diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/README.md b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/README.md
index 44ac5abee4decf4405834e142b293ff88f17a1ef..9964bcbfe0fbf4665d60294d83a6b88a0994da30 100644
--- a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/README.md
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/README.md
@@ -195,6 +195,10 @@ pip3 install requirements.txt
     4. 8卡训练
        
         4.1 首先检查 MiniGo_ID0629_for_TensorFlow/test 目录下是否有存在8卡IP的json配置文件 "8p.json"
+        （export CMxxx环境变量方式拉起训练无需检查）        
+
+        环境变量方式拉起详情请参考：    
+        https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/63RC1alpha002/tfmoddevg/tfmigr1/atlasmprtg_13_9036.html
 
 ​              4.2 设置单卡训练参数（脚本位于./MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh），示例如下。
 
@@ -207,7 +211,9 @@ pip3 install requirements.txt
 ​              4.3 单卡训练指令（脚本位于./MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh） 
 
     ```
-    bash train_full_8p.sh --data_path=xx
+    bash train_full_8p.sh --data_path=xx（ranktable方式）
+    bash train_full_8p.sh --data_path=xx --one_node_ip=本机IP（去ranktable--CM环境变量方式）
+
     数据集路径默认为 MiniGo_ID0629_for_TensorFlow/outputs/data/selfplay（即Step2自对弈的生成路径，不建议改动）
     数据集应有如下结构（数据切分可能不同），配置data_path时需指定为selfplay这一层，例：--data_path=./outputs/data/selfplay
     ├─selfplay
diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh
index 8cd78e2f2b804957f26e967acf45a7359b05c13e..37476144b7e243ca19afef906f1edc40b2478316 100644
--- a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh
@@ -43,7 +43,9 @@ do
         name_bind="_bindcore"
 	elif [[ $para == --dynamic_input* ]];then
       dynamic_input=`echo ${para#*=}` 
-   fi
+    elif [[ $para == --one_node_ip* ]];then
+        one_node_ip=`echo ${para#*=}`
+    fi
 done
 
 if [[ $data_path  == "" ]];then
@@ -59,17 +61,25 @@ python3 bootstrap.py --work_dir=$cur_path/estimator_working_dir --export_path=$c
 wait
 
 export ASCEND_DEVICE_ID=0
-export RANK_SIZE=8
-export RANK_TABLE_FILE="${cur_path}/test/8p.json"
+export RANK_SIZES=8
+#export RANK_TABLE_FILE="${cur_path}/test/8p.json"
 export JOB_ID=10086
 
+#8p训练必须参数（本机IP）
+one_node_ip=$one_node_ip
+#新增适配集群环境变量
+export CM_CHIEF_IP=${one_node_ip}   #主节点ip，所有服务器一致
+export CM_CHIEF_PORT=29688          #通信端口，所有服务器一致
+export CM_CHIEF_DEVICE=0            #配置为0，配置主卡，类似于主节点，所有服务器一致
+export CM_WORKER_SIZE=8             #卡数，单机为8，所有服务器一致
+export CM_WORKER_IP=${one_node_ip}  #当前服务器ip，不同环境ip不同
 start=$(date +%s)
 
 # 8P训练模式
 for i in 0 1 2 3 4 5 6 7
 do
     #设置环境变量
-    export RANK_ID=$i
+    export RANK_IDS=$i
     export ASCEND_DEVICE_ID=$i
     ASCEND_DEVICE_ID=$i
     echo "Device ID: $ASCEND_DEVICE_ID"
@@ -83,8 +93,8 @@ do
     echo $ASCEND_DEVICE_ID
     #(Step3)训练
     corenum=`cat /proc/cpuinfo |grep 'processor' |wc -l`
-    let a=RANK_ID*${corenum}/8
-    let b=RANK_ID+1
+    let a=RANK_IDS*${corenum}/8
+    let b=RANK_IDS+1
     let c=b*${corenum}/8-1
     if [ "x${bind_core}" != x ];then
         bind_core="taskset -c $a-$c"
diff --git a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_8p.sh
index 254cad2ad44821779738f3a202600f7f8c65f3ee..d00a27957f442fd9afdb42266506ba11a3d71111 100644
--- a/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_8p.sh
+++ b/TensorFlow/built-in/cv/image_classification/ResNet50_ID0058_for_TensorFlow/test/train_full_8p.sh
@@ -5,8 +5,8 @@ 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 RANK_SIZE=8
-export RANK_TABLE_FILE=${cur_path}/../configs/8p.json
+export RANK_SIZES=8
+#export RANK_TABLE_FILE=${cur_path}/../configs/8p.json
 export JOB_ID=10087
 RANK_ID_START=0
 
@@ -21,7 +21,7 @@ Network="ResNet50_ID0058_for_TensorFlow"
 export HCCL_CONNECT_TIMEOUT=600
 corenum=`cat /proc/cpuinfo |grep "processor"|wc -l`
 export RANK_INDEX=0
-export RANK_ID=0
+export RANK_IDS=0
 
 config_file=res50_256bs_8p_eval
 iterations_per_loop=100
@@ -86,9 +86,20 @@ do
     elif [[ $para == --bind_core* ]]; then
         bind_core=`echo ${para#*=}`
         name_bind="_bindcore"
+    elif [[ $para == --one_node_ip* ]];then
+        one_node_ip=`echo ${para#*=}`
     fi
 done
 
+#8p训练必须参数（本机IP）
+one_node_ip=$one_node_ip
+#新增适配集群环境变量
+export CM_CHIEF_IP=${one_node_ip}   #主节点ip，所有服务器一致
+export CM_CHIEF_PORT=29688          #通信端口，所有服务器一致
+export CM_CHIEF_DEVICE=0            #配置为0，配置主卡，类似于主节点，所有服务器一致
+export CM_WORKER_SIZE=8             #卡数，单机为8，所有服务器一致
+export CM_WORKER_IP=${one_node_ip}  #当前服务器ip，不同环境ip不同
+
 #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"
@@ -104,16 +115,16 @@ fi
 
 #训练开始时间，不需要修改
 start_time=$(date +%s)
-
+sed -i 's/RANK_SIZE/RANK_SIZES/g' ../src/data_loader/resnet50/data_loader.py
 #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<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+for((RANK_IDS=$RANK_ID_START;RANK_IDS<$((RANK_SIZES+RANK_ID_START));RANK_IDS++));
 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
+    echo "Device ID: $RANK_IDS"
+    # export RANK_IDS=$RANK_IDS
+    export ASCEND_DEVICE_ID=$RANK_IDS
+    ASCEND_DEVICE_ID=$RANK_IDS
     export DEVICE_ID=$ASCEND_DEVICE_ID
 	DEVICE_INDEX=$(( DEVICE_ID + RANK_INDEX * 8 ))
 	export DEVICE_INDEX=$DEVICE_INDEX
@@ -128,8 +139,8 @@ do
 
     #执行训练脚本，需要模型审视修改
     corenum=`cat /proc/cpuinfo |grep 'processor' |wc -l`
-    let a=RANK_ID*${corenum}/8
-    let b=RANK_ID+1
+    let a=RANK_IDS*${corenum}/8
+    let b=RANK_IDS+1
     let c=b*${corenum}/8-1
     if [ "x${bind_core}" != x ];then
         bind_core="taskset -c $a-$c"
@@ -158,6 +169,7 @@ wait
 #训练结束时间，不需要修改
 end_time=$(date +%s)
 e2e_time=$(( $end_time - $start_time ))
+sed -i 's/RANK_SIZES/RANK_SIZE/g' src/data_loader/resnet50/data_loader.py
 
 echo "------------------ Final result ------------------"
 #单step时长，需要从train_$ASCEND_DEVICE_ID.log里，通过关键字获取。需要模型审视修改
@@ -174,13 +186,13 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZES}'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}'}'`
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZES}'*1000/'${FPS}'}'`
 
 
 ##获取性能数据，不需要修改
@@ -197,7 +209,7 @@ ActualLoss=`awk 'END {print}' $cur_path/output/$ASCEND_DEVICE_ID/train_${CaseNam
 
 #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 "RankSize = ${RANK_SIZES}" >> $cur_path/output/$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
@@ -205,4 +217,4 @@ echo "ActualFPS = ${ActualFPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName
 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
\ No newline at end of file
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/README.md b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/README.md
index 1209d2ef91a4b327ceadf1b06792792ad8b1b754..b13088414c2134693db7f8241d8ee0b416723a48 100644
--- a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/README.md
+++ b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/README.md
@@ -181,6 +181,8 @@ run_config = NPURunConfig(
      bash run_accuracy_8p.sh  --data_dir=/data/BraTS2019Train
      ```
 
+     环境变量方式拉起详情请参考：
+     https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/63RC1alpha002/tfmoddevg/tfmigr1/atlasmprtg_13_9036.html
 ## 高级参考
 
 #### 脚本和示例代码<a name="section08421615141513"></a>
diff --git a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/scripts/run_accuracy_8p.sh b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/scripts/run_accuracy_8p.sh
index 771f5fb921c1d2f0df9f4bca94c0293c7172f86a..95427b89cb5ecd46efefe2742bcd1ec0655fbce0 100644
--- a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/scripts/run_accuracy_8p.sh
+++ b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/scripts/run_accuracy_8p.sh
@@ -9,8 +9,8 @@ mkdir -p ${upDir}/test/output/0
 
 # user env
 export JOB_ID=NPU20210126
-export RANK_SIZE=8
-export RANK_TABLE_FILE=${currentDir}/8p.json
+export RANK_SIZES=8
+#export RANK_TABLE_FILE=${currentDir}/8p.json
 
 data_dir=$1
 fold=$2
@@ -21,7 +21,7 @@ if [ x"${fold}" = x"all" ] ;
 then
     for device_index in ${device_group}
     do
-        RANK_ID=${device_index} ASCEND_DEVICE_ID=${device_index} ${currentDir}/train_accuracy_8p.sh ${data_dir} 0 &
+        RANK_IDS=${device_index} ASCEND_DEVICE_ID=${device_index} ${currentDir}/train_accuracy_8p.sh ${data_dir} 0 &
     done
 
     wait
@@ -32,7 +32,7 @@ else
     echo "[`date +%Y%m%d-%H:%M:%S`] [INFO] fold$fold train start"
     for device_index in ${device_group}
     do
-        RANK_ID=${device_index} ASCEND_DEVICE_ID=${device_index} ${currentDir}/train_accuracy_8p.sh ${data_dir} ${fold} &
+        RANK_IDS=${device_index} ASCEND_DEVICE_ID=${device_index} ${currentDir}/train_accuracy_8p.sh ${data_dir} ${fold} &
     done
 
     wait
diff --git a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/test/train_full_8p.sh
index 02a6ba19f7063dd1c47bedf34e76ffa88dc2f538..b69de76e242e2f58effe146caae2f1b60358e16c 100644
--- a/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/test/train_full_8p.sh
+++ b/TensorFlow/built-in/cv/image_segmentation/UNet3D_ID0057_for_TensorFlow/test/train_full_8p.sh
@@ -5,9 +5,9 @@ cur_path=`pwd`
 
 export LANG=en_US.UTF-8
 
-export RANK_SIZE=8
+export RANK_SIZES=8
 export JOB_ID=10087
-export RANK_TABLE_FILE=$cur_path/../scripts/8p.json
+#export RANK_TABLE_FILE=$cur_path/../scripts/8p.json
 RANK_ID_START=0
 
 
@@ -71,9 +71,19 @@ do
         cp -rf $install_path/fwkacllib/data/rl/Ascend910/custom ${autotune_dump_path}/RL/
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
+    elif [[ $para == --one_node_ip* ]];then
+        one_node_ip=`echo ${para#*=}`
     fi
 done
 
+#8p训练必须参数（本机IP）
+one_node_ip=$one_node_ip
+#新增适配集群环境变量
+export CM_CHIEF_IP=${one_node_ip}   #主节点ip，所有服务器一致
+export CM_CHIEF_PORT=29688          #通信端口，所有服务器一致
+export CM_CHIEF_DEVICE=0            #配置为0，配置主卡，类似于主节点，所有服务器一致
+export CM_WORKER_SIZE=8             #卡数，单机为8，所有服务器一致
+export CM_WORKER_IP=${one_node_ip}  #当前服务器ip，不同环境ip不同
 
 #data_path='../'
 #校验是否传入data_path,不需要修改
@@ -81,18 +91,23 @@ if [[ $data_path == "" ]];then
     echo "[Error] para \"data_path\" must be confing"
     exit 1
 fi
-
+cd $cur_path/../
+sed -i 's/RANK_SIZE/RANK_SIZES/g' model/model_fn.py pbinference/unet3d_pb_inference.sh main_npu.py dataset/data_loader.py runtime/hooks.py runtime/setup.py
+sed -i 's/RANK_ID/RANK_IDS/g' pbinference/unet3d_pb_inference.sh main_npu.py dataset/data_loader.py runtime/hooks.py runtime/setup.py
 cd $cur_path/../scripts
 
 #训练开始时间，不需要修改
 start_time=$(date +%s)
 
 bash run_accuracy_8p.sh ${data_path} all
+wait
 
 #训练结束时间，不需要修改
 end_time=$(date +%s)
 e2e_time=$(( $end_time - $start_time ))
-
+cd $cur_path/../
+sed -i 's/RANK_SIZES/RANK_SIZE/g' model/model_fn.py pbinference/unet3d_pb_inference.sh main_npu.py dataset/data_loader.py runtime/hooks.py runtime/setup.py
+sed -i 's/RANK_IDS/RANK_ID/g' pbinference/unet3d_pb_inference.sh main_npu.py dataset/data_loader.py runtime/hooks.py runtime/setup.py
 
 sleep 30
 train_accuracy=`grep -r "whole" $cur_path/output/0/train_0.log | awk '{print $6}'`
@@ -105,7 +120,6 @@ echo "E2E Training Duration sec : $e2e_time"
 echo "------------------ Final result ------------------"
 #输出性能FPS，需要模型审视修改
 fps=`grep throughput_train $cur_path/output/0/train_0.log|awk -F 'throughput_train' '{print $2}'|awk -F ':' '{print $2}'|awk '{print $1}'`
-#FPS=`awk 'BEGIN{printf "%.2f\n",'${RANK_SIZE}'*'${fps}'}'`
 FPS=1.5
 #打印，不需要修改
 echo "Final Performance images/sec : $FPS"
@@ -116,13 +130,13 @@ echo "E2E Training Duration sec : $e2e_time"
 #训练用例信息，不需要修改
 BatchSize=${batch_size}
 DeviceType=`uname -m`
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+CaseName=${Network}_bs${BatchSize}_${RANK_SIZES}'p'_'acc'
 
 #获取性能数据，不需要修改
 #吞吐量
 ActualFPS=${FPS}
 #单迭代训练时长
-TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZE}'*1000/'${FPS}'}'`
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*'${RANK_SIZES}'*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
@@ -133,7 +147,7 @@ ActualLoss=`awk 'END {print}' $cur_path/output/0/train_${CaseName}_loss.txt`
 
 #关键信息打印到${CaseName}.log中，不需要修改
 echo "Network = ${Network}" > $cur_path/output/0/${CaseName}.log
-echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/0/${CaseName}.log
+echo "RankSize = ${RANK_SIZES}" >> $cur_path/output/0/${CaseName}.log
 echo "BatchSize = ${BatchSize}" >> $cur_path/output/0/${CaseName}.log
 echo "DeviceType = ${DeviceType}" >> $cur_path/output/0/${CaseName}.log
 echo "CaseName = ${CaseName}" >> $cur_path/output/0/${CaseName}.log
diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/README.md b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/README.md
index c9e116c76e1d5b02ae8b1bbb145b9dd980d30dc8..d506b9e46a30f7651341afbbd002a43225f3a2b4 100644
--- a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/README.md
+++ b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/README.md
@@ -118,7 +118,15 @@ pip3 install requirements.txt
  bash train_performance_1p.sh --data_path=/data  (功能和性能)
  bash train_full_1p.sh --data_path=/data        （全量）
  ```
+- 8卡训练-CMxxx环境变量方式拉起
+ 以数据集放在/data为例
+
+环境变量方式拉起详情请参考：
+https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/63RC1alpha002/tfmoddevg/tfmigr1/atlasmprtg_13_9036.html
+
+ bash train_performance_8p.sh --data_path=/data --one_node_ip=本机IP
 
+ bash train_full_8p.sh --data_path=/data --one_node_ip=本机IP
 ## 高级参考
 
 #### 脚本和示例代码<a name="section08421615141513"></a>
diff --git a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh
index e58e09555687d14d31fe22af0948add099c5f108..7250053b376a4ff13fa825c662658b18fab2bd60 100644
--- a/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh
+++ b/TensorFlow/built-in/recommendation/WideDeep_ID2712_for_TensorFlow/test/train_full_8p.sh
@@ -4,13 +4,11 @@
 cur_path=`pwd`
 
 #集合通信参数,不需要修改
-
-
 export HCCL_CONNECT_TIMEOUT=1200
 
 #集合通信参数,不需要修改
-export RANK_SIZE=8
-export RANK_TABLE_FILE=$cur_path/8p.json
+export RANK_SIZES=8
+#export RANK_TABLE_FILE=$cur_path/8p.json
 export JOB_ID=10087
 RANK_ID_START=0
 ASCEND_DEVICE_ID_START=0
@@ -57,14 +55,19 @@ elif [[ $para == --over_dump* ]];then
         mkdir -p ${over_dump_path}
     elif [[ $para == --data_path* ]];then
         data_path=`echo ${para#*=}`
+    elif [[ $para == --one_node_ip* ]];then
+        one_node_ip=`echo ${para#*=}`
     fi
 done
 
-#校验是否传入data_path,不需要修改
-if [[ $data_path == "" ]];then
-    echo "[Error] para \"data_path\" must be confing"
-    exit 1
-fi
+#8p训练必须参数（本机IP）
+one_node_ip=$one_node_ip
+#新增适配集群环境变量
+export CM_CHIEF_IP=${one_node_ip}   #主节点ip，所有服务器一致
+export CM_CHIEF_PORT=29688          #通信端口，所有服务器一致
+export CM_CHIEF_DEVICE=0            #配置为0，配置主卡，类似于主节点，所有服务器一致
+export CM_WORKER_SIZE=8             #卡数，单机为8，所有服务器一致
+export CM_WORKER_IP=${one_node_ip}  #当前服务器ip，不同环境ip不同
 
 #校验是否传入data_path,不需要修改
 if [[ $data_path == "" ]];then
@@ -102,6 +105,8 @@ sed -i "s%/npu/traindata/ID2940_CarPeting_TF_WideDeep_TF%${data_path}%p" configs
 sed -i "s%./model%$cur_path/output/$ASCEND_DEVICE_ID/ckpt%p" configs/config.py
 sed -i "s%display_step = 100%display_step = $display_step%p" configs/config.py
 sed -i "s%n_epoches = 2%n_epoches = $n_epoches%p" configs/config.py
+sed -i 's/RANK_SIZE/RANK_SIZES/g' widedeep/WideDeep_fp16_huifeng.py
+sed -i 's/RANK_SIZE/RANK_SIZES/g' train.py
 #echo `cat configs/config.py |uniq > configs/config.py; cp -f configs/config.py configs/config.py.run`
 cp configs/config.py configs/config.py.run
 
@@ -109,13 +114,13 @@ cp configs/config.py configs/config.py.run
 cd $cur_path/../
 
 start=$(date +%s)
-for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+for((RANK_IDS=$RANK_ID_START;RANK_IDS<$((RANK_SIZES+RANK_ID_START));RANK_IDS++));
 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
+    echo "Device ID: $RANK_IDS"
+    export RANK_IDS=$RANK_IDS
+    export ASCEND_DEVICE_ID=$RANK_IDS
+    ASCEND_DEVICE_ID=$RANK_IDS
   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}
@@ -131,7 +136,8 @@ done
 wait
 end=$(date +%s)
 e2e_time=$(( $end - $start ))
-
+sed -i 's/RANK_SIZES/RANK_SIZE/g' train.py
+sed -i 's/RANK_SIZES/RANK_SIZE/g' widedeep/WideDeep_fp16_huifeng.py
 #配置文件恢复
 mv -f configs/config.py.bak configs/config.py
 
@@ -155,9 +161,9 @@ echo "E2E Training Duration sec : $e2e_time"
 BatchSize=${batch_size}
 DeviceType=`uname -m`
 if [[ $precision_mode == "must_keep_origin_dtype" ]];then
-    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'acc'
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZES}'p'_'fp32'_'acc'
 else
-    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZES}'p'_'acc'
 fi
 
 ##获取性能数据
@@ -176,7 +182,7 @@ ActualLoss=`cat $cur_path/output/$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 "RankSize = ${RANK_SIZES}" >> $cur_path/output/$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