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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： 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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CUR_DIR=${PWD}
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source ../../../tools/venv/bin/activate
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#cd **
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pushd ../../../examples/aishell/s1
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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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# 2 拷贝该模型需要数据、预训练模型
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source path.sh
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fp_item_list=(fp32)
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bs_item=(16)
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config_path=conf/conformer.yaml
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seed=0
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output=exp/conformer
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profiler_options=None
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for fp_item in ${fp_item_list[@]}; do
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for batch_size in ${bs_item[@]}
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do
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rm exp -rf
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echo "index is speed, 8gpus, run_mode is multi_process, begin, conformer"
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run_mode=mp
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ngpu=8
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${batch_size} ${fp_item} ${CUR_DIR}
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rm exp -rf
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echo "index is speed, 1gpus, begin, conformer"
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run_mode=sp
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ngpu=1
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CUDA_VISIBLE_DEVICES=0 bash ${CUR_DIR}/run_benchmark.sh ${run_mode} ${config_path} ${output} ${seed} ${ngpu} ${profiler_options} ${batch_size} ${fp_item} ${CUR_DIR}
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done
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done
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popd
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mkdir -p log
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bash run_analysis_sp.sh > log/log_sp.out
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bash run_analysis_mp.sh > log/log_mp.out
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