v0.5.0 link adaption

configs/det/dbnet/README.md

@@ -288,7 +288,7 @@ msrun --worker_num=2 --local_worker_num=2 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

configs/det/dbnet/README_CN.md

@@ -270,7 +270,7 @@ msrun --worker_num=2 --local_worker_num=2 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=2 --local_worker_num=2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

configs/det/dbnet/README_CN_PP-OCRv3.md

@@ -338,7 +338,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * 单卡训练

configs/det/east/README.md

@@ -136,7 +136,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.
 

configs/det/east/README_CN.md

@@ -132,7 +132,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

configs/det/psenet/README.md

@@ -156,7 +156,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_det`.

configs/det/psenet/README_CN.md

@@ -155,7 +155,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_det`。
 

configs/layout/yolov8/README.md

@@ -91,7 +91,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * Standalone Training

configs/layout/yolov8/README_CN.md

@@ -107,7 +107,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/layout/yolov8/yolov8n.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * 单卡训练

configs/rec/abinet/README.md

@@ -232,7 +232,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/abinet/abinet_resnet45_en.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 The pre-trained model needs to be loaded during ABINet model training, and the weight of the pre-trained model is
 from [abinet_pretrain_en.ckpt](https://download.mindspore.cn/toolkits/mindocr/abinet/abinet_pretrain_en-821ca20b.ckpt). It is needed to add the path of the pretrained weight to the model pretrained in "configs/rec/abinet/abinet_resnet45_en.yaml".

configs/rec/abinet/README_CN.md

@@ -252,7 +252,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/abinet/abinet_resnet45_en.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 ABINet模型训练时需要加载预训练模型，预训练模型的权重来自[abinet_pretrain_en.ckpt](https://download.mindspore.cn/toolkits/mindocr/abinet/abinet_pretrain_en-821ca20b.ckpt)，需要在“configs/rec/abinet/abinet_resnet45_en.yaml”中model的pretrained添加预训练权重的路径。

configs/rec/crnn/README.md

@@ -264,7 +264,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/crnn/crnn_resnet34.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 

configs/rec/crnn/README_CN.md

@@ -266,7 +266,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/crnn/crnn_resnet34.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * 单卡训练

configs/rec/master/README.md

@@ -287,7 +287,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/master/master_resnet31.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 

configs/rec/master/README_CN.md

@@ -290,7 +290,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/master/master_resnet31.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 

configs/rec/rare/README.md

@@ -255,7 +255,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/rare/rare_resnet34.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * Standalone Training

configs/rec/rare/README_CN.md

@@ -256,7 +256,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/rare/rare_resnet34.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * 单卡训练

configs/rec/robustscanner/README.md

@@ -284,7 +284,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/robustscanner/robustscanner_resnet31.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * Standalone Training

configs/rec/robustscanner/README_CN.md

@@ -289,7 +289,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/robustscanner/robustscanner_resnet31.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 * 单卡训练

configs/rec/svtr/README.md

@@ -269,7 +269,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/svtr/svtr_tiny_8p.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 

configs/rec/svtr/README_CN.md

@@ -270,7 +270,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/rec/svtr/svtr_tiny_8p.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 

configs/rec/svtr/README_CN_PP-OCRv3.md

@@ -301,7 +301,7 @@ msrun --worker_num=4 --local_worker_num=4 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/svtr/svtr_ppocrv3_ch.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 

configs/rec/visionlan/README.md

@@ -193,7 +193,7 @@ msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/visionlan/visionlan_resnet45_LF_2.yaml
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/visionlan/visionlan_resnet45_LA.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_visionlan`.
 

configs/rec/visionlan/README_CN.md

@@ -184,7 +184,7 @@ msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/visionlan/visionlan_resnet45_LF_2.yaml
 msrun --bind_core=True --worker_num=4 --local_worker_num=4 python tools/train.py --config configs/rec/visionlan/visionlan_resnet45_LA.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoints、每个阶段的性能和loss曲线）将保存在yaml配置文件中由参数`ckpt_save_dir`解析的目录中。默认目录为`./tmp_visionlan`。
 

configs/table/README.md

@@ -144,7 +144,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # Based on verification,binding cores usually results in performance acceleration.Please configure the parameters and run.
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/table/table_master.yaml
 ```
-**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/master/model_train/parallel/msrun_launcher.html).
+**Note:** For more information about msrun configuration, please refer to [here](https://www.mindspore.cn/docs/en/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 
 The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg `ckpt_save_dir` in yaml config file. The default directory is `./tmp_table`.

configs/table/README_CN.md

@@ -138,7 +138,7 @@ msrun --worker_num=8 --local_worker_num=8 python tools/train.py --config configs
 # 经验证，绑核在大部分情况下有性能加速，请配置参数并运行
 msrun --bind_core=True --worker_num=8 --local_worker_num=8 python tools/train.py --config configs/table/table_master.yaml
 ```
-**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/master/model_train/parallel/msrun_launcher.html).
+**注意:** 有关 msrun 配置的更多信息，请参考[此处](https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/msrun_launcher.html).
 
 训练结果（包括checkpoint、每个epoch的性能和曲线图）将被保存在yaml配置文件的`ckpt_save_dir`参数配置的路径下，默认为`./tmp_table`。