diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/run_squad.py b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/run_squad.py
index c245d2cb11859808e1f6d66977c551af52b7f794..85b3c8280d47606ddf763e9ebb6afa890daecdf6 100644
--- a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/run_squad.py
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/run_squad.py
@@ -916,7 +916,11 @@ def main():
             os.environ['MASTER_PORT'] = '29668'
             torch.npu.set_device("npu:%d" % args.local_rank)
             device = torch.device("npu:%d" % args.local_rank)
-            torch.distributed.init_process_group(backend='hccl', world_size=8, rank=args.local_rank)
+            if args.num_npu == 8:
+                torch.distributed.init_process_group(backend='hccl', world_size=8, rank=args.local_rank)
+            if args.num_npu == 16:
+                global_rank = int(os.getenv('RANK'))
+                torch.distributed.init_process_group(backend='hccl', world_size=16, rank=global_rank)
             n_npu = 1
         else:
             torch.cuda.set_device(args.local_rank)
diff --git a/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_performance_16p.sh b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_performance_16p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..73851904d391083cf8bc599189579e59a1085adc
--- /dev/null
+++ b/PyTorch/built-in/nlp/Bert-Squad_ID0470_for_PyTorch/test/train_performance_16p.sh
@@ -0,0 +1,225 @@
+#!/bin/bash
+export PATH=/usr/local/hdf5/bin:$PATH
+export LD_LIBRARY_PATH=/usr/local/hdf5/lib:$LD_LIBRARY_PATH
+export LIBRARY_PATH=/usr/local/hdf5/lib:$LIBRARY_PATH
+export CPATH=/usr/local/hdf5/include:$CPATH
+export HDF5_DISABLE_VERSION_CHECK=1
+#当前路径,不需要修改
+cur_path=`pwd`
+
+#集合通信参数,不需要修改
+export BMMV2_ENABLE=1
+export RANK_SIZE=8
+export JOB_ID=10087
+RANK_ID_START=0
+
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+ckpt_path=""
+conf_path=""
+server_index=""
+fix_node_ip=""
+devicesnum=""
+
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Bert-Squad_ID0470_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=32
+#训练step
+train_steps=
+#学习率
+learning_rate=2e-4
+
+
+#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#*=}`
+    elif [[ $para == --ckpt_path* ]];then
+        ckpt_path=`echo ${para#*=}`
+	elif [[ $para == --fix_node_ip* ]];then
+	    fix_node_ip=`echo ${para#*=}`
+	elif [[ $para == --devicesnum* ]];then
+	    devicesnum=`echo ${para#*=}`
+    elif [[ $para == --conf_path* ]];then
+            conf_path=`echo ${para#*=}`
+    elif [[ $para == --server_index* ]];then
+            server_index=`echo ${para#*=}`
+    fi
+done
+
+one_node_ip=`find $conf_path -name "server_*0.info"|awk -F "server_" '{print $2}'|awk -F "_" '{print $1}'`
+linux_num=`find $conf_path -name "server_*.info" |wc -l`
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+#训练开始时间，不需要修改
+
+
+
+export HCCL_IF_IP=$fix_node_ip
+export MASTER_ADDR=$one_node_ip
+export MASTER_PORT=29688
+export HCCL_WHITELIST_DISABLE=1
+device_num=${#devicesnum}
+devices_num=`awk 'BEGIN{printf "%.0f\n",'${device_num}'-1}'`
+
+start_time=$(date +%s)
+NPUS=($(seq 0 $devices_num))
+rank_server=`awk 'BEGIN{printf "%.0f\n",'${device_num}'*'${server_index}'}'`
+export NPU_WORLD_SIZE=`awk 'BEGIN{printf "%.0f\n",'${device_num}'*'${linux_num}'}'`
+
+
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path/../
+rank=0
+for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    export ASCEND_DEVICE_ID=$RANK_ID
+    echo "Device ID: $ASCEND_DEVICE_ID"
+    export RANK_ID=$RANK_ID
+    export RANK=`awk 'BEGIN{printf "%.0f\n",'${rank}'+'${rank_server}'}'`
+
+
+    #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 python3.7 run_squad.py \
+          --init_checkpoint ${ckpt_path}/bert_large_pretrained_amp.pt \
+          --bert_model bert-large-uncased \
+		  --do_train \
+		  --train_file ${data_path}/train-v1.1-min.json \
+		  --train_batch_size ${batch_size} \
+		  --do_predict \
+		  --predict_batch_size ${batch_size} \
+		  --predict_file ${data_path}/dev-v1.1.json \
+		  --learning_rate ${learning_rate} \
+		  --num_train_epochs ${train_epochs} \
+		  --seed 1 \
+		  --fp16 \
+		  --max_steps 100 \
+		  --use_npu \
+		  --loss_scale 4096 \
+		  --vocab_file "data/uncased_L-24_H-1024_A-16/vocab.txt" \
+		  --do_eval \
+          --eval_script ${data_path}/evaluate-v1.1.py \
+		  --npu_id ${ASCEND_DEVICE_ID} \
+		  --do_lower_case \
+		  --output_dir ${cur_path}/../results \
+		  --config_file bert_config.json \
+		  --num_npu 16 \
+		  --local_rank=$RANK_ID \
+		  --json-summary ${cur_path}/output/${ASCEND_DEVICE_ID}/dllogger.json> ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
+		  let rank++
+done 
+wait
+
+#训练结束时间，不需要修改
+end_time=$(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
+step_time=`grep 'step_time : ' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log| awk '{print$13}'| tail -n +3 |awk '{sum+=$1} END {print"",sum/NR}' | sed s/[[:space:]]//g`
+
+FPS=`awk 'BEGIN{printf "%d\n", '$batch_size'/'$step_time'*16}'`
+
+#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 'F1 : ' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk '{print $10}'` 
+#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}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+#单迭代训练时长
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${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 -r "step_loss :" $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $19}' > $cur_path/output/$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 "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+rm -rf ${data_path}/train-v1.1-min.json_bert-large-uncased_384_128_64
+export BMMV2_ENABLE=0
\ No newline at end of file
diff --git a/PyTorch/dev/cv/image_classification/Hourglass_ID1809_for_PyTorch/test/train_performance_16p.sh b/PyTorch/dev/cv/image_classification/Hourglass_ID1809_for_PyTorch/test/train_performance_16p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ccc9ac9928ff5748182b7eb4b5f43f0deae8c542
--- /dev/null
+++ b/PyTorch/dev/cv/image_classification/Hourglass_ID1809_for_PyTorch/test/train_performance_16p.sh
@@ -0,0 +1,225 @@
+#!/bin/bash
+
+#当前路径,不需要修改
+cur_path=`pwd`
+export ASCEND_SLOG_PRINT_TO_STDOUT=0
+#export NPU_CALCULATE_DEVICE=$ASCEND_DEVICE_ID
+#集合通信参数,不需要修改
+
+export RANK_SIZE=8
+export JOB_ID=10087
+RANK_ID_START=0
+
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+conf_path=""
+server_index=""
+fix_node_ip=""
+devicesnum=""
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="Hourglass_ID1809_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=16
+#训练step
+train_steps=10
+#学习率
+learning_rate=1e-3
+
+#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_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#*=}`
+    elif [[ $para == --fix_node_ip* ]];then
+	    fix_node_ip=`echo ${para#*=}`
+	elif [[ $para == --devicesnum* ]];then
+	    devicesnum=`echo ${para#*=}`
+    elif [[ $para == --conf_path* ]];then
+            conf_path=`echo ${para#*=}`
+    elif [[ $para == --server_index* ]];then
+            server_index=`echo ${para#*=}`
+    fi
+done
+
+one_node_ip=`find $conf_path -name "server_*0.info"|awk -F "server_" '{print $2}'|awk -F "_" '{print $1}'`
+linux_num=`find $conf_path -name "server_*.info" |wc -l`
+
+#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/../
+sed -i "s|'batchsize': 16|'batchsize': $batch_size|g" $cur_path/../task/pose.py
+sed -i "s|'learning_rate': 1e-3|'learning_rate': $learning_rate|g" $cur_path/../task/pose.py
+sed -i "s|'epoch_num': 200|'epoch_num': $train_epochs|g" $cur_path/../task/pose.py
+sed -i "s|'train_iters': 1000|'train_iters': $train_steps|g" $cur_path/../task/pose.py
+sed -i "s|annot_dir = 'data/MPII/annot'|annot_dir = '$data_path/data/MPII/annot'|g" $cur_path/../datat/MPII/ref.py
+sed -i "s|img_dir = 'data/MPII/images'|img_dir = '$data_path/data/MPII/images'|g" $cur_path/../datat/MPII/ref.py
+for((RANK_ID=$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
+    
+    #绑核，不需要绑核的模型删除，需要绑核的模型根据实际修改
+    #cpucount=`lscpu | grep "CPU(s):" | head -n 1 | awk '{print $2}'`
+    #cpustep=`expr $cpucount / 8`
+    #echo "taskset c steps:" $cpustep
+    #let a=RANK_ID*$cpustep
+    #let b=RANK_ID+1
+    #let c=b*$cpustep-1
+	
+
+    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改
+done 
+wait
+
+
+export HCCL_IF_IP=$fix_node_ip
+export MASTER_ADDR=$one_node_ip
+export MASTER_PORT=29688
+export HCCL_WHITELIST_DISABLE=1
+device_num=${#devicesnum}
+devices_num=`awk 'BEGIN{printf "%.0f\n",'${device_num}'-1}'`
+
+start_time=$(date +%s)
+NPUS=($(seq 0 $devices_num))
+rank_server=`awk 'BEGIN{printf "%.0f\n",'${device_num}'*'${server_index}'}'`
+export NPU_WORLD_SIZE=`awk 'BEGIN{printf "%.0f\n",'${device_num}'*'${linux_num}'}'`
+
+rank=0
+for i in ${NPUS[@]}
+do
+    mkdir -p  $cur_path/output/${i}/
+    export NPU_CALCULATE_DEVICE=${i}
+    export ASCEND_DEVICE_ID=${i}
+    export RANK=`awk 'BEGIN{printf "%.0f\n",'${rank}'+'${rank_server}'}'`
+    echo run process ${rank}
+
+   nohup  python3 train.py -e test_run_001   --ddp True > $cur_path/output/${i}/train_${i}.log 2>&1 &
+    let rank++
+done
+wait
+
+
+sed -i "s|'batchsize': $batch_size|'batchsize': 16|g" $cur_path/../task/pose.py
+sed -i "s|'learning_rate': $learning_rate|'learning_rate': 1e-3|g" $cur_path/../task/pose.py
+sed -i "s|'epoch_num': $train_epochs|'epoch_num': 200|g" $cur_path/../task/pose.py
+sed -i "s|'train_iters': $train_steps|'train_iters': 1000|g" $cur_path/../task/pose.py
+sed -i "s|annot_dir = '$data_path/data/MPII/annot'|annot_dir = 'data/MPII/annot'|g" $cur_path/../datat/MPII/ref.py
+sed -i "s|img_dir = '$data_path/data/MPII/images'|img_dir = 'data/MPII/images'|g" $cur_path/../datat/MPII/ref.py
+#conda deactivate
+#训练结束时间，不需要修改
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+sed -i "s|\r|\n|g" ${cur_path}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log
+#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
+FPS=`grep "fps:"  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F "fps: " '{print $2}'|awk '{sum+=$1} END {print"",sum/NR}'|sed s/[[:space:]]//g`
+FPS=$(awk 'BEGIN{print '$FPS'*16}') 
+
+#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 eval_accuracy $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|grep -v mlp_log|awk 'END {print $5}'| sed 's/,//g' |cut -c 1-5`
+#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=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据
+#吞吐量，不需要修改
+ActualFPS=${FPS}
+#单迭代训练时长，不需要修改
+TrainingTime=`awk 'BEGIN{printf "%.2f\n",'${BatchSize}'*1000/'${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 "the loss is: " $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk -F "the loss is: " '{print $2}'|sed s/[[:space:]]//g > $cur_path/output/$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
diff --git a/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/main.py b/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/main.py
index 97f372ed27ed4c34e9af747f9db75f9b69669a5a..b919655e4f5cb8feabf2d4276f094d1bd3aadc9c 100644
--- a/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/main.py
+++ b/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/main.py
@@ -37,7 +37,7 @@ import torch.utils.data.distributed
 import torchvision.transforms as transforms
 import torchvision.datasets as datasets
 import torchvision.models as models
-
+print(os.environ)
 
 model_names = sorted(name for name in models.__dict__
                      if name.islower() and not name.startswith("__")
diff --git a/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/test/train_performance_16p.sh b/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/test/train_performance_16p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ad5b68f39561c6472289c352b6a53f2e25415f4d
--- /dev/null
+++ b/PyTorch/dev/cv/image_classification/ResNet152_ID0424_for_PyTorch/test/train_performance_16p.sh
@@ -0,0 +1,213 @@
+#!/bin/bash
+
+#当前路径,不需要修改
+cur_path=`pwd`
+#source env.sh
+#集合通信参数,不需要修改
+
+export RANK_SIZE=8
+export JOB_ID=10087
+RANK_ID_START=0
+
+
+# 数据集路径,保持为空,不需要修改
+data_path=""
+conf_path=""
+server_index=""
+fix_node_ip=""
+devicesnum=""
+
+#设置默认日志级别,不需要修改
+export ASCEND_GLOBAL_LOG_LEVEL=3
+
+#基础参数，需要模型审视修改
+#网络名称，同目录名称
+Network="ResNet152_ID0424_for_PyTorch"
+#训练epoch
+train_epochs=1
+#训练batch_size
+batch_size=4096
+#训练step
+#train_steps=`expr 1281167 / ${batch_size}`
+#学习率
+learning_rate=0.5
+
+#TF2.X独有，需要模型审视修改
+#export NPU_LOOP_SIZE=${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
+
+# 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#*=}`
+    elif [[ $para == --fix_node_ip* ]];then
+	    fix_node_ip=`echo ${para#*=}`
+	elif [[ $para == --devicesnum* ]];then
+	    devicesnum=`echo ${para#*=}`
+    elif [[ $para == --conf_path* ]];then
+        conf_path=`echo ${para#*=}`
+    elif [[ $para == --server_index* ]];then
+        server_index=`echo ${para#*=}`
+    fi
+done
+
+one_node_ip=`find $conf_path -name "server_*0.info"|awk -F "server_" '{print $2}'|awk -F "_" '{print $1}'`
+linux_num=`find $conf_path -name "server_*.info" |wc -l`
+
+#校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be confing"
+    exit 1
+fi
+
+export HCCL_IF_IP=$fix_node_ip
+export MASTER_ADDR=$one_node_ip
+export MASTER_PORT=29688
+export HCCL_WHITELIST_DISABLE=1
+device_num=${#devicesnum}
+devices_num=`awk 'BEGIN{printf "%.0f\n",'${device_num}'-1}'`
+
+NPUS=($(seq 0 $devices_num))
+rank_server=`awk 'BEGIN{printf "%.0f\n",'${device_num}'*'${server_index}'}'`
+export NPU_WORLD_SIZE=`awk 'BEGIN{printf "%.0f\n",'${device_num}'*'${linux_num}'}'`
+
+
+#训练开始时间，不需要修改
+start_time=$(date +%s)
+
+#进入训练脚本目录，需要模型审视修改
+cd $cur_path/..
+rank=0
+for((RANK_ID=$RANK_ID_START;RANK_ID<1;RANK_ID++));
+do
+    #设置环境变量，不需要修改
+    ASCEND_DEVICE_ID=$RANK_ID
+    echo "Device ID: $ASCEND_DEVICE_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
+    # 绑核，不需要的绑核的模型删除，需要的模型审视修改
+    #let a=RANK_ID*12
+    #let b=RANK_ID+1
+    #let c=b*12-1
+    
+    #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 ${cur_path}/../main.py \
+	${data_path} \
+	-a resnet152 \
+    --addr=$one_node_ip \
+    --seed=49 \
+    --workers=$(nproc) \
+    --learning-rate=${learning_rate} \
+    --mom=0.9 \
+    --weight-decay=1.0e-04  \
+    --print-freq=1 \
+    --dist-url='tcp://127.0.0.1:50000' \
+    --multiprocessing-distributed \
+    --world-size=2 \
+    --rank=0 \
+    --device='npu' \
+    --dist-backend='hccl' \
+	--epochs=${train_epochs} \
+    --batch-size=${batch_size} \
+	--amp \
+    --device_list=0,1,2,3,4,5,6,7 \
+    --FusedSGD \
+	--stop-step-num=128 \
+    --loss-scale=1024  > ${cur_path}/output/${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
+fps=`grep -a 'FPS'  $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk -F " " '{print $NF}'|awk 'END {print}'`
+FPS=`awk 'BEGIN{printf "%.2f\n", '$fps'*16}'`
+#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 -a '* Acc@1' $cur_path/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print}'|awk -F "Acc@1" '{print $NF}'|awk -F " " '{print $1}'`
+#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}_${RANK_SIZE}'p'_'perf'
+
+##获取性能数据，不需要修改
+#吞吐量
+ActualFPS=${FPS}
+#单迭代训练时长
+TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`
+
+#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
+grep Epoch: $cur_path/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|grep -v Test|awk -F "Loss" '{print $NF}' | awk -F " " '{print $1}' >> $cur_path/output/$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 "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