#!/usr/bin/env bash set -xe # 运行示例:CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode} # 参数说明 function _set_params(){ run_mode=${1:-"sp"} # 单卡sp|多卡mp batch_size=${2:-"64"} fp_item=${3:-"fp32"} # fp32|fp16 max_iter=${4:-"10"} # 可选,如果需要修改代码提前中断 model_name=${5:-"model_name"} run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数 # 以下不用修改 device=${CUDA_VISIBLE_DEVICES//,/ } arr=(${device}) num_gpu_devices=${#arr[*]} log_file=${run_log_path}/${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices} } function _train(){ echo "Train on ${num_gpu_devices} GPUs" echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size" train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} Global.eval_batch_step=[0,20000] Global.print_batch_step=2" case ${run_mode} in sp) train_cmd="python3.7 tools/train.py "${train_cmd}"" ;; mp) train_cmd="python3.7 -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}" ;; *) echo "choose run_mode(sp or mp)"; exit 1; esac # 以下不用修改 timeout 15m ${train_cmd} > ${log_file} 2>&1 if [ $? -ne 0 ];then echo -e "${model_name}, FAIL" export job_fail_flag=1 else echo -e "${model_name}, SUCCESS" export job_fail_flag=0 fi if [ $run_mode = "mp" -a -d mylog ]; then rm ${log_file} cp mylog/workerlog.0 ${log_file} fi } function _analysis_log(){ analysis_cmd="python3.7 benchmark/analysis.py --filename ${log_file} --mission_name ${model_name} --run_mode ${run_mode} --direction_id 0 --keyword 'ips:' --base_batch_size ${batch_size} --skip_steps 1 --gpu_num ${num_gpu_devices} --index 1 --model_mode=-1 --ips_unit=samples/sec" eval $analysis_cmd } function _kill_process(){ kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'` } _set_params $@ _train _analysis_log _kill_process