!20243 modify urls related to docs repository for master_mindspore

Merge pull request !20243 from lvmingfu/code_docs_master3

model_zoo/official/cv/FCN8s/README.md

@@ -49,8 +49,8 @@ Dataset used:
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需获取更多信息，请查看如下链接：
-    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # [快速开始](#contents)
 
@@ -434,7 +434,7 @@ python export.py
 
 ### 教程
 
-如果你需要在不同硬件平台（如GPU，Ascend 910 或者 Ascend 310）使用训练好的模型，你可以参考这个 [Link](https://www.mindspore.cn/docs/programming_guide/en/master/multi_platform_inference.html)。以下是一个简单例子的步骤介绍：
+如果你需要在不同硬件平台（如GPU，Ascend 910 或者 Ascend 310）使用训练好的模型，你可以参考这个 [Link](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html)。以下是一个简单例子的步骤介绍：
 
 - Running on Ascend
 

model_zoo/official/cv/cnnctc/README.md

@@ -94,7 +94,7 @@ This takes around 75 minutes.
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/official/cv/cnnctc/README_CN.md

@@ -95,7 +95,7 @@ python src/preprocess_dataset.py
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求
@@ -236,7 +236,7 @@ bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT(o
 
 > 注意:
 
-  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
+  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
 ### 训练结果
 

model_zoo/official/cv/crnn/README.md

@@ -204,7 +204,7 @@ max_text_length": 23,                     # max number of digits in each
 
 ## [Training Process](#contents)
 
-- Set options in `config.py`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `config.py`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 ### [Training](#contents)
 

model_zoo/official/cv/crnn_seq2seq_ocr/README.md

@@ -174,7 +174,7 @@ Parameters for both training and evaluation can be set in config.py.
 
 ## [Training Process](#contents)
 
-- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 ### [Training](#contents)
 

model_zoo/official/cv/cspdarknet53/README.md

@@ -49,7 +49,7 @@ Dataset used can refer to paper.
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 
@@ -206,7 +206,7 @@ bash run_distribute_train.sh [RANK_TABLE_FILE] [DATA_DIR] (option)[PATH_CHECKPOI
 bash run_standalone_train.sh [DEVICE_ID] [DATA_DIR] (option)[PATH_CHECKPOINT]
 ```
 
-> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 

model_zoo/official/cv/ctpn/README.md

@@ -195,7 +195,7 @@ imagenet_cfg = edict({
 
 Then you can train it with ImageNet2012.
 > Notes:
-> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 >

model_zoo/official/cv/deeplabv3/README.md

@@ -68,7 +68,7 @@ You can also generate the list file automatically by run script: `python get_dat
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, 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/docs/programming_guide/en/master/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, 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)
@@ -78,8 +78,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
 - Install python packages in requirements.txt
 - Generate config json file for 8pcs training
 

model_zoo/official/cv/deeplabv3/README_CN.md

@@ -93,7 +93,7 @@ python convert_resnet101.py
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求

model_zoo/official/cv/deeptext/README.md

@@ -116,7 +116,7 @@ sh run_eval_ascend.sh [IMGS_PATH] [ANNOS_PATH] [CHECKPOINT_PATH] [COCO_TEXT_PARS
 ```
 
 > Notes:
-> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 >

model_zoo/official/cv/densenet/README.md

@@ -75,7 +75,7 @@ The default configuration of the Dataset are as follows:
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 

model_zoo/official/cv/densenet/README_CN.md

@@ -80,7 +80,7 @@ DenseNet-100使用的数据集： Cifar-10
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求

model_zoo/official/cv/dpn/README.md

@@ -67,7 +67,7 @@ All the models in this repository are trained and validated on ImageNet-1K. The
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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’.
+The [mixed precision](https://www.mindspore.cn/docs/programming_guide/en/master/enable_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)
 

model_zoo/official/cv/googlenet/README.md

@@ -66,7 +66,7 @@ Dataset used can refer to paper.
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/official/cv/googlenet/README_CN.md

@@ -20,7 +20,7 @@
         - [分布式训练](#分布式训练)
     - [评估过程](#评估过程)
         - [评估](#评估)
-     - [导出过程](#导出过程)
+    - [导出过程](#导出过程)
         - [导出](#导出)
     - [推理过程](#推理过程)
         - [推理](#推理)
@@ -73,7 +73,7 @@ GoogleNet由多个inception模块串联起来，可以更加深入。  降维的
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求
@@ -84,7 +84,7 @@ GoogleNet由多个inception模块串联起来，可以更加深入。  降维的
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # 快速入门
 
@@ -549,7 +549,7 @@ python export.py --config_path [CONFIG_PATH]
 
 ### 推理
 
-如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/programming_guide/en/master/multi_platform_inference.html)。下面是操作步骤示例：
+如果您需要使用此训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考此[链接](https://www.mindspore.cn/docs/programming_gui/zh-CN/master/multi_platform_inference.html)。下面是操作步骤示例：
 
 - Ascend处理器环境运行
 

model_zoo/official/cv/inceptionv3/README.md

@@ -52,7 +52,7 @@ Dataset used: [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html)
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 
@@ -275,7 +275,7 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
 sh scripts/run_standalone_train_cpu.sh DATA_PATH
 ```
 
-> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV3, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 

model_zoo/official/cv/inceptionv3/README_CN.md

@@ -63,7 +63,7 @@ InceptionV3的总体网络架构如下：
 
 ## 混合精度（Ascend）
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
@@ -281,7 +281,7 @@ train.py和config.py中主要参数如下：
     sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
     ```
 
-> 注：RANK_TABLE_FILE可参考[链接](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/distributed_training_ascend.html)。device_ip可以通过[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)获取
+> 注：RANK_TABLE_FILE可参考[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html)。device_ip可以通过[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)获取
 > 这是关于device_num和处理器总数的处理器核绑定操作。如不需要，请删除scripts/run_distribute_train.sh中的taskset操作。
 
 ### 启动

model_zoo/official/cv/inceptionv4/README.md

@@ -44,7 +44,7 @@ Dataset used can refer to paper.
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 
@@ -253,7 +253,7 @@ sh scripts/run_standalone_train_ascend.sh DEVICE_ID DATA_DIR
 ```
 
 > Notes:
-> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 > This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 

model_zoo/official/cv/lenet_quant/Readme_CN.md

@@ -63,10 +63,10 @@ LeNet非常简单，包含5层，由2个卷积层和3个全连接层组成。
 - 硬件：Ascend
     - 使用Ascend搭建硬件环境
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
-    - [MindSpore教程](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 ## 快速入门
 

model_zoo/official/cv/maskrcnn/README.md

@@ -522,7 +522,7 @@ Usage: bash run_standalone_train.sh [PRETRAINED_MODEL]
 
 ## [Training Process](#contents)
 
-- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 ### [Training](#content)
 

model_zoo/official/cv/maskrcnn/README_CN.md

@@ -517,7 +517,7 @@ bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
 
 ## 训练过程
 
-- 在`config.py`中设置配置项，包括loss_scale、学习率和网络超参。单击[此处](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)获取更多数据集相关信息.
+- 在`config.py`中设置配置项，包括loss_scale、学习率和网络超参。单击[此处](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dataset_sample.html)获取更多数据集相关信息.
 
 ### 训练
 

model_zoo/official/cv/maskrcnn_mobilenetv1/README.md

@@ -474,7 +474,7 @@ Usage: sh run_standalone_train.sh [PRETRAINED_MODEL]
 
 ## [Training Process](#contents)
 
-- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 ### [Training](#content)
 

model_zoo/official/cv/mobilenetv1/README.md

@@ -58,7 +58,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 ### Mixed Precision(Ascend)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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

model_zoo/official/cv/mobilenetv2/README.md

@@ -48,7 +48,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/official/cv/mobilenetv2/README_CN.md

@@ -54,7 +54,7 @@ MobileNetV2总体网络架构如下：
 
 ## 混合精度（Ascend）
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求

model_zoo/official/cv/mobilenetv2_quant/README_CN.md

@@ -59,7 +59,7 @@ MobileNetV2总体网络架构如下：
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)
 的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 

model_zoo/official/cv/mobilenetv2_quant/Readme.md

@@ -46,7 +46,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 
 ## [Learned Step Size Quantization](#contents)

model_zoo/official/cv/nasnet/README_CN.md

@@ -49,8 +49,8 @@ NASNet总体网络架构如下：
     - [MindSpore](https://www.mindspore.cn/install)
 
 - 如需查看详情，请参见如下资源：
-    - [MindSpore教程](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # 脚本说明
 

model_zoo/official/cv/openpose/README.md

@@ -69,7 +69,7 @@ In the currently provided training script, the coco2017 data set is used as an e
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/official/cv/resnet/README.md

@@ -80,7 +80,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, 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/docs/programming_guide/en/master/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, 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)
@@ -373,7 +373,7 @@ bash run_eval_gpu_resnet_benchmark.sh [DATASET_PATH] [CKPT_PATH] [BATCH_SIZE](op
 
 For distributed training, a hostfile configuration needs to be created in advance.
 
-Please follow the instructions in the link [GPU-Multi-Host](https://www.mindspore.cn/tutorial/training/zh-CN/r1.0/advanced_use/distributed_training_gpu.html).
+Please follow the instructions in the link [GPU-Multi-Host](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_gpu.html).
 
 #### Running parameter server mode training
 

model_zoo/official/cv/resnet/README_CN.md

@@ -83,7 +83,7 @@ ResNet的总体网络架构如下：
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求

model_zoo/official/cv/resnet50_quant/README.md

@@ -53,7 +53,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/official/cv/resnet50_quant/README_CN.md

@@ -58,7 +58,7 @@ ResNet-50总体网络架构如下：
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求
@@ -71,9 +71,9 @@ ResNet-50总体网络架构如下：
 
 - 如需查看详情，请参见如下资源：
 
-  - [MindSpore教程](https://www.mindspore.cn/tutorials/en/master/index.html)
+  - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
 
-  - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+  - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 ## 脚本说明
 

model_zoo/official/cv/resnext/README.md

@@ -47,7 +47,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 

model_zoo/official/cv/resnext/README_CN.md

@@ -54,7 +54,7 @@ ResNeXt整体网络架构如下：
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 

model_zoo/official/cv/retinanet/README_CN.md

@@ -67,10 +67,10 @@ MSCOCO2017
 - 硬件（Ascend）
     - 使用Ascend处理器准备硬件环境。
 - 架构
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 想要获取更多信息，请检查以下资源：
-    - [MindSpore 教程](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore 教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 ## [脚本说明](#content)
 
@@ -190,7 +190,7 @@ sh scripts/run_single_train.sh DEVICE_ID MINDRECORD_DIR PRE_TRAINED(optional) PR
 
 > 注意:
 
-  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
+  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
 #### 运行
 

model_zoo/official/cv/simple_pose/README.md

@@ -57,7 +57,7 @@ Dataset used: COCO2017
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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’.
+The [mixed precision](https://www.mindspore.cn/docs/programming_guide/en/master/enable_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)
 

model_zoo/official/cv/squeezenet/README.md

@@ -62,7 +62,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/official/cv/ssd/README.md

@@ -302,7 +302,7 @@ Then you can run everything just like on ascend.
 
 ### [Training Process](#contents)
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/programming_guide/en/master/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 #### Training on Ascend
 

model_zoo/official/cv/ssd/README_CN.md

@@ -246,7 +246,7 @@ sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]
 
 ## 训练过程
 
-运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。**
+运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/convert_dataset.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。**
 
 ### Ascend上训练
 

model_zoo/official/cv/tinydarknet/README_CN.md

@@ -64,17 +64,17 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的
 
 <!-- 不同的机器有同一个模型的多个副本，每个机器分配到不同的数据，然后将所有机器的计算结果按照某种方式合并 -->
 
-<!-- 在深度学习中，当数据集和参数量的规模越来越大，训练所需的时间和硬件资源会随之增加，最后会变成制约训练的瓶颈。[分布式并行训练](<https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/distributed_training_tutorials.html>)，可以降低对内存、计算性能等硬件的需求，是进行训练的重要优化手段。本模型使用了mindspore提供的自动并行模式AUTO_PARALLEL：该方法是融合了数据并行、模型并行及混合并行的1种分布式并行模式，可以自动建立代价模型，找到训练时间较短的并行策略，为用户选择1种并行模式。 -->
+<!-- 在深度学习中，当数据集和参数量的规模越来越大，训练所需的时间和硬件资源会随之增加，最后会变成制约训练的瓶颈。[分布式并行训练](<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training.html>)，可以降低对内存、计算性能等硬件的需求，是进行训练的重要优化手段。本模型使用了mindspore提供的自动并行模式AUTO_PARALLEL：该方法是融合了数据并行、模型并行及混合并行的1种分布式并行模式，可以自动建立代价模型，找到训练时间较短的并行策略，为用户选择1种并行模式。 -->
 
 # [环境要求](#目录)
 
 - 硬件（Ascend/CPU）
     - 请准备具有Ascend/CPU处理器的硬件环境.
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 更多的信息请访问以下链接：
-    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # [快速入门](#目录)
 

model_zoo/official/cv/unet3d/README.md

@@ -281,7 +281,7 @@ After training, you'll get some checkpoint files under the `train_parallel_fp[32
 #### Distributed training on Ascend
 
 > Notes:
-> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
+> RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html) , and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). For large models like InceptionV4, it's better to export an external environment variable `export HCCL_CONNECT_TIMEOUT=600` to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.
 >
 
 ```shell

model_zoo/official/cv/vgg16/README.md

@@ -84,7 +84,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 
 ### Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 
@@ -445,7 +445,7 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579
 ...
 ```
 
-> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_tutorials.html).
+> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training.html).
 > **Attention** This will bind the processor cores according to the `device_num` and total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations about `taskset` in `scripts/run_distribute_train.sh`
 
 ##### Run vgg16 on GPU

model_zoo/official/cv/vgg16/README_CN.md

@@ -87,7 +87,7 @@ VGG 16网络主要由几个基本模块（包括卷积层和池化层）和三
 
 ### 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
@@ -449,7 +449,7 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579
 ...
 ```
 
-> 关于rank_table.json，可以参考[分布式并行训练](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/distributed_training_tutorials.html)。
+> 关于rank_table.json，可以参考[分布式并行训练](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training.html)。
 > **注意** 将根据`device_num`和处理器总数绑定处理器核。如果您不希望预训练中绑定处理器内核，请在`scripts/run_distribute_train.sh`脚本中移除`taskset`相关操作。
 
 ##### GPU处理器环境运行VGG16

model_zoo/official/cv/warpctc/README.md

@@ -244,7 +244,7 @@ save_checkpoint_path: "./checkpoint"        # path to save checkpoint
 
 ### [Training Process](#contents)
 
-- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `default_config.yaml`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 #### [Training](#contents)
 

model_zoo/official/cv/warpctc/README_CN.md

@@ -248,7 +248,7 @@ save_checkpoint_path: "./checkpoints"       # 检查点保存路径，相对于t
 
 ## 训练过程
 
-- 在`default_config.yaml`中设置选项，包括学习率和网络超参数。单击[MindSpore加载数据集教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)，了解更多信息。
+- 在`default_config.yaml`中设置选项，包括学习率和网络超参数。单击[MindSpore加载数据集教程](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dataset_sample.html)，了解更多信息。
 
 ### 训练
 

model_zoo/official/cv/xception/README.md

@@ -54,7 +54,7 @@ Dataset used can refer to paper.
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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’.
 
@@ -193,7 +193,7 @@ sh run_eval_gpu.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH
 sh run_infer_310.sh MINDIR_PATH DATA_PATH LABEL_FILE DEVICE_ID
 ```
 
-> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
+> Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
 ### Launch
 

model_zoo/official/cv/yolov3_resnet18/README.md

@@ -263,7 +263,7 @@ After installing MindSpore via the official website, you can start training and
 
 ### Training on Ascend
 
-To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.**
+To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/docs/programming_guide/en/master/convert_dataset.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.**
 
 - Stand alone mode
 
@@ -304,7 +304,7 @@ Note the results is two-classification(person and face) used our own annotations
 
 ### Evaluation on Ascend
 
-To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/training/en/master/use/save_model.html) file.
+To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/docs/programming_guide/en/master/save_model.html) file.
 
   ```bash
   sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt

model_zoo/official/cv/yolov3_resnet18/README_CN.md

@@ -266,7 +266,7 @@ YOLOv3整体网络架构如下：
 
 ### Ascend上训练
 
-训练模型运行`train.py`，使用数据集`image_dir`、`anno_path`和`mindrecord_dir`。如果`mindrecord_dir`为空，则通过`image_dir`和`anno_path`（图像绝对路径由`image_dir`和`anno_path`中的相对路径连接）生成[MindRecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html)文件。**注意，如果`mindrecord_dir`不为空，将使用`mindrecord_dir`而不是`image_dir`和`anno_path`。**
+训练模型运行`train.py`，使用数据集`image_dir`、`anno_path`和`mindrecord_dir`。如果`mindrecord_dir`为空，则通过`image_dir`和`anno_path`（图像绝对路径由`image_dir`和`anno_path`中的相对路径连接）生成[MindRecord](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/convert_dataset.html)文件。**注意，如果`mindrecord_dir`不为空，将使用`mindrecord_dir`而不是`image_dir`和`anno_path`。**
 
 - 单机模式
 
@@ -307,7 +307,7 @@ YOLOv3整体网络架构如下：
 
 ### Ascend评估
 
-运行`eval.py`，数据集为`image_dir`、`anno_path`(评估TXT)、`mindrecord_dir`和`ckpt_path`。`ckpt_path`是[检查点](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/save_model.html)文件的路径。
+运行`eval.py`，数据集为`image_dir`、`anno_path`(评估TXT)、`mindrecord_dir`和`ckpt_path`。`ckpt_path`是[检查点](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/save_model.html)文件的路径。
 
   ```shell script
   sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt

model_zoo/official/nlp/bert/README.md

@@ -193,7 +193,7 @@ For distributed training among multiple machines, training command should be exe
 Please follow the instructions in the link below to create an hccl.json file in need:
 [https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
-For dataset, if you want to set the format and parameters, a schema configuration file with JSON format needs to be created, please refer to [tfrecord](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dataset_loading.html#tfrecord) format.
+For dataset, if you want to set the format and parameters, a schema configuration file with JSON format needs to be created, please refer to [tfrecord](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_loading.html#tfrecord) format.
 
 ```text
 For pretraining, schema file contains ["input_ids", "input_mask", "segment_ids", "next_sentence_labels", "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"].

model_zoo/official/nlp/cpm/README.md

@@ -52,8 +52,8 @@ CPM is implemented by GPT, which includes multi-layer decoder module.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below:
-    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
 
 # Quick Start
 
@@ -309,7 +309,7 @@ After processing, the mindrecord file of training and reasoning is generated in
 
 ### Finetune Training Process
 
-- Set options in `src/config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `src/config.py`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 - Run `run_distribute_train_ascend_single_machine.sh` for distributed and single machine training of CPM model.
 

model_zoo/official/nlp/cpm/README_CN.md

@@ -309,7 +309,7 @@ Parameters for dataset and network (Training/Evaluation):
 
 ### Finetune训练过程
 
-- 在`src/config.py`中设置，包括模型并行、batchsize、学习率和网络超参数。点击[这里](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)查看更多数据集信息。
+- 在`src/config.py`中设置，包括模型并行、batchsize、学习率和网络超参数。点击[这里](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dataset_sample.html)查看更多数据集信息。
 
 - 运行`run_distribute_train_ascend_single_machine.sh`，进行CPM模型的单机8卡分布式训练。
 

model_zoo/official/nlp/emotect/README_CN.md

@@ -56,10 +56,10 @@ label   text_a
 - 硬件（Ascend/GPU）
     - 使用Ascend或GPU处理器来搭建硬件环境。
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # 快速入门
 

model_zoo/official/nlp/pangu_alpha/README.md

@@ -40,7 +40,7 @@ with our parallel setting. We summarized the training tricks as followings:
 2. Pipeline Model Parallelism
 3. Optimizer Model Parallelism
 
-The above features can be found [here](https://www.mindspore.cn/doc/programming_guide/en/r1.2/auto_parallel.html).
+The above features can be found [here](https://www.mindspore.cn/docs/programming_guide/en/master/auto_parallel.html).
 More amazing features are still under developing.
 
 The technical report and checkpoint file can be found [here](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-AIpha).
@@ -143,7 +143,7 @@ bash scripts/run_distribute_training.sh DATASET RANK_TABLE RANK_SIZE TYPE MODE
 The above command involves some `args` described below:
 
 - DATASET: The path to the mindrecord files's parent directory . For example: `/home/work/mindrecord/`.
-- RANK_TABLE: The details of the rank table can be found [here](https://www.mindspore.cn/tutorial/training/zh-CN/r1.2/advanced_use/distributed_training_ascend.html). It's a json file describes the `device id`, `service ip` and `rank`.
+- RANK_TABLE: The details of the rank table can be found [here](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_ascend.html). It's a json file describes the `device id`, `service ip` and `rank`.
 - RANK_SIZE: The device number. This can be your total device numbers. For example, 8, 16, 32 ...
 - TYPE: The param init type. The parameters will be initialized with float32. Or you can replace it with `fp16`. This will save a little memory used on the device.
 - MODE: The configure mode. This mode will set the `hidden size` and `layers` to make the parameter number near 2.6 billions. The other mode can be `13B` (`hidden size` 5120 and `layers` 40, which needs at least 16 cards to train.) and `200B`.
@@ -169,7 +169,7 @@ bash scripts/run_distributed_train_gpu.sh RANK_SIZE HOSTFILE DATASET MOD
 ```
 
 - RANK_SIZE: The device number. This can be your total device numbers. For example, 8, 16, 32 ...
-- HOSTFILE:  It's a text file describes the host ip and its devices. Please see our [tutorial](https://www.mindspore.cn/tutorial/training/zh-CN/r1.2/advanced_use/distributed_training_gpu.html) or [OpenMPI](https://www.open-mpi.org/) for more details.
+- HOSTFILE:  It's a text file describes the host ip and its devices. Please see our [tutorial](https://www.mindspore.cn/docs/programming_guide/en/master/distributed_training_gpu.html) or [OpenMPI](https://www.open-mpi.org/) for more details.
 - DATASET: The path to the mindrecord files's parent directory . For example: `/home/work/mindrecord/`.
 - MODE: Can be `2.6B`, `13B` and `200B`.
 

model_zoo/official/nlp/transformer/README.md

@@ -309,7 +309,7 @@ Parameters for learning rate:
 
 ## [Training Process](#contents)
 
-- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `default_config.yaml`, including loss_scale, learning rate and network hyperparameters. Click [here](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 - Run `run_standalone_train.sh` for non-distributed training of Transformer model.
 

model_zoo/official/nlp/transformer/README_CN.md

@@ -316,7 +316,7 @@ Parameters for learning rate:
 
 ### 训练过程
 
-- 在`default_config.yaml`中设置选项，包括loss_scale、学习率和网络超参数。点击[这里](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html)查看更多数据集信息。
+- 在`default_config.yaml`中设置选项，包括loss_scale、学习率和网络超参数。点击[这里](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dataset_sample.html)查看更多数据集信息。
 
 - 运行`run_standalone_train.sh`，进行Transformer模型的非分布式训练。
 

model_zoo/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/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)
@@ -310,7 +310,7 @@ Inference result is saved in current path, you can find result like this in acc.
 
 If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/docs/programming_guide/en/master/multi_platform_inference.html). Following the steps below, this is a simple example:
 
-<https://www.mindspore.cn/tutorial/inference/en/master/multi_platform_inference.html>
+<https://www.mindspore.cn/docs/programming_guide/en/master/multi_platform_inference.html>
 
   ```python
   # Load unseen dataset for inference

model_zoo/research/audio/fcn-4/README.md

@@ -36,7 +36,7 @@ FCN-4 is a convolutional neural network architecture, its name FCN-4 comes from
 
 ### Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/research/cv/AttGAN/README_CN.md

@@ -53,10 +53,10 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
 - 硬件（Ascend）
     - 使用Ascend来搭建硬件环境。
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # 快速入门
 

model_zoo/research/cv/GENet_Res50/README_CN.md

@@ -60,7 +60,7 @@ Imagenet 2017和Imagenet 2012 数据集一致
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求
@@ -73,9 +73,9 @@ Imagenet 2017和Imagenet 2012 数据集一致
 
 - 如需查看详情，请参见如下资源：
 
-  - [MindSpore教程](https://www.mindspore.cn/tutorials/en/master/index.html)
+  - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
 
-  - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+  - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 ## 脚本说明
 

model_zoo/research/cv/ManiDP/Readme.md

@@ -37,7 +37,7 @@ Dataset used: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html)
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)
@@ -47,7 +47,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-    - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
+    - [MindSpore tutorials](https://www.mindspore.cn/tutorial/en/master/index.html)
     - [MindSpore API](https://www.mindspore.cn/docs/api/en/master/index.html)
 
 # [Script description](#contents)

model_zoo/research/cv/SE-Net/README.md

@@ -53,7 +53,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, 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/docs/programming_guide/en/master/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, 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)

model_zoo/research/cv/arcface/README.md

@@ -213,7 +213,7 @@ epoch time: 1104929.793 ms, per step time: 97.162 ms
 
 ### 推理
 
-如果您需要使用已训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考[此处](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.2/index.html)。
+如果您需要使用已训练模型在GPU、Ascend 910、Ascend 310等多个硬件平台上进行推理，可参考[此处](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html)。
 
 ### 迁移学习
 

model_zoo/research/cv/deeplabv3plus/README_CN.md

@@ -79,7 +79,7 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求

model_zoo/research/cv/glore_res200/README_CN.md

@@ -65,7 +65,7 @@
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_gui/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求

model_zoo/research/cv/glore_res50/README.md

@@ -61,14 +61,14 @@ glore_res的总体网络架构如下：
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求
 
 - 硬件(Ascend)
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)

model_zoo/research/cv/hardnet/README_CN.md

@@ -60,7 +60,7 @@ HarDNet指的是Harmonic DenseNet: A low memory traffic network，其突出的
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # 环境要求
@@ -68,10 +68,10 @@ HarDNet指的是Harmonic DenseNet: A low memory traffic network，其突出的
 - 硬件（Ascend/GPU）
     - 使用Ascend或GPU处理器来搭建硬件环境。
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # 快速入门
 
@@ -419,7 +419,7 @@ bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
 
 ### 推理
 
-如果您需要使用此训练模型在Ascend 910上进行推理，可参考此[链接](https://www.mindspore.cn/docs/programming_guide/en/master/multi_platform_inference.html)。下面是操作步骤示例：
+如果您需要使用此训练模型在Ascend 910上进行推理，可参考此[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html)。下面是操作步骤示例：
 
 - Ascend处理器环境运行
 
@@ -456,7 +456,7 @@ bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
   print("==============Acc: {} ==============".format(acc))
   ```
 
-如果您需要使用此训练模型在GPU上进行推理，可参考此[链接](https://www.mindspore.cn/docs/programming_guide/en/master/multi_platform_inference.html)。下面是操作步骤示例：
+如果您需要使用此训练模型在GPU上进行推理，可参考此[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html)。下面是操作步骤示例：
 
 - GPU处理器环境运行
 

model_zoo/research/cv/ntsnet/README.md

@@ -126,7 +126,7 @@ Usage: bash run_standalone_train.sh [DATA_URL] [TRAIN_URL]
 
 ## [Training Process](#contents)
 
-- Set options in `config.py`, including learning rate, output filename and network hyperparameters. Click [here](https://www.mindspore.cn/tutorial/training/zh-CN/master/use/data_preparation.html) for more information about dataset.
+- Set options in `config.py`, including learning rate, output filename and network hyperparameters. Click [here](https://www.mindspore.cn/docs/programming_guide/en/master/dataset_sample.html) for more information about dataset.
 
 ### [Training](#content)
 

model_zoo/research/cv/renas/Readme.md

@@ -36,7 +36,7 @@ An effective and efficient architecture performance evaluation scheme is essenti
 
 ## [Mixed Precision(Ascend)](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)
@@ -46,7 +46,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-    - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
+    - [MindSpore tutorials](https://www.mindspore.cn/tutorial/en/master/index.html)
     - [MindSpore API](https://www.mindspore.cn/docs/api/en/master/index.html)
 
 # [Script description](#contents)

model_zoo/research/cv/retinanet_resnet101/README_CN.md

@@ -60,10 +60,10 @@ MSCOCO2017
 - 硬件（Ascend）
     - 使用Ascend处理器准备硬件环境。
 - 架构
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 想要获取更多信息，请检查以下资源：
-    - [MindSpore 教程](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore 教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 ## [脚本说明](#content)
 
@@ -178,7 +178,7 @@ sh run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED(optional)
 
 > 注意:
 
-  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
+  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
 #### 运行
 

model_zoo/research/cv/retinanet_resnet152/README_CN.md

@@ -60,10 +60,10 @@ MSCOCO2017
 - 硬件（Ascend）
     - 使用Ascend处理器准备硬件环境。
 - 架构
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 想要获取更多信息，请检查以下资源：
-    - [MindSpore 教程](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore 教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 ## [脚本说明](#content)
 
@@ -178,7 +178,7 @@ sh run_distribute_train.sh DEVICE_ID EPOCH_SIZE LR DATASET PRE_TRAINED(optional)
 
 > 注意:
 
-  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
+  RANK_TABLE_FILE相关参考资料见[链接](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html), 获取device_ip方法详见[链接](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
 #### 运行
 

model_zoo/research/cv/squeezenet/README.md

@@ -57,7 +57,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/research/cv/ssd_ghostnet/README.md

@@ -200,7 +200,7 @@ If you want to run in modelarts, please check the official documentation of [mod
 
 ### Training on Ascend
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/programming_guide/en/master/convert_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 - Distribute mode
 

model_zoo/research/cv/ssd_mobilenetV2/README.md

@@ -186,7 +186,7 @@ sh scripts/run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 
 ### [Training Process](#contents)
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/programming_guide/en/master/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 #### Training on Ascend
 

model_zoo/research/cv/ssd_mobilenetV2_FPNlite/README.md

@@ -190,7 +190,7 @@ sh scripts/run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 
 ### [Training Process](#contents)
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/programming_guide/en/master/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 #### Training on Ascend
 

model_zoo/research/cv/ssd_resnet50/README.md

@@ -204,7 +204,7 @@ Then you can run everything just like on ascend.
 
 ### [Training Process](#contents)
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/docs/programming_guide/en/master/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 #### Training on Ascend
 

model_zoo/research/cv/ssd_resnet50/README_CN.md

@@ -163,7 +163,7 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 
 ## 训练过程
 
-运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。**
+运行`train.py`训练模型。如果`mindrecord_dir`为空，则会通过`coco_root`（coco数据集）或`image_dir`和`anno_path`（自己的数据集）生成[MindRecord](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/convert_dataset.html)文件。**注意，如果mindrecord_dir不为空，将使用mindrecord_dir代替原始图像。**
 
 ### Ascend上训练
 

model_zoo/research/cv/vgg19/README_CN.md

@@ -68,7 +68,7 @@ VGG 19网络主要由几个基本模块（包括卷积层和池化层）和三
 
 ## 混合精度
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 

model_zoo/research/cv/wgan/README_CN.md

@@ -55,10 +55,10 @@ WGAN网络包含两部分，生成器网络和判别器网络。判别器网络
 - 硬件（Ascend）
     - 使用Ascend来搭建硬件环境。
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # 快速入门
 

model_zoo/research/hpc/pinns/README.md

@@ -72,7 +72,7 @@ Dataset used：[cylinder nektar wake](https://github.com/maziarraissi/PINNs/tree
 
 ## [Mixed Precision](#Contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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/docs/programming_guide/en/master/enable_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)

model_zoo/research/hpc/pinns/README_CN.md

@@ -70,7 +70,7 @@ Navier-Stokes方程是流体力学中描述粘性牛顿流体的方程。针对N
 
 ## [混合精度](#目录)
 
-采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
+采用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
 以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
 
 # [环境要求](#目录)
@@ -78,10 +78,10 @@ Navier-Stokes方程是流体力学中描述粘性牛顿流体的方程。针对N
 - 硬件（GPU）
     - 使用GPU处理器来搭建硬件环境。
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install/en)
+    - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
 
 # [快速入门](#目录)
 

model_zoo/research/hpc/sponge/README.md

@@ -26,7 +26,7 @@ There are three inputs in the example, the property file `NVT_290_10ns.in`, the
 
 The topology file and coordinates file can be generated by `tleap` in `AmberTools` ([link](<http://ambermd.org/GetAmber.php>)). For more details, please refer to:
 
-- [SPONGE Tutorial](https://gitee.com/mindspore/docs/blob/master/tutorials/training/source_zh_cn/advanced_use/hpc_sponge.md)
+- [SPONGE Tutorial](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/programming_guide/source_en/hpc_sponge.md)
 
 ## Environment Requirements