forked from PaddlePaddle/Serving
-
Notifications
You must be signed in to change notification settings - Fork 0
/
benchmark.sh
44 lines (38 loc) · 2 KB
/
benchmark.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
export FLAGS_profile_pipeline=1
alias python3="python3.6"
modelname="clas-DarkNet53"
# HTTP
#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
sleep 3
# Create yaml,If you already have the config.yaml, ignore it.
#python3 benchmark.py yaml local_predictor 1 gpu
rm -rf profile_log_$modelname
echo "Starting HTTP Clients..."
# Start a client in each thread, tesing the case of multiple threads.
for thread_num in 1 2 4 8 12 16
do
for batch_size in 1
do
echo "#----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
# Start one web service, If you start the service yourself, you can ignore it here.
#python3 web_service.py >web.log 2>&1 &
#sleep 3
# --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTIL:', cpu_utilization)\n" > cpu_utilization.py
# Start http client
python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
# Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
python3 cpu_utilization.py >> profile_log_$modelname
grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
awk -F' ' '{sum+=$1} END {print "GPU_UTIL:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
# Show profiles
python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
tail -n 8 profile >> profile_log_$modelname
echo '' >> profile_log_$modelname
done
done
# Kill all nvidia-smi background task.
pkill nvidia-smi