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提交 8d1f7101 编写于 作者: G gentelyang
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python/examples/bert/benchmark.sh
浏览文件 @ 8d1f7101
rm profile_log rm profile_log*
export CUDA_VISIBLE_DEVICES=0,1,2,3 export CUDA_VISIBLE_DEVICES=0,1,2,3
export FLAGS_profile_server=1 export FLAGS_profile_server=1
export FLAGS_profile_client=1 export FLAGS_profile_client=1
export FLAGS_serving_latency=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 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 #warm up
python3 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 $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo -e "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
for thread_num in 4 8 16 for thread_num in 1 4 8 16
do do
for batch_size in 1 4 16 64 256 for batch_size in 1 4 16 64
do do
job_bt=`date '+%Y%m%d%H%M%S'` job_bt=`date '+%Y%m%d%H%M%S'`
nvidia-smi --id=$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=$! gpu_memory_pid=$!
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 ${gpu_memory_pid}
kill `ps -ef|grep used_memory|awk '{print $2}'`
echo "model_name:" $1 echo "model_name:" $1
echo "thread_num:" $thread_num echo "thread_num:" $thread_num
echo "batch_size:" $batch_size echo "batch_size:" $batch_size
echo "=================Done====================" echo "=================Done===================="
echo "model_name:$1" >> profile_log_$1 echo "model_name:$1" >> profile_log_$1
echo "batch_size:$batch_size" >> 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'` 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 awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "MAX_GPU_MEMORY:", 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} awk 'BEGIN {max = 0} {if(NR>1){if ($1 > max) max=$1}} END {print "GPU_UTILIZATION:", max}' gpu_utilization.log >> profile_log_$1
monquery -n ${hostname} -i CPU_USER -s $job_bt -e $job_et -d 10 > cpu_log_file_${job_bt} rm -rf gpu_use.log gpu_utilization.log
cpu_num=$(cat /proc/cpuinfo | grep processor | wc -l) $PYTHONROOT/bin/python3 ../util/show_profile.py profile $thread_num >> profile_log_$1
gpu_num=$(nvidia-smi -L|wc -l)
python ../util/show_profile.py profile $thread_num >> profile_log_$1
tail -n 8 profile >> profile_log_$1 tail -n 8 profile >> profile_log_$1
echo "" >> profile_log_$1 echo "" >> profile_log_$1
done done
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 ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/imagenet/benchmark.sh
浏览文件 @ 8d1f7101
rm profile_log rm profile_log*
export CUDA_VISIBLE_DEVICES=0,1,2,3 export CUDA_VISIBLE_DEVICES=0,1,2,3
export FLAGS_profile_server=1 export FLAGS_profile_server=1
export FLAGS_profile_client=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 2> elog > stdlog &
sleep 5 sleep 5
gpu_id=0
#save cpu and gpu utilization log
if [ -d utilization ];then
rm -rf utilization
else
mkdir utilization
fi
#warm up #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 $2/serving_client_conf.prototxt --request rpc > profile 2>&1
echo -e "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
for thread_num in 4 8 16 for thread_num in 1 4 8 16
do do
for batch_size in 1 4 16 64 for batch_size in 1 4 16 64
do do
job_bt=`date '+%Y%m%d%H%M%S'`
nvidia-smi --id=0 --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
nvidia-smi --id=0 --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
gpu_memory_pid=$!
$PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model $2/serving_client_conf.prototxt --request rpc > profile 2>&1 $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 "model name :" $1
echo "thread num :" $thread_num echo "thread num :" $thread_num
echo "batch size :" $batch_size echo "batch size :" $batch_size
echo "=================Done====================" echo "=================Done===================="
echo "model name :$1" >> profile_log echo "model name :$1" >> profile_log
echo "batch size :$batch_size" >> 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 $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
tail -n 8 profile >> profile_log tail -n 8 profile >> profile_log
echo "" >> profile_log_$1
done done
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 ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
python/examples/imdb/benchmark.sh
浏览文件 @ 8d1f7101
rm profile_log rm profile_log*
export CUDA_VISIBLE_DEVICES=0,1,2,3
export FLAGS_profile_server=1 export FLAGS_profile_server=1
export FLAGS_profile_client=1 export FLAGS_profile_client=1
export FLAGS_serving_latency=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}'` 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 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 do
for batch_size in 1 4 16 64 256 for batch_size in 1 4 16 64
do do
job_bt=`date '+%Y%m%d%H%M%S'` 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 "model_name:" $1
echo "thread_num:" $thread_num echo "thread_num:" $thread_num
echo "batch_size:" $batch_size echo "batch_size:" $batch_size
...@@ -21,15 +29,14 @@ do ...@@ -21,15 +29,14 @@ do
echo "model_name:$1" >> profile_log_$1 echo "model_name:$1" >> profile_log_$1
echo "batch_size:$batch_size" >> profile_log_$1 echo "batch_size:$batch_size" >> profile_log_$1
job_et=`date '+%Y%m%d%H%M%S'` 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 $PYTHONROOT/bin/python3 ../util/show_profile.py profile $thread_num >> profile_log_$1
monquery -n ${hostname} -i GPU_AVERAGE_UTILIZATION -s $job_bt -e $job_et -d 10 > gpu_log_file_${job_bt} $PYTHONROOT/bin/python3 cpu_utilization.py >> profile_log_$1
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
tail -n 8 profile >> profile_log_$1 tail -n 8 profile >> profile_log_$1
echo "" >> profile_log_$1 echo "" >> profile_log_$1
done done
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 ps -ef|grep 'serving'|grep -v grep|cut -c 9-15 | xargs kill -9
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