diff --git a/TensorFlow/built-in/audio/Tacotron2-v1-Encoder_ID1997_for_TensorFlow/test/train_RT2_full_1p.sh b/TensorFlow/built-in/audio/Tacotron2-v1-Encoder_ID1997_for_TensorFlow/test/train_RT2_full_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ad7ec725689ee63912562cf7e77fe8301d1ee8e6
--- /dev/null
+++ b/TensorFlow/built-in/audio/Tacotron2-v1-Encoder_ID1997_for_TensorFlow/test/train_RT2_full_1p.sh
@@ -0,0 +1,206 @@
+#!/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="Tacotron2-v1-Encoder_ID1997_for_TensorFlow"
+#训练epoch
+train_epochs=2
+#训练step
+train_steps=100
+#训练batch_size
+batch_size=16
+#学习率
+learning_rate=1e-3
+
+#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
+autotune=False
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo "usage:./train_full_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
+    --dynamic_bs             dynamic batch size, 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 == --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 == --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 == --dynamic_bs* ]];then
+        dynamic_bs=`echo ${para#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+#训练开始时间，不需要修改
+start_time=$(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 train_rt.py \
+		--log_dir=${cur_path}/output \
+		--log_name="test" \
+		--data_paths=${data_path} \
+		--epoch=${train_epochs} \
+		--batch_size=${batch_size} \
+		--dynamic_bs=${dynamic_bs} \
+		--precision_mode=${precision_mode} \
+		--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=${data_dump_path} \
+		--profiling=${profiling} \
+		--profiling_dump_path=${profiling_dump_path} \
+		--deltree=True > ${cur_path}/output/${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
+t_step_time=`grep "perf = " $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log  | tail -50 | awk -F "=" '{print $3}' | awk '{sum+=$1} END {print sum}'`
+FPS=`awk 'BEGIN {printf "%.2f\n", '${batch_size}'*'50'/'${t_step_time}'}'`
+
+TrainingTime=`awk 'BEGIN {printf "%.2f\n", '1000'*'${batch_size}'/'${FPS}'}'`
+#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 : $e2e_time"
+
+#稳定性精度看护结果汇总
+#训练用例信息，不需要修改
+BatchSize=${batch_size}
+DeviceType=`uname -m`
+if [[ $dynamic_bs == "" || $dynamic_bs == "False" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+else
+    CaseName=${Network}_dynamic_bs${BatchSize}_${RANK_SIZE}'p'_'RT2'_'acc'
+fi
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${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 '{print $6}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+
+#最后一个迭代loss值，不需要修改
+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 "TrainAccuracy = Loss" >> $cur_path/output/$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/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/built-in/cv/image_classification/PixelLink_ID3056_for_TensorFlow/test/train_RT2_full_1p.sh b/TensorFlow/built-in/cv/image_classification/PixelLink_ID3056_for_TensorFlow/test/train_RT2_full_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..94ea3d14f20908990e5d007558ad49ef6d361abc
--- /dev/null
+++ b/TensorFlow/built-in/cv/image_classification/PixelLink_ID3056_for_TensorFlow/test/train_RT2_full_1p.sh
@@ -0,0 +1,169 @@
+#!/bin/bash
+set -x
+#当前路径,不需要修改
+cur_path=`pwd`
+export PYTHONPATH=${cur_path}/../pylib/src:$PYTHONPATH
+#集合通信参数,不需要修改
+
+export RANK_SIZE=1
+export JOB_ID=10087
+RANK_ID_START=0
+RankSize=1
+# 数据集路径,保持为空,不需要修改
+data_path=""
+#export ASCEND_SLOG_PRINT_TO_STDOUT=1
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="PixelLink_ID3056_for_TensorFlow"
+#训练epoch
+train_epochs=
+#训练batch_size
+batch_size=24
+#训练step
+train_steps=60000
+#学习率
+learning_rate=
+
+#维测参数，precision_mode需要模型审视修改
+precision_mode="allow_fp32_to_fp16"
+#维持参数，以下不需要修改
+over_dump=False
+data_dump_flag=False
+data_dump_step="10"
+profiling=False
+
+# 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
+    -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#*=}`
+    fi
+done
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path/..
+
+for((RANK_ID=$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_dir, --model_dir, --precision_mode, --over_dump, --over_dump_path，--data_dump_flag，--data_dump_step，--data_dump_path，--profiling，--profiling_dump_path
+    nohup python3 train_pixel_link.py \
+            --train_dir=./models/pixel_link \
+            --num_gpus=1 \
+            --learning_rate=1e-3 \
+            --gpu_memory_fraction=-1 \
+            --train_image_width=512 \
+            --train_image_height=512 \
+            --batch_size=${batch_size}\
+            --dataset_dir=${data_path} \
+            --dataset_name=icdar2015 \
+            --dynamic_loss_scale=True \
+            --dataset_split_name=train \
+            --max_number_of_steps=${train_steps} \
+            --checkpoint_path=${CKPT_PATH} \
+            --using_moving_average=1 > ${cur_path}/output/${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 "------------------ Final result ------------------"
+
+#单迭代训练时长
+TrainingTime=`grep 'loss =' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk -F "(" '{print $2}' |awk -F " " '{print $1}' |tail -10|awk '{sum+=$1}END {print"",sum/NR}'|sed s/[[:space:]]//g`
+# #输出性能FPS，需要模型审视修改
+FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${TrainingTime}'}'`
+#打印，不需要修改
+echo "Final Performance item/sec : $FPS"
+
+
+# #输出训练精度,需要模型审视修改
+#train_accuracy=`grep "test AUC" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $3}'`
+# #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}_${RANK_SIZE}'p'_'RT2'_'acc'
+
+#吞吐量
+ActualFPS=${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 " " '{print $6}' > $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt
+
+#最后一个迭代loss值，不需要修改
+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 "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$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
\ No newline at end of file