32 lines
1.9 KiB
Bash
32 lines
1.9 KiB
Bash
# 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37
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# 执行目录：需说明
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# cd **
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# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
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# pip install ...
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# 2 拷贝该模型需要数据、预训练模型
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# 3 批量运行（如不方便批量，1，2需放到单个模型中）
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log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # LOG_PATH_INDEX_DIR 后续QA设置参数
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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
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#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
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fp_item_list=(fp32)
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#bs_list=(32 64 96 128)
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for model_mode in ${model_mode_list[@]}; do
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for fp_item in ${fp_item_list[@]}; do
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if [ ${model_mode} = MobileNetV3_large_x1_0 ] || [ ${model_mode} = ShuffleNetV2_x1_0 ]; then
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bs_list=(256)
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else
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bs_list=(64)
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fi
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for bs_item in ${bs_list[@]};do
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echo "index is speed, 1gpus, begin, ${model_name}"
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run_mode=sp
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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)
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sleep 10
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echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
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run_mode=mp
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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
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sleep 10
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done
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done
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done
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