diff --git a/TensorFlow/built-in/cv/detection/SSD-Resnet50V1-FPN_ID1463_for_TensorFlow/test/train_RT2_full_8p_inceptionv2.sh b/TensorFlow/built-in/cv/detection/SSD-Resnet50V1-FPN_ID1463_for_TensorFlow/test/train_RT2_full_8p_inceptionv2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7340a8251614a66f5692f51bdecdf13818ae62c9
--- /dev/null
+++ b/TensorFlow/built-in/cv/detection/SSD-Resnet50V1-FPN_ID1463_for_TensorFlow/test/train_RT2_full_8p_inceptionv2.sh
@@ -0,0 +1,142 @@
+#!bin/bash
+cur_path=`pwd`
+export PYTHONPATH=$cur_path/../models/research:$cur_path/../models/research/slim:$PYTHONPATH
+#集合通信
+export RANK_SIZE=8
+export RANK_TABLE_FILE=$cur_path/../configs/${RANK_SIZE}p.json
+export JOB_ID=10087
+RANK_ID_START=0
+ASCEND_DEVICE_ID_START=0
+
+#数据集参数
+data_path="/data"
+
+#训练参数，需要根据模型修改
+Network="SSD-InceptionV2_ID0510_for_TensorFlow"
+num_train_steps=50000
+batch_size=24
+ckpt_path=/checkpoints
+pipeline_config=$cur_path/../models/research/configs/ssd_inception_v2_coco_8p.config
+
+#帮助提示，需要根据网络修改
+if [[ $1 == --help || $1 == -h ]];then 
+	echo "usage: ./train_full_8p_inceptionv2.sh <args>"
+
+	echo ""
+	echo "parameter explain:
+	--num_train_steps           training steps
+	--data_path                 source data of training
+	--ckpt_path                  pre-checkpoint path
+	--pipeline_config           pipeline config path
+    -h/--help             Show help message
+	"
+	exit 1
+fi
+
+#入参设置，需要根据网络修改
+for para in $*
+do
+    if [[ $para == --num_train_steps* ]];then
+		num_train_steps=`echo ${para#*=}`
+	elif [[ $para == --data_path* ]];then
+		data_path=`echo ${para#*=}`
+    elif [[ $para == --bind_core* ]]; then
+        bind_core=`echo ${para#*=}`
+        name_bind="_bindcore"
+	elif [[ $para == --ckpt_path* ]];then
+		ckpt_path=`echo ${para#*=}`
+	elif [[ $para == --pipeline_config* ]];then
+		pipeline_config=`echo ${para#*=}`
+    fi
+done	
+
+if [[ $data_path == "" ]];then
+	echo "[Error] para \"data_path\" must be config"
+	exit 1
+fi
+
+
+
+##########################执行训练#########################
+start_time=$(date +%s)
+cd $cur_path/../models/research
+if [  -f ${pipeline_config}.bak ];then
+   cp ${pipeline_config}.bak ${pipeline_config}
+else
+   cp ${pipeline_config} ${pipeline_config}.bak
+fi
+
+sed -i "s%/checkpoints%${ckpt_path}%p" ${pipeline_config} 
+sed -i "s%/data/coco2017_tfrecords%${data_path}/coco2017_tfrecords%p" ${pipeline_config} 
+
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+ do
+  export RANK_ID=$RANK_ID
+  export ASCEND_DEVICE_ID=$((ASCEND_DEVICE_ID_START+RANK_ID))
+  echo "Device ID: $ASCEND_DEVICE_ID"
+  if [   -d $cur_path/output/${ASCEND_DEVICE_ID} ];then
+     rm -rf $cur_path/output/${ASCEND_DEVICE_ID}
+     mkdir -p $cur_path/output/${ASCEND_DEVICE_ID}
+  else
+     mkdir -p $cur_path/output/${ASCEND_DEVICE_ID}
+  fi
+
+#训练执行脚本，需要根据网络修改
+    corenum=`cat /proc/cpuinfo |grep 'processor' |wc -l`
+    let a=RANK_ID*${corenum}/8
+    let b=RANK_ID+1
+    let c=b*${corenum}/8-1
+    if [ "x${bind_core}" != x ];then
+        bind_core="taskset -c $a-$c"
+    fi
+  nohup ${bind_core} python3 -u ./object_detection/model_main_rt.py \
+       --pipeline_config_path=${pipeline_config} \
+       --model_dir=$cur_path/output/${ASCEND_DEVICE_ID_START} \
+       --data_path=${data_path}   \
+       --alsologtostder \
+       --amp \
+       --num_train_steps=${num_train_steps}  \
+       "${@:1}"  > $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
+done
+wait
+
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+##########################业务日志#########################
+grep ERROR $HOME/ascend/log/plog/*.log > $cur_path/output/$ASCEND_DEVICE_ID_START/plog_err.log
+
+################################性能结果处理#########################
+echo "-----------------------Final result------------------------"
+#性能FPS计算，需要根据网络修改
+FPS=`grep -a 'INFO:tensorflow:global_step/sec: ' $cur_path/output/$ASCEND_DEVICE_ID_START/train_$ASCEND_DEVICE_ID_START.log|awk 'END {print $2}'` 
+
+FPS=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'*'${FPS}'*'${RANK_SIZE}'}'`
+echo "Final Performance images/sec : $FPS"
+################################精度结果处理#########################
+#精度计算，需要根据网络修改
+train_accuracy=`grep Precision $cur_path/output/$ASCEND_DEVICE_ID_START/train_$ASCEND_DEVICE_ID_START.log|grep Average |awk 'NR==1 {print $13}'`
+
+echo 'Final Training Accuracy mAP: $train_accuracy'
+################################E2E训练时长##########################
+echo "Final Training Duration sec : $e2e_time"
+
+################################性能看护#############################
+DeviceType=`uname -m`
+CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'RT2'_'acc'
+ActualFPS=${FPS}
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'*1000/'${FPS}'}'`
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型修改
+grep INFO:tensorflow:loss $cur_path/output/$ASCEND_DEVICE_ID_START/train_$ASCEND_DEVICE_ID_START.log|awk '{print $3}'|sed 's/,//g'|sed '/^$/d' >> $cur_path/output/$ASCEND_DEVICE_ID_START/train_${CaseName}_loss.txt
+
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+echo "TrainingAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID_START/${CaseName}.log
+
diff --git a/TensorFlow/built-in/nlp/FastText_ID0135_for_TensorFlow/test/train_RT2_full_1p.sh b/TensorFlow/built-in/nlp/FastText_ID0135_for_TensorFlow/test/train_RT2_full_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ce08a6826d3c6362205b8387ddb0b5f289773084
--- /dev/null
+++ b/TensorFlow/built-in/nlp/FastText_ID0135_for_TensorFlow/test/train_RT2_full_1p.sh
@@ -0,0 +1,184 @@
+#!/bin/bash
+
+#当前路径,不需要修改
+cur_path=`pwd`
+
+#集合通信参数,不需要修改
+
+export RANK_SIZE=1
+export JOB_ID=10087
+RANK_ID_START=0
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="FastText_ID0135_for_TensorFlow"
+#训练epoch
+train_epochs=10
+#训练step
+train_steps=250000
+#训练batch_size
+batch_size=1024
+#学习率
+learning_rate=0.01
+
+#维测参数，precision_mode需要模型审视修改
+precision_mode="allow_mix_precision"
+#维持参数，以下不需要修改
+over_dump=False
+data_dump_flag=False
+data_dump_step="10"
+profiling=False
+loss_scale=True
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
+    --over_dump              if or not over detection, default is False
+    --data_dump_flag         data dump flag, default is False
+    --data_dump_step         data dump step, default is 10
+    --profiling              if or not profiling for performance debug, default is False
+    --data_path              source data of training
+    --max_step               # of step for training
+    --learning_rate          learning rate
+    --batch                  batch size
+    --modeldir               model dir
+    --save_interval          save interval for ckpt
+    --loss_scale             enable loss scale ,default is False
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+#参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --max_step* ]];then
+        train_steps=`echo ${para#*=}`
+    elif [[ $para == --learning_rate* ]];then
+        learning_rate=`echo ${para#*=}`
+    elif [[ $para == --batch* ]];then
+        batch_size=`echo ${para#*=}`
+    elif [[ $para == --modeldir* ]];then
+        modeldir=`echo ${para#*=}`
+    elif [[ $para == --save_interval* ]];then
+        save_interval=`echo ${para#*=}`
+    elif [[ $para == --loss_scale* ]];then
+        loss_scale=`echo ${para#*=}`
+    elif [[ $para == --epoch* ]];then
+        train_epochs=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+#############执行训练#########################
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+cd $cur_path/../
+#进入训练脚本目录，需要模型审视修改
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+
+    #创建DeviceID输出目录，不需要修改
+    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
+    else
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
+    fi
+
+    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
+    #--data_path, --model_dir, --precision_mode, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path，--autotune
+    nohup python3 main_rt.py \
+      --epoch=${train_epochs} \
+      --data_path=${data_path} \
+      --precision_mode=${precision_mode} \
+      --loss_scale=${loss_scale} \
+      --over_dump=${over_dump} \
+      --over_dump_path=${over_dump_path} \
+      --data_dump_flag=${data_dump_flag} \
+      --data_dump_step=${data_dump_step} \
+      --data_dump_path=${data_dump_path} \
+      --batch_size=${batch_size} \
+      --profiling=${profiling} \
+      --profiling_dump_path=${profiling_dump_path} > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+if [ $? -ne 0 ];then
+  exit 1
+fi
+done
+wait
+
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+#结果打印，不需要修改
+echo "------------------ Final result ------------------"
+#输出性能FPS，需要模型审视修改
+TrainingTime=`grep "us/step" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "/" 'END {print $2}' | awk -F " " '{print $5}' | awk -F "us" '{print $1}'`
+FPS=`awk 'BEGIN {printf "%.2f\n", '1000'*'1000'*'${batch_size}'/'${TrainingTime}'}'`
+#打印，不需要修改
+echo "Final Performance images/sec : $FPS"
+
+#输出训练精度,需要模型审视修改
+train_accuracy=`grep "Accuracy:" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F " " '{print $2}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${train_accuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+#性能看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'RT2'_'acc'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep "loss:" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk -F "loss:" '{print $2}' | awk -F " " '{print $1}'  > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt
+#最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/recommendation/DeepFM_ID0030_for_TensorFlow/test/train_RT2_full_8p.sh b/TensorFlow/built-in/recommendation/DeepFM_ID0030_for_TensorFlow/test/train_RT2_full_8p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d8300c4c00933df01a62e78cf180b7a07a2e9bfb
--- /dev/null
+++ b/TensorFlow/built-in/recommendation/DeepFM_ID0030_for_TensorFlow/test/train_RT2_full_8p.sh
@@ -0,0 +1,162 @@
+#!/bin/bash
+
+#当前路径,不需要修改
+cur_path=`pwd`
+
+export FLAG_ENABLE_DUMP=False
+export DUMP_PATH=/var/log/npu/dump
+export DUMP_STEP="0|2"
+export DUMP_MODE="all"
+mkdir -p $DUMP_PATH
+
+#集合通信参数,不需要修改
+#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下
+export RANK_SIZE=8
+batch_size=16000
+export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json
+export JOB_ID=10087
+RANK_ID_START=0
+
+sed -i "s/n_epoches = 2/n_epoches = 20/g" `grep -rl "n_epoches = 2" ${cur_path}/../configs/config.py`
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+
+
+#基础参数 需要模型审视修改
+#网络名称，同目录名称
+Network="DeepFM_ID0030_for_TensorFlow"
+
+#维持参数，以下不需要修改
+over_dump=False
+data_dump_flag=False
+data_dump_step="10"
+profiling=False
+#参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --precision_mode* ]];then
+        precision_mode=`echo ${para#*=}`
+    elif [[ $para == --over_dump* ]];then
+        over_dump=`echo ${para#*=}`
+        over_dump_path=${cur_path}/output/overflow_dump
+        mkdir -p ${over_dump_path}
+    elif [[ $para == --data_dump_flag* ]];then
+        data_dump_flag=`echo ${para#*=}`
+        data_dump_path=${cur_path}/output/data_dump
+        mkdir -p ${data_dump_path}
+    elif [[ $para == --data_dump_step* ]];then
+        data_dump_step=`echo ${para#*=}`
+    elif [[ $para == --profiling* ]];then
+        profiling=`echo ${para#*=}`
+        profiling_dump_path=${cur_path}/output/profiling
+        mkdir -p ${profiling_dump_path}
+    elif [[ $para == --autotune* ]];then
+        autotune=`echo ${para#*=}`
+        mv $install_path/fwkacllib/data/rl/Ascend910/custom $install_path/fwkacllib/data/rl/Ascend910/custom_bak
+        mv $install_path/fwkacllib/data/tiling/Ascend910/custom $install_path/fwkacllib/data/tiling/Ascend910/custom_bak
+        autotune_dump_path=${cur_path}/output/autotune_dump
+        mkdir -p ${autotune_dump_path}/GA
+        mkdir -p ${autotune_dump_path}/rl
+        cp -rf $install_path/fwkacllib/data/tiling/Ascend910/custom ${autotune_dump_path}/GA/
+        cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/rl/
+    elif [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --bind_core* ]]; then
+        bind_core=`echo ${para#*=}`
+        name_bind="_bindcore"
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#autotune时，先开启autotune执行单P训练，不需要修改
+if [[ $autotune == True ]]; then
+     sh -x train_full_1p.sh --autotune=$autotune --data_path=$data_path
+    wait
+    cp -rf $install_path/fwkacllib/data/tiling/Ascend910/custom ${autotune_dump_path}/GA/
+    cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/RL/
+    wait
+    autotune=False
+	export autotune=False
+	 
+	export RANK_SIZE=8
+    export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json
+    export JOB_ID=10087
+    RANK_ID_START=0
+	unset TE_PARALLEL_COMPILER	 
+fi
+
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+#进入训练脚本目录，需要模型审视修改
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    echo "Device ID: $RANK_ID"
+    export RANK_ID=$RANK_ID
+    export ASCEND_DEVICE_ID=$RANK_ID
+    export DEVICE_ID=$RANK_ID
+    ASCEND_DEVICE_ID=$RANK_ID
+
+    if [ -d ${cur_path}/output/${ASCEND_DEVICE_ID} ];then
+        rm -rf ${cur_path}/output/${ASCEND_DEVICE_ID}
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
+    else
+        mkdir -p ${cur_path}/output/$ASCEND_DEVICE_ID/ckpt
+    fi
+	
+    EXEC_DIR=$(pwd)
+
+    corenum=`cat /proc/cpuinfo |grep 'processor' |wc -l`
+    let a=RANK_ID*${corenum}/8
+    let b=RANK_ID+1
+    let c=b*${corenum}/8-1
+    if [ "x${bind_core}" != x ];then
+        bind_core="taskset -c $a-$c"
+    fi
+    nohup ${bind_core} python3.7 ${EXEC_DIR}/../train.py > ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+done
+wait
+
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+#结果打印，不需要修改
+echo "E2E training Duration sec: $e2e_time"
+
+#稳定性精度看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p'_'RT2'_'acc'
+
+TrainAccuracy=`grep "eval auc:" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $8}'`
+#打印，不需要修改
+echo "Final Train Accuracy : ${TrainAccuracy}"
+echo "E2E Training Duration sec : $e2e_time"
+
+##获取性能数据
+ActualFPS=`grep "fps" $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk 'END {print $22}'`
+temp1=`echo "1000 * ${batch_size} * ${RANK_SIZE}"|bc`
+TrainingTime=`echo "scale=2;${temp1} / ${ActualFPS}"|bc`
+#ActualFPS=`echo "${RANK_SIZE} * ${FPS}"|bc`
+grep 'loss =' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $9}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${BatchSize}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = ${DeviceType}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${TrainingTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${TrainAccuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log