!4011 修改office/others的环境变量设置

Merge pull request !4011 from JoeyLin/office-others

official/recommend/naml/README.md

@@ -236,6 +236,8 @@ python export.py --platform [PLATFORM] --checkpoint_path [CHECKPOINT_PATH] --fil
 
 ## [Infer on Ascend310](#contents)
 
+**Before inference, please refer to [Environment Variable Setting Guide](https://gitee.com/mindspore/models/tree/master/utils/ascend310_env_set/README.md) to set environment variables.**
+
 ```shell
 # Ascend310 inference
 bash run_infer_310.sh [NEWS_MODEL] [USER_MODEL] [DEVICE_ID]

official/recommend/naml/scripts/run_infer_310.sh

@@ -45,16 +45,6 @@ echo $news_model
 echo $user_model
 echo $device_id
 
-export ASCEND_HOME=/usr/local/Ascend/
-if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
-    export ASCEND_HOME=/usr/local/Ascend/ascend-toolkit/latest
-else
-    export ASCEND_HOME=/usr/local/Ascend/latest
-fi
-export PATH=$ASCEND_HOME/compiler/ccec_compiler/bin:$PATH
-export LD_LIBRARY_PATH=$ASCEND_HOME/lib64:/usr/local/Ascend/driver/lib64:$LD_LIBRARY_PATH
-export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-
 function preprocess_data()
 {
     if [ -d preprocess_Result ]; then

official/recommend/ncf/README.md

@@ -73,7 +73,7 @@ In both datasets, the timestamp is represented in seconds since midnight Coordin
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorials/experts/en/master/others/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
+The [mixed precision](https://www.mindspore.cn/tutorials/en/master/advanced/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 
 # [Environment Requirements](#contents)
@@ -288,6 +288,8 @@ Parameters for both training and evaluation can be set in config.py.
 
 ## Inference Process
 
+**Before inference, please refer to [Environment Variable Setting Guide](https://gitee.com/mindspore/models/tree/master/utils/ascend310_env_set/README.md) to set environment variables.**
+
 ### [Export MindIR](#contents)
 
 ```shell

official/recommend/ncf/scripts/run_infer_310.sh

@@ -46,16 +46,6 @@ echo "mindir name: "$model
 echo "need preprocess: "$need_preprocess
 echo "device id: "$device_id
 
-export ASCEND_HOME=/usr/local/Ascend/
-if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
-    export ASCEND_HOME=/usr/local/Ascend/ascend-toolkit/latest
-else
-    export ASCEND_HOME=/usr/local/Ascend/latest
-fi
-export PATH=$ASCEND_HOME/compiler/ccec_compiler/bin:$PATH
-export LD_LIBRARY_PATH=$ASCEND_HOME/lib64:/usr/local/Ascend/driver/lib64:$LD_LIBRARY_PATH
-export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-
 function preprocess_data()
 {
     if [ -d preprocess_Result ]; then

official/recommend/wide_and_deep/README.md

@@ -407,6 +407,8 @@ python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_
 
 ## Inference Process
 
+**Before inference, please refer to [Environment Variable Setting Guide](https://gitee.com/mindspore/models/tree/master/utils/ascend310_env_set/README.md) to set environment variables.**
+
 ### [Export MindIR](#contents)
 
 ```python

official/recommend/wide_and_deep/README_CN.md

@@ -411,6 +411,8 @@ python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_
 
 ## 推理过程
 
+**推理前需参照 [环境变量设置指引](https://gitee.com/mindspore/models/tree/master/utils/ascend310_env_set/README_CN.md) 进行环境变量设置。**
+
 ### [导出MindIR](#contents)
 
 ```shell

official/recommend/wide_and_deep/script/run_infer_310.sh

@@ -51,16 +51,6 @@ echo "data type: "$dataset_type
 echo "need preprocess: "$need_preprocess
 echo "device id: "$device_id
 
-export ASCEND_HOME=/usr/local/Ascend/
-if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
-    export ASCEND_HOME=/usr/local/Ascend/ascend-toolkit/latest
-else
-    export ASCEND_HOME=/usr/local/Ascend/latest
-fi
-export PATH=$ASCEND_HOME/compiler/ccec_compiler/bin:$PATH
-export LD_LIBRARY_PATH=$ASCEND_HOME/lib64:/usr/local/Ascend/driver/lib64:$LD_LIBRARY_PATH
-export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-
 function preprocess_data()
 {
     if [ -d preprocess_Result ]; then