fix links in doc (#759)

* fix doc * fix link * fix doc * fix link * fix doc * fix mkdoc * remove acl * remove acl in code * remove acl in code * fix mkdoc * remove gpu description * fix link * revert example change * fix
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diff --git a/README.md b/README.md
@@ -295,7 +295,7 @@ You can do MindSpore Lite inference in MindOCR using **MindOCR models** or **Thi
 
 For the detailed performance of the trained models, please refer to [https://github.com/mindspore-lab/mindocr/blob/main/configs](./configs).
 
-For details of MindSpore Lite and ACL inference models support, please refer to [MindOCR Models Support List](docs/en/inference/mindocr_models_list.md) and [Third-party Models Support List](docs/en/inference/thirdparty_models_list.md) (PaddleOCR etc.).
+For details of MindSpore Lite inference models support, please refer to [MindOCR Models Support List](docs/en/inference/mindocr_models_list.md) and [Third-party Models Support List](docs/en/inference/thirdparty_models_list.md) (PaddleOCR etc.).
 
 ## Dataset List
 

diff --git a/configs/cls/mobilenetv3/README.md b/configs/cls/mobilenetv3/README.md
@@ -132,7 +132,7 @@ python tools/train.py -c configs/cls/mobilenetv3/cls_mv3.yaml
 Please set `distribute` in yaml config file to be `True`.
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 4 python tools/train.py -c configs/cls/mobilenetv3/cls_mv3.yaml
 ```
 

diff --git a/configs/cls/mobilenetv3/README_CN.md b/configs/cls/mobilenetv3/README_CN.md
@@ -134,7 +134,7 @@ python tools/train.py -c configs/cls/mobilenetv3/cls_mv3.yaml
 请确保yaml文件中的`distribute`参数为`True`。
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 4 python tools/train.py -c configs/cls/mobilenetv3/cls_mv3.yaml
 yaml
 ```

diff --git a/configs/det/dbnet/README.md b/configs/det/dbnet/README.md
@@ -397,7 +397,7 @@ python tools/train.py -c=configs/det/dbnet/db_r50_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
 
@@ -418,7 +418,7 @@ Please refer to the tutorial [MindOCR Inference](../../../docs/en/inference/infe
 
 - Model Export
 
-Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../README.md) tutorial and use the following command to export the trained ckpt model to  MindIR file:
+Please [download](#3-results) the exported MindIR file first, or refer to the [Model Export](../../../docs/en/inference/convert_tutorial.md#1-model-export) tutorial and use the following command to export the trained ckpt model to  MindIR file:
 
 ```shell
 python tools/export.py --model_name_or_config dbnet_resnet50 --data_shape 736 1280 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -430,11 +430,11 @@ The `data_shape` is the model input shape of height and width for MindIR file. T
 
 - Environment Installation
 
-Please refer to [Environment Installation](../../../docs/en/inference/environment.md#2-mindspore-lite-inference) tutorial to configure the MindSpore Lite inference environment.
+Please refer to [Environment Installation](../../../docs/en/inference/environment.md) tutorial to configure the MindSpore Lite inference environment.
 
 - Model Conversion
 
-Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#1-mindocr-models),
+Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#2-mindspore-lite-mindir-convert),
 and use the `converter_lite` tool for offline conversion of the MindIR file.
 
 - Inference

diff --git a/configs/det/dbnet/README_CN.md b/configs/det/dbnet/README_CN.md
@@ -375,7 +375,7 @@ python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 2 python tools/train.py --config configs/det/dbnet/db_r50_icdar15.yaml
 ```
 
@@ -391,11 +391,11 @@ python tools/eval.py --config configs/det/dbnet/db_r50_icdar15.yaml
 
 ## 5. MindSpore Lite 推理
 
-请参考[MindOCR 推理](../../../docs/cn/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
+请参考[MindOCR 推理](../../../docs/zh/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
 
 - 模型导出
 
-请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../README.md)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
+请先[下载](#3-实验结果)已导出的MindIR文件，或者参考[模型导出](../../../docs/zh/inference/convert_tutorial.md#1-模型导出)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
 
 ```shell
 python tools/export.py --model_name_or_config dbnet_resnet50 --data_shape 736 1280 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -407,11 +407,11 @@ python tools/export.py --model_name_or_config configs/det/dbnet/db_r50_icdar15.y
 
 - 环境搭建
 
-请参考[环境安装](../../../docs/cn/inference/environment.md#2-mindspore-lite推理)教程，配置MindSpore Lite推理运行环境。
+请参考[环境安装](../../../docs/zh/inference/environment.md)教程，配置MindSpore Lite推理运行环境。
 
 - 模型转换
 
-请参考[模型转换](../../../docs/cn/inference/convert_tutorial.md#1-mindocr模型)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
+请参考[模型转换](../../../docs/zh/inference/convert_tutorial.md#2-mindspore-lite-mindir-转换)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
 
 - 执行推理
 

diff --git a/configs/det/dbnet/README_CN_PP-OCRv3.md b/configs/det/dbnet/README_CN_PP-OCRv3.md
@@ -326,10 +326,10 @@ model:
 
 * 分布式训练
 
-在大量数据的情况下，建议用户使用分布式训练。对于在多个昇腾910设备或着GPU卡的分布式训练，请将配置参数`system.distribute`修改为True, 例如：
+在大量数据的情况下，建议用户使用分布式训练。对于在多个昇腾910设备的分布式训练，请将配置参数`system.distribute`修改为True, 例如：
 
 ```shell
-# 在多个 GPU/Ascend 设备上进行分布式训练
+# 在多个 Ascend 设备上进行分布式训练
 mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
 ```
 
@@ -338,7 +338,7 @@ mpirun --allow-run-as-root -n 4 python tools/train.py --config configs/det/dbnet
 如果要在没有分布式训练的情况下在较小的数据集上训练模型，请将配置参数`distribute`修改为False 并运行：
 
 ```shell
-# CPU/GPU/Ascend 设备上的单卡训练
+# CPU/Ascend 设备上的单卡训练
 python tools/train.py --config configs/det/dbnet/db_mobilenetv3_ppocrv3.yaml
 ```
 

diff --git a/configs/det/east/README.md b/configs/det/east/README.md
@@ -138,7 +138,7 @@ python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
 
@@ -158,7 +158,7 @@ Please refer to the tutorial [MindOCR Inference](../../../docs/en/inference/infe
 
 - Model Export
 
-Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../README.md) tutorial and use the following command to export the trained ckpt model to  MindIR file:
+Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../../docs/en/inference/convert_tutorial.md#1-model-export) tutorial and use the following command to export the trained ckpt model to  MindIR file:
 
 ``` shell
 python tools/export.py --model_name_or_config east_resnet50 --data_shape 720 1280 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -170,11 +170,11 @@ The `data_shape` is the model input shape of height and width for MindIR file. T
 
 - Environment Installation
 
-Please refer to [Environment Installation](../../../docs/en/inference/environment.md#2-mindspore-lite-inference) tutorial to configure the MindSpore Lite inference environment.
+Please refer to [Environment Installation](../../../docs/en/inference/environment.md) tutorial to configure the MindSpore Lite inference environment.
 
 - Model Conversion
 
-Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#1-mindocr-models),
+Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#2-mindspore-lite-mindir-convert),
 and use the `converter_lite` tool for offline conversion of the MindIR file.
 
 - Inference

diff --git a/configs/det/east/README_CN.md b/configs/det/east/README_CN.md
@@ -133,7 +133,7 @@ python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/east/east_r50_icdar15.yaml
 ```
 
@@ -149,11 +149,11 @@ python tools/eval.py --config configs/det/east/east_r50_icdar15.yaml
 
 ### 3.6 MindSpore Lite 推理
 
-请参考[MindOCR 推理](../../../docs/cn/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
+请参考[MindOCR 推理](../../../docs/zh/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
 
 - 模型导出
 
-请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../README.md)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
+请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../../docs/zh/inference/convert_tutorial.md#1-模型导出)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
 
 ``` shell
 python tools/export.py --model_name_or_config east_resnet50 --data_shape 720 1280 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -165,11 +165,11 @@ python tools/export.py --model_name_or_config configs/det/east/east_r50_icdar15.
 
 - 环境搭建
 
-请参考[环境安装](../../../docs/cn/inference/environment.md#2-mindspore-lite推理)教程，配置MindSpore Lite推理运行环境。
+请参考[环境安装](../../../docs/zh/inference/environment.md)教程，配置MindSpore Lite推理运行环境。
 
 - 模型转换
 
-请参考[模型转换](../../../docs/cn/inference/convert_tutorial.md#1-mindocr模型)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
+请参考[模型转换](../../../docs/zh/inference/convert_tutorial.md#2-mindspore-lite-mindir-转换)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
 
 
 - 执行推理

diff --git a/configs/det/fcenet/README.md b/configs/det/fcenet/README.md
@@ -157,7 +157,7 @@ python tools/train.py -c=configs/det/fcenet/fce_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 2 python tools/train.py --config configs/det/fcenet/fce_icdar15.yaml
 ```
 
@@ -174,11 +174,11 @@ python tools/eval.py -c=configs/det/fcenet/fce_icdar15.yaml
 
 ### 3.6 MindSpore Lite Inference
 
-Please refer to the tutorial [MindOCR Inference](../../../docs/en/inference/inference_tutorial_en.md) for model inference based on MindSpot Lite on Ascend 310, including the following steps:
+Please refer to the tutorial [MindOCR Inference](../../../docs/en/inference/inference_tutorial.md) for model inference based on MindSpot Lite on Ascend 310, including the following steps:
 
 - Model Export
 
-Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../README.md) tutorial and use the following command to export the trained ckpt model to  MindIR file:
+Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../../docs/en/inference/convert_tutorial.md#1-model-export) tutorial and use the following command to export the trained ckpt model to  MindIR file:
 
 ```shell
 python tools/export.py --model_name_or_config fcenet_resnet50 --data_shape 736 1280 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -190,11 +190,11 @@ The `data_shape` is the model input shape of height and width for MindIR file. T
 
 - Environment Installation
 
-Please refer to [Environment Installation](../../../docs/en/inference/environment_en.md#2-mindspore-lite-inference) tutorial to configure the MindSpore Lite inference environment.
+Please refer to [Environment Installation](../../../docs/en/inference/environment.md) tutorial to configure the MindSpore Lite inference environment.
 
 - Model Conversion
 
-Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial_en.md#1-mindocr-models),
+Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#2-mindspore-lite-mindir-convert),
 and use the `converter_lite` tool for offline conversion of the MindIR file.
 
 - Inference

diff --git a/configs/det/fcenet/README_CN.md b/configs/det/fcenet/README_CN.md
@@ -165,7 +165,7 @@ python tools/train.py --config configs/det/fcenet/fce_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 2 python tools/train.py --config configs/det/fcenet/fce_icdar15.yaml
 ```
 
@@ -181,11 +181,11 @@ python tools/eval.py --config configs/det/fcenet/fce_icdar15.yaml
 
 ### 3.6 MindSpore Lite 推理
 
-请参考[MindOCR 推理](../../../docs/cn/inference/inference_tutorial_cn.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
+请参考[MindOCR 推理](../../../docs/zh/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
 
 - 模型导出
 
-请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../README.md)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
+请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../../docs/zh/inference/convert_tutorial.md#1-模型导出)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
 
 ```shell
 python tools/export.py --model_name_or_config fcenet_resnet50 --data_shape 736 1280 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -197,11 +197,11 @@ python tools/export.py --model_name_or_config configs/det/fcenet/fce_icdar15.yam
 
 - 环境搭建
 
-请参考[环境安装](../../../docs/cn/inference/environment_cn.md#2-mindspore-lite推理)教程，配置MindSpore Lite推理运行环境。
+请参考[环境安装](../../../docs/zh/inference/environment.md)教程，配置MindSpore Lite推理运行环境。
 
 - 模型转换
 
-请参考[模型转换](../../../docs/cn/inference/convert_tutorial_cn.md#1-mindocr模型)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
+请参考[模型转换](../../../docs/zh/inference/convert_tutorial.md#2-mindspore-lite-mindir-转换)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
 
 - 执行推理
 

diff --git a/configs/det/psenet/README.md b/configs/det/psenet/README.md
@@ -168,7 +168,7 @@ python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 Please set `distribute` in yaml config file to be True.
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 ```
 
@@ -188,7 +188,7 @@ Please refer to the tutorial [MindOCR Inference](../../../docs/en/inference/infe
 
 - Model Export
 
-Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../README.md) tutorial and use the following command to export the trained ckpt model to  MindIR file:
+Please [download](#2-results) the exported MindIR file first, or refer to the [Model Export](../../../docs/en/inference/convert_tutorial.md#1-model-export) tutorial and use the following command to export the trained ckpt model to  MindIR file:
 
 ```shell
 python tools/export.py --model_name_or_config psenet_resnet152 --data_shape 1472 2624 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -200,11 +200,11 @@ The `data_shape` is the model input shape of height and width for MindIR file. T
 
 - Environment Installation
 
-Please refer to [Environment Installation](../../../docs/en/inference/environment.md#2-mindspore-lite-inference) tutorial to configure the MindSpore Lite inference environment.
+Please refer to [Environment Installation](../../../docs/en/inference/environment.md) tutorial to configure the MindSpore Lite inference environment.
 
 - Model Conversion
 
-Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#1-mindocr-models),
+Please refer to [Model Conversion](../../../docs/en/inference/convert_tutorial.md#2-mindspore-lite-mindir-convert),
 and use the `converter_lite` tool for offline conversion of the MindIR file.
 
 - Inference

diff --git a/configs/det/psenet/README_CN.md b/configs/det/psenet/README_CN.md
@@ -168,7 +168,7 @@ python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 请确保yaml文件中的`distribute`参数为True。
 
 ```shell
-# n is the number of GPUs/NPUs
+# n is the number of NPUs
 mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/det/psenet/pse_r152_icdar15.yaml
 ```
 
@@ -184,11 +184,11 @@ python tools/eval.py --config configs/det/psenet/pse_r152_icdar15.yaml
 
 ### 3.6 MindSpore Lite 推理
 
-请参考[MindOCR 推理](../../../docs/cn/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
+请参考[MindOCR 推理](../../../docs/zh/inference/inference_tutorial.md)教程，基于MindSpore Lite在Ascend 310上进行模型的推理，包括以下步骤：
 
 - 模型导出
 
-请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../README.md)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
+请先[下载](#2-实验结果)已导出的MindIR文件，或者参考[模型导出](../../../docs/zh/inference/convert_tutorial.md#1-模型导出)教程，使用以下命令将训练完成的ckpt导出为MindIR文件:
 
 ```shell
 python tools/export.py --model_name_or_config psenet_resnet152 --data_shape 1472 2624 --local_ckpt_path /path/to/local_ckpt.ckpt
@@ -200,11 +200,11 @@ python tools/export.py --model_name_or_config configs/det/psenet/pse_r152_icdar1
 
 - 环境搭建
 
-请参考[环境安装](../../../docs/cn/inference/environment.md#2-mindspore-lite推理)教程，配置MindSpore Lite推理运行环境。
+请参考[环境安装](../../../docs/zh/inference/environment.md)教程，配置MindSpore Lite推理运行环境。
 
 - 模型转换
 
-请参考[模型转换](../../../docs/cn/inference/convert_tutorial.md#1-mindocr模型)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
+请参考[模型转换](../../../docs/zh/inference/convert_tutorial.md#2-mindspore-lite-mindir-转换)教程，使用`converter_lite`工具对MindIR模型进行离线转换。
 
 - 执行推理
 

diff --git a/configs/kie/layoutlmv3/README.md b/configs/kie/layoutlmv3/README.md
@@ -183,7 +183,7 @@ eval:
 ```
 
 **Notes:**
-- As the global batch size  (batch_size x num_devices) is important for reproducing the result, please adjust `batch_size` accordingly to keep the global batch size unchanged for a different number of GPUs/NPUs, or adjust the learning rate linearly to a new global batch size.
+- As the global batch size  (batch_size x num_devices) is important for reproducing the result, please adjust `batch_size` accordingly to keep the global batch size unchanged for a different number of NPUs, or adjust the learning rate linearly to a new global batch size.
 
 
 ### 3.2 Model Training
@@ -193,7 +193,7 @@ eval:
 It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please modify the configuration parameter `distribute` as True and run:
 
 ```shell
-# distributed training on multiple GPU/Ascend devices
+# distributed training on multiple Ascend devices
 mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/kie/layoutlmv3/ser_layoutlmv3_xfund_zh.yaml
 ```
 
@@ -203,7 +203,7 @@ mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/kie/layou
 If you want to train or finetune the model on a smaller dataset without distributed training, please modify the configuration parameter`distribute` as False and run:
 
 ```shell
-# standalone training on a CPU/GPU/Ascend device
+# standalone training on a CPU/Ascend device
 python tools/train.py --config configs/kie/layoutlmv3/ser_layoutlmv3_xfund_zh.yaml
 ```