diff --git a/TensorFlow/built-in/nlp/Nezha-large_for_TensorFlow/test/train_full_8p_CI.sh b/TensorFlow/built-in/nlp/Nezha-large_for_TensorFlow/test/train_full_8p_CI.sh
new file mode 100644
index 0000000000000000000000000000000000000000..356d14b9328ffc1d53131e705b6090af5e270711
--- /dev/null
+++ b/TensorFlow/built-in/nlp/Nezha-large_for_TensorFlow/test/train_full_8p_CI.sh
@@ -0,0 +1,216 @@
+#!/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
+#保证rank table file 文件rank_table_8p.json存放在和test同级的configs目录下
+export JOB_ID=9999001
+export RANK_SIZE=8
+export RANK_INDEX=0
+export RANK_TABLE_FILE=${cur_path}/../configs/rank_table_8p.json
+RANK_ID_START=0
+
+export HCCL_CONNECT_TIMEOUT=600
+export SLOG_PRINT_TO_STDOUT=0
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+
+#基础参数 需要模型审视修改
+#网络名称，同目录名称
+Network="Nezha-large_ID0062_for_TensorFlow"
+
+batch_size=64
+
+#TF2.X独有，不需要修改
+#export NPU_LOOP_SIZE=${train_steps}
+
+#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_8p.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 0
+    --data_dump_step		   data dump step, default is 10
+    --profiling		           if or not profiling for performance debug, default is False
+    --autotune                 whether to enable autotune, 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 == --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 [[ $autotune == True ]]; then
+    train_full_1p.sh --autotune=$autotune --data_path=$data_path
+    wait
+    autotune=False
+fi
+
+#训练开始时间，不需要修改
+start_time=$(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: $RANK_ID"
+    export RANK_ID=$RANK_ID
+    export ASCEND_DEVICE_ID=$RANK_ID
+    ASCEND_DEVICE_ID=$RANK_ID
+	
+	# 自行添加环境变量
+
+	export DEVICE_ID=$RANK_ID
+	DEVICE_INDEX=$DEVICE_ID
+    export DEVICE_INDEX=${DEVICE_INDEX}
+    
+    #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
+    
+    python3.7 src/pretrain/run_pretraining.py \
+	    --bert_config_file=${cur_path}/../configs/nezha_large_config.json \
+		--max_seq_length=128 \
+		--max_predictions_per_seq=20 \
+		--train_batch_size=64 \
+		--learning_rate=1e-4 \
+		--num_warmup_steps=1000 \
+		--num_train_steps=30000 \
+		--optimizer_type=lamb \
+		--manual_fp16=True \
+		--use_fp16_cls=True \
+		--input_files_dir=$data_path/wikipedia_128 \
+		--eval_files_dir=$data_path/wikipedia_128 \
+		--npu_bert_debug=False \
+		--npu_bert_use_tdt=True \
+		--do_train=True \
+		--num_accumulation_steps=1 \
+		--npu_bert_job_start_file= \
+		--iterations_per_loop=100 \
+		--save_checkpoints_steps=1000 \
+		--npu_bert_clip_by_global_norm=False \
+		--distributed=True \
+		--npu_bert_loss_scale=0 \
+		--output_dir=${cur_path}/output/$ASCEND_DEVICE_ID/ckpt \
+		> ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+        #--data_dump_flag=${data_dump_flag} \
+        #--data_dump_step=${data_dump_step} \
+        #--data_dump_path=${data_dump_path} \
+        #--profiling=${profiling} \
+        #--profiling_dump_path=${profiling_dump_path} \
+        #--autotune=${autotune} > ${cur_path}/output/${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 ------------------"
+#输出性能FPS，需要模型审视修改
+grep 'Throughput' $cur_path/output/0/train_0.log|awk '{print $6}' >> $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_Throughput.txt
+FPS=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseName}_Throughput.txt`
+#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="NULL"
+#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`
+CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+
+##获取性能数据
+#吞吐量，不需要修改
+ActualFPS=${FPS}
+#单迭代训练时长，不需要修改
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${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 'Throughput' $cur_path/output/0/train_0.log|awk '{print $21}' >> $cur_path/output/$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 = ${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