export FLAGS_profile_pipeline=1
alias python3="python3.7"
modelname="imagenet"
use_gpu=1
gpu_id="0"
benchmark_config_filename="benchmark_config.yaml"

# HTTP
ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 
sleep 3
if [ $use_gpu -eq 1 ]; then
  python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
else
  python3 benchmark.py yaml local_predictor 1 cpu
fi
rm -rf profile_log_$modelname
for thread_num in 1
do
  for batch_size in 1
  do
    echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
    rm -rf PipelineServingLogs
    rm -rf cpu_utilization.py
    python3 resnet50_web_service.py >web.log 2>&1 &
    sleep 3
    nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
    nvidia-smi --id=${gpu_id} --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
    python3 benchmark.py run http $thread_num $batch_size
    python3 cpu_utilization.py >>profile_log_$modelname
    python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
    ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
    ps -ef | grep nvidia-smi | awk '{print $2}' | xargs kill -9
    python3 benchmark.py dump benchmark.log benchmark.tmp
    mv benchmark.tmp benchmark.log
    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
    cat benchmark.log >> profile_log_$modelname
    python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
    #rm -rf gpu_use.log gpu_utilization.log
  done
done