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update_scripts_benchmark

pull/1469/head
hysunflower 2021-11-23 10:50:23 +00:00
parent 5fb8f0d308
commit 55489f7179
2 changed files with 19 additions and 7 deletions
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7
benchmark/run_all.sh
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@ -6,7 +6,8 @@
# 2 拷贝该模型需要数据、预训练模型
# 3 批量运行（如不方便批量，1，2需放到单个模型中）
model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 EfficientNetB0 ShuffleNetV2_x1_0 DenseNet121 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base)
model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 ShuffleNetV2_x1_0 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base) # benchmark 监控模型列表
#model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 EfficientNetB0 ShuffleNetV2_x1_0 DenseNet121 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base) # 该脚本支持列表
fp_item_list=(fp32)
bs_list=(32 64 96 128)
for model_mode in ${model_mode_list[@]}; do
@ -14,11 +15,11 @@ for model_mode in ${model_mode_list[@]}; do
for bs_item in ${bs_list[@]};do
echo "index is speed, 1gpus, begin, ${model_name}"
run_mode=sp
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/clas_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}_1gpus 2>&1 # (5min)
sleep 10
echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
run_mode=mp
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode}| tee ${log_path}/clas_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}_8gpus8p 2>&1
sleep 10
done
done

19
benchmark/run_benchmark.sh
View File

@ -6,10 +6,19 @@ function _set_params(){
run_mode=${1:-"sp"} # 单卡sp|多卡mp
batch_size=${2:-"64"}
fp_item=${3:-"fp32"} # fp32|fp16
epochs=${4:-"10"} # 可选，如果需要修改代码提前中断
epochs=${4:-"2"} # 可选，如果需要修改代码提前中断
model_name=${5:-"model_name"}
run_log_path="${TRAIN_LOG_DIR:-$(pwd)}/benchmark" # TRAIN_LOG_DIR 后续QA设置该参数
index=1
mission_name="图像分类" # 模型所属任务名称，具体可参考scripts/config.ini （必填）
direction_id=0 # 任务所属方向，0：CV，1：NLP，2：Rec。 (必填)
skip_steps=8 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
keyword_loss="loss:" #选填
model_mode=-1 # 解析日志，具体参考scripts/analysis.py. (必填)
ips_unit="images/s"
base_batch_size=$batch_size
# 以下不用修改
device=${CUDA_VISIBLE_DEVICES//,/ }
arr=(${device})
@ -51,6 +60,8 @@ function _train(){
cp mylog/workerlog.0 ${log_file}
fi
}
source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
_set_params $@
_train
_run
#_train