mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
commit
eee3ca70fb
@ -142,7 +142,6 @@ else
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batch_size=${params_list[1]}
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batch_size=`echo ${batch_size} | tr -cd "[0-9]" `
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precision=${params_list[2]}
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# run_process_type=${params_list[3]}
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run_mode=${params_list[3]}
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device_num=${params_list[4]}
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IFS=";"
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@ -167,10 +166,9 @@ for batch_size in ${batch_size_list[*]}; do
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gpu_id=$(set_gpu_id $device_num)
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if [ ${#gpu_id} -le 1 ];then
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run_process_type="SingleP"
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log_path="$SAVE_LOG/profiling_log"
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mkdir -p $log_path
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_profiling"
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_profiling"
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func_sed_params "$FILENAME" "${line_gpuid}" "0" # sed used gpu_id
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# set profile_option params
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tmp=`sed -i "${line_profile}s/.*/${profile_option}/" "${FILENAME}"`
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@ -186,8 +184,8 @@ for batch_size in ${batch_size_list[*]}; do
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speed_log_path="$SAVE_LOG/index"
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mkdir -p $log_path
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mkdir -p $speed_log_path
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
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speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_log"
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speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
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func_sed_params "$FILENAME" "${line_profile}" "null" # sed profile_id as null
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cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
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echo $cmd
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@ -198,13 +196,12 @@ for batch_size in ${batch_size_list[*]}; do
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eval "cat ${log_path}/${log_name}"
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# parser log
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_model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
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_model_name="${model_name}_bs${batch_size}_${precision}_${run_mode}"
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cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
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--speed_log_file '${speed_log_path}/${speed_log_name}' \
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--model_name ${_model_name} \
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--base_batch_size ${batch_size} \
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--run_mode ${run_mode} \
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--run_process_type ${run_process_type} \
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--fp_item ${precision} \
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--keyword ips: \
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--skip_steps 2 \
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@ -218,13 +215,12 @@ for batch_size in ${batch_size_list[*]}; do
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else
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IFS=";"
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unset_env=`unset CUDA_VISIBLE_DEVICES`
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run_process_type="MultiP"
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log_path="$SAVE_LOG/train_log"
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speed_log_path="$SAVE_LOG/index"
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mkdir -p $log_path
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mkdir -p $speed_log_path
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_log"
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speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}_${device_num}_speed"
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log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_log"
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speed_log_name="${repo_name}_${model_name}_bs${batch_size}_${precision}_${run_mode}_${device_num}_speed"
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func_sed_params "$FILENAME" "${line_gpuid}" "$gpu_id" # sed used gpu_id
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func_sed_params "$FILENAME" "${line_profile}" "null" # sed --profile_option as null
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cmd="bash test_tipc/test_train_inference_python.sh ${FILENAME} benchmark_train > ${log_path}/${log_name} 2>&1 "
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@ -235,14 +231,13 @@ for batch_size in ${batch_size_list[*]}; do
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export model_run_time=$((${job_et}-${job_bt}))
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eval "cat ${log_path}/${log_name}"
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# parser log
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_model_name="${model_name}_bs${batch_size}_${precision}_${run_process_type}_${run_mode}"
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_model_name="${model_name}_bs${batch_size}_${precision}_${run_mode}"
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cmd="${python} ${BENCHMARK_ROOT}/scripts/analysis.py --filename ${log_path}/${log_name} \
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--speed_log_file '${speed_log_path}/${speed_log_name}' \
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--model_name ${_model_name} \
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--base_batch_size ${batch_size} \
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--run_mode ${run_mode} \
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--run_process_type ${run_process_type} \
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--fp_item ${precision} \
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--keyword ips: \
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--skip_steps 2 \
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=256
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fp_item=fp16
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run_process_type=SingleP
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run_mode=DP
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device_num=N1C1
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max_epochs=1
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@ -10,4 +9,4 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=256
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fp_item=fp32
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run_process_type=SingleP
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run_mode=DP
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device_num=N1C1
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max_epochs=1
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@ -10,8 +9,8 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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# run profiling
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sleep 10;
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export PROFILING=true
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=64
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fp_item=fp16
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run_process_type=SingleP
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run_mode=DP
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device_num=N1C1
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max_epochs=1
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@ -10,4 +9,4 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=64
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fp_item=fp32
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run_process_type=SingleP
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run_mode=DP
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device_num=N1C1
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max_epochs=1
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@ -10,8 +9,8 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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# run profiling
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sleep 10;
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export PROFILING=true
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=256
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fp_item=fp16
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run_process_type=MultiP
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run_mode=DP
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device_num=N1C8
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max_epochs=1
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@ -10,4 +9,4 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=256
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fp_item=fp32
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run_process_type=MultiP
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run_mode=DP
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device_num=N1C8
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max_epochs=1
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@ -10,4 +9,4 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=64
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fp_item=fp16
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run_process_type=MultiP
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run_mode=DP
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device_num=N1C8
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max_epochs=1
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@ -10,4 +9,4 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,7 +1,6 @@
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model_item=ResNet50
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bs_item=64
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fp_item=fp32
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run_process_type=MultiP
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run_mode=DP
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device_num=N1C8
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max_epochs=1
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@ -10,4 +9,4 @@ num_workers=8
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# get data
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bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
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# run
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
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@ -1,23 +1,22 @@
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#!/usr/bin/env bash
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# Test training benchmark for a model.
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# Usage:bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_process_type} ${run_mode} ${device_num}
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# Usage:bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num}
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function _set_params(){
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model_item=${1:-"model_item"} # (必选) 模型 item
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base_batch_size=${2:-"2"} # (必选) 如果是静态图单进程,则表示每张卡上的BS,需在训练时*卡数
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fp_item=${3:-"fp32"} # (必选) fp32|fp16
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run_process_type=${4:-"SingleP"} # (必选) 单进程 SingleP|多进程 MultiP
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run_mode=${5:-"DP"} # (必选) MP模型并行|DP数据并行|PP流水线并行|混合并行DP1-MP1-PP1|DP1-MP4-PP1
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device_num=${6:-"N1C1"} # (必选) 使用的卡数量,N1C1|N1C8|N4C32 (4机32卡)
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run_mode=${4:-"DP"} # (必选) MP模型并行|DP数据并行|PP流水线并行|混合并行DP1-MP1-PP1|DP1-MP4-PP1
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device_num=${5:-"N1C1"} # (必选) 使用的卡数量,N1C1|N1C8|N4C32 (4机32卡)
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profiling=${PROFILING:-"false"} # (必选) Profiling 开关,默认关闭,通过全局变量传递
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model_repo="PaddleClas" # (必选) 模型套件的名字
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speed_unit="samples/sec" # (必选)速度指标单位
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skip_steps=10 # (必选)解析日志,跳过模型前几个性能不稳定的step
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keyword="ips:" # (必选)解析日志,筛选出性能数据所在行的关键字
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convergence_key="loss:" # (可选)解析日志,筛选出收敛数据所在行的关键字 如:convergence_key="loss:"
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max_epochs=${7:-"1"} # (可选)需保证模型执行时间在5分钟内,需要修改代码提前中断的直接提PR 合入套件;或使用max_epoch参数
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num_workers=${8:-"4"} # (可选)
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max_epochs=${6:-"1"} # (可选)需保证模型执行时间在5分钟内,需要修改代码提前中断的直接提PR 合入套件;或使用max_epoch参数
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num_workers=${7:-"4"} # (可选)
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# 以下为通用执行命令,无特殊可不用修改
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model_name=${model_item}_bs${base_batch_size}_${fp_item}_${run_process_type}_${run_mode} # (必填) 且格式不要改动,与竞品名称对齐
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model_name=${model_item}_bs${base_batch_size}_${fp_item}_${run_mode} # (必填) 且格式不要改动,与竞品名称对齐
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device=${CUDA_VISIBLE_DEVICES//,/ }
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arr=(${device})
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num_gpu_devices=${#arr[*]}
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@ -48,13 +47,19 @@ function _train(){
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train_cmd="${config_file} -o DataLoader.Train.sampler.batch_size=${base_batch_size} -o Global.epochs=${max_epochs} -o DataLoader.Train.loader.num_workers=${num_workers} ${profiling_config} -o Global.eval_during_train=False"
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# 以下为通用执行命令,无特殊可不用修改
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case ${run_process_type} in
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SingleP)
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train_cmd="python ppcls/static/train.py ${train_cmd}";;
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MultiP)
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train_cmd="python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 ppcls/static/train.py ${train_cmd}";;
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*) echo "choose run_process_type(SingleP or MultiP)"; exit 1;
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case ${run_mode} in
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DP) if [[ ${device_num} = "N1C1" ]];then
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echo "run ${run_mode} ${device_num}"
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train_cmd="python ppcls/static/train.py ${train_cmd}"
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else
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rm -rf ./mylog
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train_cmd="python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 ppcls/static/train.py ${train_cmd}"
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fi
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;;
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DP1-MP1-PP1) echo "run run_mode: DP1-MP1-PP1" ;;
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*) echo "choose run_mode "; exit 1;
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esac
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echo "train_cmd: ${train_cmd} log_file: ${log_file}"
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timeout 5m ${train_cmd} > ${log_file} 2>&1
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if [ $? -ne 0 ];then
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@ -63,7 +68,7 @@ function _train(){
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echo -e "${model_name}, SUCCESS"
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fi
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# kill -9 `ps -ef|grep 'python'|awk '{print $2}'`
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if [ ${run_process_type} = "MultiP" -a -d mylog ]; then
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if [ ${device_num} != "N1C1" -a -d mylog ]; then
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rm ${log_file}
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cp mylog/workerlog.0 ${log_file}
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fi
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