set -e function train() { unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY topology=$1 layer_num=$2 bs=$3 thread=`nproc` # each trainer_count use only 1 core to avoid conflict log="logs/train-${topology}-${layer_num}-${thread}openblas-${bs}.log" args="batch_size=${bs},layer_num=${layer_num}" config="${topology}.py" paddle train --job=time \ --config=$config \ --use_gpu=False \ --trainer_count=$thread \ --log_period=10 \ --test_period=100 \ --config_args=$args \ 2>&1 | tee ${log} avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'` fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'` echo "FPS: $fps images/sec" 2>&1 | tee -a ${log} } if [ ! -f "train.list" ]; then echo " " > train.list fi if [ ! -d "logs" ]; then mkdir logs fi # training benchmark for batchsize in 64 128 256; do train vgg 19 $batchsize train resnet 50 $batchsize train googlenet v1 $batchsize train alexnet group2 $batchsize $use_mkldnn done