpuinfo | grep processor | wc -l)
gpu_num=$(nvidia-smi -L|wc -l)
python ../util/show_profile.py profile $thread_num >> profile_log_$1
$PYTHONROOT/bin/python3 ../util/show_profile.py profile $thread_num >> profile_log_$1
$PYTHONROOT/bin/python3 cpu_utilization.py >> profile_log_$1
tail -n 8 profile >> profile_log_$1
echo "" >> profile_log_$1
done
done
#Divided log
awk 'BEGIN{RS="\n\n"}{i++}{print > "imdb_log_"i}' profile_log_$1
mkdir $1_log && mv imdb_log_* $1_log
ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/paddle_serving_app/reader/image_reader.py
浏览文件 @ f2e35fae
......@@ -677,7 +677,7 @@ class Resize(object):
Args:
size (sequence or int): Desired output size. If size is a sequence like
(h, w), output size will be matched to this. If size is an int,
(w, h), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size)
......
python/paddle_serving_server/__init__.py
浏览文件 @ f2e35fae
......@@ -25,6 +25,7 @@ from contextlib import closing
import collections
import fcntl
import shutil
import numpy as np
import grpc
from .proto import multi_lang_general_model_service_pb2
......@@ -230,7 +231,7 @@ class Server(object):
infer_service.workflows.extend(["workflow1"])
self.infer_service_conf.services.extend([infer_service])
def _prepare_resource(self, workdir):
def _prepare_resource(self, workdir, cube_conf):
self.workdir = workdir
if self.resource_conf == None:
with open("{}/{}".format(workdir, self.general_model_config_fn),
......@@ -242,6 +243,11 @@ class Server(object):
if "dist_kv" in node.name:
self.resource_conf.cube_config_path = workdir
self.resource_conf.cube_config_file = self.cube_config_fn
if cube_conf == None:
raise ValueError(
"Please set the path of cube.conf while use dist_kv op."
)
shutil.copy(cube_conf, workdir)
if "quant" in node.name:
self.resource_conf.cube_quant_bits = 8
self.resource_conf.model_toolkit_path = workdir
......@@ -366,7 +372,11 @@ class Server(object):
os.chdir(self.cur_path)
self.bin_path = self.server_path + "/serving"
def prepare_server(self, workdir=None, port=9292, device="cpu"):
def prepare_server(self,
workdir=None,
port=9292,
device="cpu",
cube_conf=None):
if workdir == None:
workdir = "./tmp"
os.system("mkdir {}".format(workdir))
......@@ -377,7 +387,7 @@ class Server(object):
if not self.port_is_available(port):
raise SystemExit("Port {} is already used".format(port))
self.set_port(port)
self._prepare_resource(workdir)
self._prepare_resource(workdir, cube_conf)
self._prepare_engine(self.model_config_paths, device)
self._prepare_infer_service(port)
self.workdir = workdir
......@@ -645,7 +655,11 @@ class MultiLangServer(object):
server_config_paths)
self.bclient_config_path_ = client_config_path
def prepare_server(self, workdir=None, port=9292, device="cpu"):
def prepare_server(self,
workdir=None,
port=9292,
device="cpu",
cube_conf=None):
if not self._port_is_available(port):
raise SystemExit("Prot {} is already used".format(port))
default_port = 12000
......@@ -656,7 +670,10 @@ class MultiLangServer(object):
self.port_list_.append(default_port + i)
break
self.bserver_.prepare_server(
workdir=workdir, port=self.port_list_[0], device=device)
workdir=workdir,
port=self.port_list_[0],
device=device,
cube_conf=cube_conf)
self.set_port(port)
def _launch_brpc_service(self, bserver):
......
python/paddle_serving_server_gpu/__init__.py
浏览文件 @ f2e35fae
......@@ -26,7 +26,7 @@ from contextlib import closing
import argparse
import collections
import fcntl
import shutil
import numpy as np
import grpc
from .proto import multi_lang_general_model_service_pb2
......@@ -285,7 +285,7 @@ class Server(object):
infer_service.workflows.extend(["workflow1"])
self.infer_service_conf.services.extend([infer_service])
def _prepare_resource(self, workdir):
def _prepare_resource(self, workdir, cube_conf):
self.workdir = workdir
if self.resource_conf == None:
with open("{}/{}".format(workdir, self.general_model_config_fn),
......@@ -297,6 +297,11 @@ class Server(object):
if "dist_kv" in node.name:
self.resource_conf.cube_config_path = workdir
self.resource_conf.cube_config_file = self.cube_config_fn
if cube_conf == None:
raise ValueError(
"Please set the path of cube.conf while use dist_kv op."
)
shutil.copy(cube_conf, workdir)
self.resource_conf.model_toolkit_path = workdir
self.resource_conf.model_toolkit_file = self.model_toolkit_fn
self.resource_conf.general_model_path = workdir
......@@ -406,7 +411,11 @@ class Server(object):
os.chdir(self.cur_path)
self.bin_path = self.server_path + "/serving"
def prepare_server(self, workdir=None, port=9292, device="cpu"):
def prepare_server(self,
workdir=None,
port=9292,
device="cpu",
cube_conf=None):
if workdir == None:
workdir = "./tmp"
os.system("mkdir {}".format(workdir))
......@@ -418,7 +427,7 @@ class Server(object):
raise SystemExit("Port {} is already used".format(port))
self.set_port(port)
self._prepare_resource(workdir)
self._prepare_resource(workdir, cube_conf)
self._prepare_engine(self.model_config_paths, device)
self._prepare_infer_service(port)
self.workdir = workdir
......@@ -690,7 +699,11 @@ class MultiLangServer(object):
server_config_paths)
self.bclient_config_path_ = client_config_path
def prepare_server(self, workdir=None, port=9292, device="cpu"):
def prepare_server(self,
workdir=None,
port=9292,
device="cpu",
cube_conf=None):
if not self._port_is_available(port):
raise SystemExit("Prot {} is already used".format(port))
default_port = 12000
......@@ -701,7 +714,10 @@ class MultiLangServer(object):
self.port_list_.append(default_port + i)
break
self.bserver_.prepare_server(
workdir=workdir, port=self.port_list_[0], device=device)
workdir=workdir,
port=self.port_list_[0],
device=device,
cube_conf=cube_conf)
self.set_port(port)
def _launch_brpc_service(self, bserver):
......
tools/serving_build.sh
浏览文件 @ f2e35fae
......@@ -229,10 +229,7 @@ function python_run_criteo_ctr_with_cube() {
check_cmd "mv models/data ./cube/"
check_cmd "mv models/ut_data ./"
cp ../../../build-server-$TYPE/output/bin/cube* ./cube/
mkdir -p $PYTHONROOT/lib/python2.7/site-packages/paddle_serving_server/serving-cpu-avx-openblas-0.1.3/
yes | cp ../../../build-server-$TYPE/output/demo/serving/bin/serving $PYTHONROOT/lib/python2.7/site-packages/paddle_serving_server/serving-cpu-avx-openblas-0.1.3/
sh cube_prepare.sh &
check_cmd "mkdir work_dir1 && cp cube/conf/cube.conf ./work_dir1/"
python test_server.py ctr_serving_model_kv &
sleep 5
check_cmd "python test_client.py ctr_client_conf/serving_client_conf.prototxt ./ut_data >score"
......@@ -257,10 +254,7 @@ function python_run_criteo_ctr_with_cube() {
check_cmd "mv models/data ./cube/"
check_cmd "mv models/ut_data ./"
cp ../../../build-server-$TYPE/output/bin/cube* ./cube/
mkdir -p $PYTHONROOT/lib/python2.7/site-packages/paddle_serving_server_gpu/serving-gpu-0.1.3/
yes | cp ../../../build-server-$TYPE/output/demo/serving/bin/serving $PYTHONROOT/lib/python2.7/site-packages/paddle_serving_server_gpu/serving-gpu-0.1.3/
sh cube_prepare.sh &
check_cmd "mkdir work_dir1 && cp cube/conf/cube.conf ./work_dir1/"
python test_server_gpu.py ctr_serving_model_kv &
sleep 5
# for warm up
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或