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#!/bin/bash
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CUR_DIR=${PWD}
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ROOT_DIR=../../
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# 提供可稳定复现性能的脚本,默认在标准docker环境内py37执行:
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# collect env info
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bash ${ROOT_DIR}/utils/pd_env_collect.sh
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#cat pd_env.txt
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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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#pushd ${ROOT_DIR}/tools; make; popd
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#source ${ROOT_DIR}/tools/venv/bin/activate
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#pushd ${ROOT_DIR}; bash setup.sh; popd
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# 2 拷贝该模型需要数据、预训练模型
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# 执行目录:需说明
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#pushd ${ROOT_DIR}/examples/aishell/s1
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pushd ${ROOT_DIR}/examples/tiny/s1
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mkdir -p exp/log
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. path.sh
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#bash local/data.sh &> exp/log/data.log
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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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model_mode_list=(conformer transformer)
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fp_item_list=(fp32)
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bs_item_list=(32 64 96)
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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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for bs_item in ${bs_item_list[@]}
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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 ${CUR_DIR}/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode} # (5min)
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sleep 60
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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 ${CUR_DIR}/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode}
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sleep 60
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done
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done
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done
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popd # aishell/s1
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