[add]上传训练benchmark by z00560161
This commit is contained in:
liang_chaoming@huawei.com
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#!/bin/bash
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rank_size=$1
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yamlPath=$2
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toolsPath=$3
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# ${rank_size} ${yamlPath} ${currentDir}
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if [ -f /.dockerenv ];then
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CLUSTER=$4
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MPIRUN_ALL_IP="$5"
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export CLUSTER=${CLUSTER}
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fi
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currentDir=$(cd "$(dirname "$0")/.."; pwd)
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# 配置环境变量并调用 train 方法
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currtime=`date +%Y%m%d%H%M%S`
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currtime=`date +%Y%m%d%H%M%S`
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mkdir -p ${currentDir%train*}/train/result/pt_resnet50/training_job_${currtime}/
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train_job_dir=${currentDir%train*}/train/result/pt_resnet50/training_job_${currtime}/
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echo "[`date +%Y%m%d-%H:%M:%S`] [INFO] ${train_job_dir}"
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# user env
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export HCCL_CONNECT_TIMEOUT=600
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export JOB_ID=9999001
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export SLOG_PRINT_TO_STDOUT=0
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export RANK_TABLE_FILE=${currentDir}/config/${rank_size}p.json
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# 从 yaml 获取配置
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eval $(${toolsPath}/get_params_for_yaml.sh ${yamlPath} "pytorch_config")
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# device 列表, 若无指定 device 根据 rank_size 顺序选择
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eval device_group=\$device_group_${rank_size}p
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if [ x"${device_group}" == x"" ] || [ ${rank_size} -ge 8 ];then
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device_group="$(seq 0 "$(expr $rank_size - 1)")"
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fi
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# get last device id in device_group, hw log in performance from the dir named last_device_id
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device_group_str=`echo ${device_group} | sed 's/ //g'`
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first_device_id=`echo ${device_group_str: 0:1}`
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if [ x"${rank_size}" == x"8" ]; then
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export WHICH_OP=GEOP
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export NEW_GE_FE_ID=1
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export GE_AICPU_FLAG=1
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fi
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rank_id=0
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if [ x"${CLUSTER}" == x"True" ];then
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# ln hw log
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ln -snf ${train_job_dir}/0/hw_resnet50.log ${train_job_dir}
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this_ip=$(hostname -I |awk '{print $1}')
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for ip in $MPIRUN_ALL_IP;do
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if [ x"$ip" != x"$this_ip" ];then
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scp $yamlPath root@$ip:$yamlPath
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scp $jsonFilePath root@$ip:$jsonFilePath
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fi
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done
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export PATH=$PATH:/usr/local/mpirun4.0/bin
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mpirun -H ${mpirun_ip} \
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--bind-to none -map-by slot\
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--allow-run-as-root \
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--mca btl_tcp_if_exclude lo,docker0,endvnic,virbr0,vethf40501b,docker_gwbridge,br-f42ac38052b4\
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--prefix /usr/local/mpirun4.0/ \
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${currentDir}/scripts/train.sh 0 $rank_size $yamlPath $currtime ${toolsPath} ${CLUSTER}
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else
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# ln hw log
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ln -snf ${train_job_dir}/${first_device_id}/hw_resnet50.log ${train_job_dir}
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for device_id in $device_group;do
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#echo "[`date +%Y%m%d-%H:%M:%S`] [INFO] start: train ${device_id} & " >> ${currentDir}/result/main.log
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${currentDir}/scripts/train.sh $device_id $rank_size $yamlPath $currtime ${toolsPath} $rank_id &
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let rank_id++
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done
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fi
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wait
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echo "[`date +%Y%m%d-%H:%M:%S`] [INFO] ${train_job_dir} "
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@@ -0,0 +1,138 @@
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#!/usr/bin/env bash
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device_id=$1
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rank_size=$2
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yamlPath=$3
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currtime=$4
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toolsPath=$5
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currentDir=$(cd "$(dirname "$0")/.."; pwd)
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export REMARK_LOG_FILE=hw_resnet50.log
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mkdir -p ${currentDir%train*}/train/result/pt_resnet50/training_job_${currtime}/
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export train_job_dir=${currentDir%train*}/train/result/pt_resnet50/training_job_${currtime}/
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source ${currentDir}/config/npu_set_env.sh
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benchmark_log_path=${currentDir%atlas_benchmark-master*}/atlas_benchmark-master/utils
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#atlasboost_path=${currentDir%atlas_benchmark-master*}/atlas_benchmark-master/utils/atlasboost
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code_dir_path=${currentDir}/code
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export PYTHONPATH=$PYTHONPATH:${benchmark_log_path}:${code_dir_path}
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# 从 yaml 获取配置
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eval $(${toolsPath}/get_params_for_yaml.sh ${yamlPath} "pytorch_config")
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# user env
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export YAML_PATH=$3
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export HCCL_CONNECT_TIMEOUT=600
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export JOB_ID=9999001
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#export HCCL_RANK_TABLE_PATH=${currentDir}/config/${rank_size}p.json
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export RANK_SIZE=${rank_size}
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export RANK_INDEX=0
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export SLOG_PRINT_TO_STDOUT=0
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export DEVICE_ID=$1
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DEVICE_INDEX=$(( DEVICE_ID + RANK_INDEX * 8 ))
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export DEVICE_INDEX=${DEVICE_INDEX}
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export MODEL_CKPT_PATH=${train_job_dir}/${device_id}/ckpt${device_id}
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cd ${train_job_dir}
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curd_dir=${currentDir%atlas_benchmark-master*}/atlas_benchmark-master/utils/atlasboost
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export PYTHONPATH=$PYTHONPATH:${curd_dir}
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if [ x"$6" != x"True" ];then
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rank_id=$6
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export RANK_ID=$6
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else
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device_id_mo=$(python3.7 -c "import src.tensorflow.mpi_ops as atlasboost;atlasboost.init(); \
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device_id = atlasboost.local_rank();cluster_device_id = str(device_id); \
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atlasboost.set_device_id(device_id);print(atlasboost.rank())")
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device_id_mo=`echo $device_id_mo`
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rank_id=${device_id_mo##* }
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export RANK_ID=${rank_id}
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device=${device_id_mo##*deviceid = }
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device_id=${device%% phyid=*}
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export DEVICE_ID=${device_id}
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hccljson=${train_job_dir}/*.json
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cp ${hccljson} ${currentDir}/config/${rank_size}p.json
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fi
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#mkdir exec path
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mkdir -p ${train_job_dir}/${device_id}
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cd ${train_job_dir}/${device_id}
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startTime=`date +%Y%m%d-%H:%M:%S`
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startTime_s=`date +%s`
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if [ x"${mode}" == x"evaluate" ];then
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eval_data_url="--data=${data_url} --evaluate --resume=${ckpt_path}"
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else
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eval_data_url="--data=${data_url}"
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fi
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if [ x"${rank_size}" == x"1" ];then
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python3.7 ${currentDir}/code/pytorch-resnet50-apex.py \
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${eval_data_url} \
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--workers=64 \
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--epochs=${epoches} \
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--batch-size=${batch_size} \
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--learning-rate=${lr} \
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--warmup=5 \
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--label-smoothing=0.1 \
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--optimizer-batch-size=1024 \
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--npu=${device_id} > ${train_job_dir}/train_1p.log 2>&1
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else
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export KERNEL_NAME_ID=${device_id}
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rank_id=$6
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python3.7 ${currentDir}/code/DistributedResnet50/main-apex-d76-npu.py \
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--data=${data_url} \
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--addr=$(hostname -I |awk '{print $1}') \
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--seed=49 \
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--workers=184 \
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--learning-rate=${lr} \
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--warmup=8 \
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--label-smoothing=0.1 \
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--mom=0.875 \
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--weight-decay=3.0517578125e-05 \
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--static-loss-scale=128 \
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--print-freq=1 \
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--dist-url='tcp://127.0.0.1:50000' \
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--dist-backend='hccl' \
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--multiprocessing-distributed \
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--world-size=1 \
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--rank=${rank_id} \
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--gpu=${device_id} \
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--benchmark=0 \
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--device='npu' \
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--epochs=${epoches} \
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--device_num=${rank_size} \
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--batch-size=${batch_size} > ${train_job_dir}/train_${rank_size}p.log 2>&1
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fi
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if [ $? -eq 0 ] ;
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then
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echo ":::ABK 1.0.0 resnet50 train success"
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echo ":::ABK 1.0.0 resnet50 train success" >> ${train_job_dir}/train_${rank_size}p.log
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echo ":::ABK 1.0.0 resnet50 train success" >> ${train_job_dir}/${device_id}/hw_resnet50.log
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else
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echo ":::ABK 1.0.0 resnet50 train failed"
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echo ":::ABK 1.0.0 resnet50 train failed" >>${train_job_dir}/train_${rank_size}p.log
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echo ":::ABK 1.0.0 resnet50 train failed" >> ${train_job_dir}/${device_id}/hw_resnet50.log
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fi
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endTime=`date +%Y%m%d-%H:%M:%S`
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endTime_s=`date +%s`
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sumTime=$[ $endTime_s - $startTime_s ]
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hour=$(( $sumTime/3600 ))
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min=$(( ($sumTime-${hour}*3600)/60 ))
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sec=$(( $sumTime-${hour}*3600-${min}*60 ))
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echo ":::ABK 1.0.0 resnet50 train total time：${hour}:${min}:${sec}"
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echo ":::ABK 1.0.0 resnet50 train total time：${hour}:${min}:${sec}" >> ${train_job_dir}/${device_id}/hw_resnet50.log
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