diff --git a/docs/source_en/architecture.md b/docs/source_en/architecture.md
index cd30baf77b5b436e4761e78c2f87545ad6b36c34..f1abfe2fd13779e19e8d854540e7185f60064c27 100644
--- a/docs/source_en/architecture.md
+++ b/docs/source_en/architecture.md
@@ -8,7 +8,7 @@ This document describes the overall architecture of MindSpore.
-\", \"\"}。\n",
+ "这里我们需要将MNIST数据集中随机取出一张图片，并增强成适合LeNet网络的数据格式（如何处理请参考[quick_start.ipynb](https://gitee.com/mindspore/docs/blob/r0.6/tutorials/notebook/quick_start.ipynb)），训练数据集下载地址：{\"\", \"\"}。\n",
""
+ "> 更多设置请参考官网："
]
},
{
diff --git a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb
index fb562620846ff291b4c5438bb568de8baf6af099..fc1027b86958b5ec63929472065328ccc66bdb88 100644
--- a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb
+++ b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb
@@ -439,7 +439,7 @@
"source": [
"2. 使用一类图片当作数据，体验操作。在一个数据量比较大的图片数据集中，例如数据集名称叫`images`，它的存储方式是在`images`文件夹下，有不同子类别的文件夹，一个子类别文件夹中的图片属于同一类。所以我们本次体验所使用的图片放置方法，就需要创建`enhance_images`文件夹，接着在`enhance_images`下建一个名为`sample`的子类别文件夹，将图片放在`sample`文件夹中即可。如果有更多类别图片，可以在`enhance_images`下创建对应的子类别文件夹，将图片放入即可。\n",
"\n",
- " 增强体验使用的数据位置在中，使用过程中可以在此路径下找到图片数据，并参照本次体验中图片放置的位置来新建文件夹。"
+ " 增强体验使用的数据位置在中，使用过程中可以在此路径下找到图片数据，并参照本次体验中图片放置的位置来新建文件夹。"
]
},
{
diff --git a/tutorials/notebook/debugging_in_pynative_mode.ipynb b/tutorials/notebook/debugging_in_pynative_mode.ipynb
index 9b523f43fa66500891b9b39b5299ccf85f15b0dd..eed84a617b2f3f205640aacfa11655c39de2e66f 100644
--- a/tutorials/notebook/debugging_in_pynative_mode.ipynb
+++ b/tutorials/notebook/debugging_in_pynative_mode.ipynb
@@ -34,7 +34,7 @@
"\n",
"4. 执行神经网络训练，查看网络各参数梯度。\n",
"\n",
- "> 你可以在这里找到完整可运行的样例代码：。"
+ "> 你可以在这里找到完整可运行的样例代码：。"
]
},
{
@@ -55,7 +55,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "这里我们需要将MNIST数据集中随机取出一张图片，并增强成适合LeNet网络的数据格式（如何处理请参考[quick_start.ipynb](https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/quick_start.ipynb)），训练数据集下载地址：{\"\", \"\"} 。\n",
+ "这里我们需要将MNIST数据集中随机取出一张图片，并增强成适合LeNet网络的数据格式（如何处理请参考[quick_start.ipynb](https://gitee.com/mindspore/docs/blob/r0.6/tutorials/notebook/quick_start.ipynb)），训练数据集下载地址：{\"\", \"\"} 。\n",
"。"
+ "本次体验将使用快速入门案例作为基础用例，将MindInsight的模型溯源和数据溯源的数据记录功能加入到案例中，快速入门案例的源码请参考：。"
]
},
{
@@ -402,7 +402,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "初始化`SummaryCollector`，使用`collect_specified_data`控制需要记录的数据，我们这里只需要记录模型溯源和数据溯源，所以将`collect_train_lineage`和`collect_eval_lineage`参数设置成`True`,其他的参数使用`keep_default_action`设置成`False`，SummaryCollector能够记录哪些数据，请参考官网：。"
+ "初始化`SummaryCollector`，使用`collect_specified_data`控制需要记录的数据，我们这里只需要记录模型溯源和数据溯源，所以将`collect_train_lineage`和`collect_eval_lineage`参数设置成`True`,其他的参数使用`keep_default_action`设置成`False`，SummaryCollector能够记录哪些数据，请参考官网：。"
]
},
{
@@ -452,7 +452,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "这里主要展示如何启用及关闭MindInsight，更多的命令集信息，请参考MindSpore官方网站：。"
+ "这里主要展示如何启用及关闭MindInsight，更多的命令集信息，请参考MindSpore官方网站：。"
]
},
{
@@ -488,7 +488,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -527,7 +527,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -581,7 +581,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
@@ -617,7 +617,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ""
+ ""
]
},
{
diff --git a/tutorials/notebook/mindspore_computer_vision_application.ipynb b/tutorials/notebook/mindspore_computer_vision_application.ipynb
index f1ed253fd77a35eb5c30db80f1eff8efd48b317b..1ea4af281bfa8f5ca641ddeb5e05d4f2eae4552a 100644
--- a/tutorials/notebook/mindspore_computer_vision_application.ipynb
+++ b/tutorials/notebook/mindspore_computer_vision_application.ipynb
@@ -71,7 +71,7 @@
"metadata": {},
"source": [
"本次面向Ascend 910 AI处理器硬件平台,将卷积神经网络ResNet加入到案例中，你可以在这里下载完整的样例代码案例作为基础用例：\n",
- "https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/resnet"
+ "https://gitee.com/mindspore/docs/blob/r0.6/tutorials/tutorial_code/resnet"
]
},
{
diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb
index e56f9cd1746269e946b4b8b5c80ff4ef3adf98e4..255f87930a16712c74cd3a5d5f04f92bfb04019f 100644
--- a/tutorials/notebook/quick_start.ipynb
+++ b/tutorials/notebook/quick_start.ipynb
@@ -34,7 +34,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "说明：。"
+ "说明：。"
]
},
{
@@ -470,7 +470,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- ".
+> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： .
The key parts of the task process code are explained below.
@@ -143,7 +143,7 @@ CNN is a standard algorithm for image classification tasks. CNN uses a layered s
ResNet is recommended. First, it is deep enough with 34 layers, 50 layers, or 101 layers. The deeper the hierarchy, the stronger the representation capability, and the higher the classification accuracy. Second, it is learnable. The residual structure is used. The lower layer is directly connected to the upper layer through the shortcut connection, which solves the problem of gradient disappearance caused by the network depth during the reverse propagation. In addition, the ResNet network has good performance, including the recognition accuracy, model size, and parameter quantity.
-MindSpore Model Zoo has a ResNet [model](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py). The calling method is as follows:
+MindSpore Model Zoo has a ResNet [model](https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/resnet/src/resnet.py). The calling method is as follows:
```python
network = resnet50(class_num=10)
diff --git a/tutorials/source_en/advanced_use/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md
index acfc29cc5320063d1906d4cd93b4adc5e20b4dfb..df84772dc5ac21a735061291cd9086461c540969 100644
--- a/tutorials/source_en/advanced_use/customized_debugging_information.md
+++ b/tutorials/source_en/advanced_use/customized_debugging_information.md
@@ -15,7 +15,7 @@
-.
+> This example is for the Ascend 910 AI processor. You can download the complete sample code from .
## Implementation
diff --git a/tutorials/source_en/advanced_use/distributed_training_ascend.md b/tutorials/source_en/advanced_use/distributed_training_ascend.md
index 919c9de4b95dadfd250c19cb3c4d9609425a3b6e..e8d55ce1ef477dec13d387369bebd2286970526a 100644
--- a/tutorials/source_en/advanced_use/distributed_training_ascend.md
+++ b/tutorials/source_en/advanced_use/distributed_training_ascend.md
@@ -18,12 +18,12 @@
-
+> Download address of the complete sample code:
## Preparations
@@ -154,7 +154,7 @@ Different from the single-node system, the multi-node system needs to transfer t
## Defining the Network
-In data parallel and automatic parallel modes, the network definition method is the same as that in a single-node system. The reference code is as follows:
+In data parallel and automatic parallel modes, the network definition method is the same as that in a single-node system. The reference code is as follows:
## Defining the Loss Function and Optimizer
diff --git a/tutorials/source_en/advanced_use/graph_kernel_fusion.md b/tutorials/source_en/advanced_use/graph_kernel_fusion.md
index 35a873a656a541f9f0fa6a2775c80b875c100d7e..1b88a6db9bc2cf0cc156ee4b4014b5859746db1c 100644
--- a/tutorials/source_en/advanced_use/graph_kernel_fusion.md
+++ b/tutorials/source_en/advanced_use/graph_kernel_fusion.md
@@ -12,7 +12,7 @@
-. You only need to modify the `context` parameter.
+ Take the training model of the `BERT-large` network as an example. For details about the dataset and training script, see . You only need to modify the `context` parameter.
## Effect Evaluation
diff --git a/tutorials/source_en/advanced_use/host_device_training.md b/tutorials/source_en/advanced_use/host_device_training.md
index d52d54d0b58a20c5e9ebbc1281f5a00336e56211..ea188e176e5fe5c5c632a7f1c6de42f56c8b20e5 100644
--- a/tutorials/source_en/advanced_use/host_device_training.md
+++ b/tutorials/source_en/advanced_use/host_device_training.md
@@ -10,19 +10,19 @@
-, in which `train_and_eval_auto_parallel.py` is the main function for training,
+1. Prepare the model. The Wide&Deep code can be found at: , in which `train_and_eval_auto_parallel.py` is the main function for training,
`src/` directory contains the model definition, data processing and configuration files, `script/` directory contains the launch scripts in different modes.
2. Prepare the dataset. The dataset can be found at: . Use the script `src/preprocess_data.py` to transform dataset into MindRecord format.
diff --git a/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md b/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md
index e4f4b2b8233960d5a00c98b1c0c2430ef722318c..7eec37e42aa8f5a1a89495fc2ab2c4e2441ec3a6 100644
--- a/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md
+++ b/tutorials/source_en/advanced_use/lineage_and_scalars_comparision.md
@@ -11,7 +11,7 @@
-
+> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at：
> - `mnist_attack_fgsm.py`: contains attack code.
> - `mnist_defense_nad.py`: contains defense code.
diff --git a/tutorials/source_en/advanced_use/network_migration.md b/tutorials/source_en/advanced_use/network_migration.md
index b6e49d57b80fc12bf84080e90bf0670e1b98240d..ad1f277d40d9d55f1197c328d043fe29c86b6a80 100644
--- a/tutorials/source_en/advanced_use/network_migration.md
+++ b/tutorials/source_en/advanced_use/network_migration.md
@@ -17,7 +17,7 @@
-.
+ Import the corresponding MindSpore modules based on the required APIs. For details about the module list, see .
2. Load and preprocess a dataset.
- Use MindSpore to build the required dataset. Currently, MindSpore supports common datasets. You can call APIs in the original format, `MindRecord`, and `TFRecord`. In addition, MindSpore supports data processing and data augmentation. For details, see the [Data Preparation](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_preparation.html).
+ Use MindSpore to build the required dataset. Currently, MindSpore supports common datasets. You can call APIs in the original format, `MindRecord`, and `TFRecord`. In addition, MindSpore supports data processing and data augmentation. For details, see the [Data Preparation](https://www.mindspore.cn/tutorial/en/r0.6/use/data_preparation/data_preparation.html).
In this example, the CIFAR-10 dataset is loaded, which supports both single-GPU and multi-GPU scenarios.
@@ -79,7 +79,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
num_shards=device_num, shard_id=rank_id)
```
- Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see .
+ Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see .
3. Build a network.
@@ -214,7 +214,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
6. Build the entire network.
- The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
+ The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
7. Define a loss function and an optimizer.
@@ -235,7 +235,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
```
- You can use a built-in assessment method of `Model` by setting the [metrics](https://www.mindspore.cn/tutorial/en/master/advanced_use/customized_debugging_information.html#mindspore-metrics) attribute.
+ You can use a built-in assessment method of `Model` by setting the [metrics](https://www.mindspore.cn/tutorial/en/r0.6/advanced_use/customized_debugging_information.html#mindspore-metrics) attribute.
```python
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
@@ -264,15 +264,15 @@ The accuracy optimization process is as follows:
#### On-Cloud Integration
-Run your scripts on ModelArts. For details, see [Using MindSpore on Cloud](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html).
+Run your scripts on ModelArts. For details, see [Using MindSpore on Cloud](https://www.mindspore.cn/tutorial/zh-CN/r0.6/advanced_use/use_on_the_cloud.html).
### Inference Phase
-Models trained on the Ascend 910 AI processor can be used for inference on different hardware platforms. Refer to the [Multi-platform Inference Tutorial](https://www.mindspore.cn/tutorial/en/master/use/multi_platform_inference.html) for detailed steps.
+Models trained on the Ascend 910 AI processor can be used for inference on different hardware platforms. Refer to the [Multi-platform Inference Tutorial](https://www.mindspore.cn/tutorial/en/r0.6/use/multi_platform_inference.html) for detailed steps.
## Examples
-1. [Common dataset examples](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/loading_the_datasets.html)
+1. [Common dataset examples](https://www.mindspore.cn/tutorial/en/r0.6/use/data_preparation/loading_the_datasets.html)
-2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)
+2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.6/model_zoo)
diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md
index 4e56d1116a3ffb33aadd2dd61658e4825cdac440..516345622ba9d39550b3371a7ce262313b7d5a2d 100644
--- a/tutorials/source_en/advanced_use/nlp_application.md
+++ b/tutorials/source_en/advanced_use/nlp_application.md
@@ -13,7 +13,7 @@
- [Configuring Environment Information](#configuring-environment-information)
- [Preprocessing the Dataset](#preprocessing-the-dataset)
- [Defining the Network](#defining-the-network)
- - [Pre-Traning](#pre-training)
+ - [Pre-Training](#pre-training)
- [Defining the Optimizer and Loss Function](#defining-the-optimizer-and-loss-function)
- [Training and Saving the Model](#training-and-saving-the-model)
- [Validating the Model](#validating-the-model)
@@ -21,7 +21,7 @@
-
+> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at：
> - `src/config.py`：some configurations on the network, including the batch size and number of training epochs.
> - `src/dataset.py`：dataset related definition，include MindRecord file convert and data-preprocess, etc.
> - `src/imdb.py`： the util class for parsing IMDB dataset.
@@ -156,7 +156,7 @@ if args.preprocess == "true":
```
> After convert success, we can file `mindrecord` files under the directory `preprocess_path`. Usually, this operation does not need to be performed every time while the data set is unchanged.
-> `convert_to_mindrecord` You can find the complete definition at:
+> `convert_to_mindrecord` You can find the complete definition at:
> It consists of two steps:
>1. Process the text dataset, including encoding, word segmentation, alignment, and processing the original GloVe data to adapt to the network structure.
@@ -176,7 +176,7 @@ network = SentimentNet(vocab_size=embedding_table.shape[0],
weight=Tensor(embedding_table),
batch_size=cfg.batch_size)
```
-> `SentimentNet` You can find the complete definition at:
+> `SentimentNet` You can find the complete definition at:
### Pre-Training
@@ -215,7 +215,7 @@ else:
model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("============== Training Success ==============")
```
-> `lstm_create_dataset` You can find the complete definition at:
+> `lstm_create_dataset` You can find the complete definition at:
### Validating the Model
diff --git a/tutorials/source_en/advanced_use/on_device_inference.md b/tutorials/source_en/advanced_use/on_device_inference.md
index a0fb1c8c61a35455a2caf3fe50962eb531c766cf..a714e9e8c1be2666385b17f07d8c6b96c7ec4a33 100644
--- a/tutorials/source_en/advanced_use/on_device_inference.md
+++ b/tutorials/source_en/advanced_use/on_device_inference.md
@@ -11,7 +11,7 @@
-. |
+| Network | Supports networks such as LeNet and ResNet50. For details, see . |
| Algorithm | Supports symmetric and asymmetric quantization algorithms in MindSpore fake quantization training. |
| Solution | Supports 4-, 7-, and 8-bit quantization solutions. |
@@ -74,7 +74,7 @@ Compared with common training, the quantization aware training requires addition
Next, the LeNet network is used as an example to describe steps 3 and 6.
-> You can obtain the complete executable sample code at .
+> You can obtain the complete executable sample code at .
### Defining a Fusion Network
@@ -173,7 +173,7 @@ The preceding describes the quantization aware training from scratch. A more com
2. Define a network.
3. Define a fusion network.
4. Define an optimizer and loss function.
- 5. Load a model file and retrain the model. Load an existing model file and retrain the model based on the fusion network to generate a fusion model. For details, see .
+ 5. Load a model file and retrain the model. Load an existing model file and retrain the model based on the fusion network to generate a fusion model. For details, see .
6. Generate a quantization network.
7. Perform quantization training.
@@ -181,7 +181,7 @@ The preceding describes the quantization aware training from scratch. A more com
The inference using a quantization model is the same as common model inference. The inference can be performed by directly using the checkpoint file or converting the checkpoint file into a common model format (such as ONNX or GEIR).
-For details, see .
+For details, see .
- To use a checkpoint file obtained after quantization aware training for inference, perform the following steps:
diff --git a/tutorials/source_en/advanced_use/summary_record.md b/tutorials/source_en/advanced_use/summary_record.md
index 07982670cec921762a8cdba1fefc153d6e99458a..9764aa5b8131f00df1a21eb6b619d0477492ff20 100644
--- a/tutorials/source_en/advanced_use/summary_record.md
+++ b/tutorials/source_en/advanced_use/summary_record.md
@@ -13,7 +13,7 @@
-.
+> You can find the complete executable sample code at .
This is a simple and basic application process. For other advanced and complex applications, extend this basic process as needed.
@@ -83,7 +83,7 @@ Currently, the `os` libraries are required. For ease of understanding, other req
import os
```
-For details about MindSpore modules, search on the [MindSpore API Page](https://www.mindspore.cn/api/en/master/index.html).
+For details about MindSpore modules, search on the [MindSpore API Page](https://www.mindspore.cn/api/en/r0.6/index.html).
### Configuring the Running Information
@@ -179,7 +179,7 @@ In the preceding information:
Perform the shuffle and batch operations, and then perform the repeat operation to ensure that data during an epoch is unique.
-> MindSpore supports multiple data processing and augmentation operations, which are usually combined. For details, see section "Data Processing and Augmentation" in the MindSpore Tutorials (https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_processing_and_augmentation.html).
+> MindSpore supports multiple data processing and augmentation operations, which are usually combined. For details, see section "Data Processing and Augmentation" in the MindSpore Tutorials (https://www.mindspore.cn/tutorial/en/r0.6/use/data_preparation/data_processing_and_augmentation.html).
## Defining the Network
diff --git a/tutorials/source_en/quick_start/quick_video.md b/tutorials/source_en/quick_start/quick_video.md
index fde8761558a09f0753d8e20eb843ec6f32d9a4b1..f61b3560e1f8a242a572f88be6c373deb21f7c29 100644
--- a/tutorials/source_en/quick_start/quick_video.md
+++ b/tutorials/source_en/quick_start/quick_video.md
@@ -10,7 +10,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
-
-
-**View the full tutorial**：
\ No newline at end of file
+**View the full tutorial**：
\ No newline at end of file
diff --git a/tutorials/source_en/quick_start/quick_video/mindInsight_installation_and_common_commands.md b/tutorials/source_en/quick_start/quick_video/mindInsight_installation_and_common_commands.md
index 4f5fd939f30334d602558672bac4b91559e05ed3..0c5784aec4b1f81bb37d0743b55eccd19696185a 100644
--- a/tutorials/source_en/quick_start/quick_video/mindInsight_installation_and_common_commands.md
+++ b/tutorials/source_en/quick_start/quick_video/mindInsight_installation_and_common_commands.md
@@ -8,4 +8,4 @@
**Install now**:
-**More commands**:
\ No newline at end of file
+**More commands**:
\ No newline at end of file
diff --git a/tutorials/source_en/quick_start/quick_video/quick_start_video.md b/tutorials/source_en/quick_start/quick_video/quick_start_video.md
index eb4483b1f87c61abc1df0670ee17b5d6e9aa995d..5ddab8c59c8c731acae753866fbb049e3f591105 100644
--- a/tutorials/source_en/quick_start/quick_video/quick_start_video.md
+++ b/tutorials/source_en/quick_start/quick_video/quick_start_video.md
@@ -6,6 +6,6 @@
-**View code**:
+**View code**:
-**View the full tutorial**：
\ No newline at end of file
+**View the full tutorial**：
\ No newline at end of file
diff --git a/tutorials/source_en/quick_start/quick_video/saving_and_loading_model_parameters.md b/tutorials/source_en/quick_start/quick_video/saving_and_loading_model_parameters.md
index 12c9d36d2a243cf7d8399f01d1a4ddf66bc9afca..0b36ba126bb1ca316ff128e197481b3406ef158c 100644
--- a/tutorials/source_en/quick_start/quick_video/saving_and_loading_model_parameters.md
+++ b/tutorials/source_en/quick_start/quick_video/saving_and_loading_model_parameters.md
@@ -6,4 +6,4 @@
-**View the full tutorial**：
\ No newline at end of file
+**View the full tutorial**：
\ No newline at end of file
diff --git a/tutorials/source_en/use/custom_operator.md b/tutorials/source_en/use/custom_operator.md
index c6f4296a3df4ed91a1834627fa4e5e79b5b90425..1a44685de1d44d267810db0b9b312dc231900199 100644
--- a/tutorials/source_en/use/custom_operator.md
+++ b/tutorials/source_en/use/custom_operator.md
@@ -14,7 +14,7 @@
-.
- Operator information: describes basic information about a TBE operator, such as the operator name and supported input and output types. It is the basis for the backend to select and map operators.
-This section takes a Square operator as an example to describe how to customize an operator. For details, see cases in [tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe) in the MindSpore source code.
+This section takes a Square operator as an example to describe how to customize an operator. For details, see cases in [tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/r0.6/tests/st/ops/custom_ops_tbe) in the MindSpore source code.
## Registering the Operator Primitive
The primitive of an operator is a subclass inherited from `PrimitiveWithInfer`. The type name of the subclass is the operator name.
The definition of the custom operator primitive is the same as that of the built-in operator primitive.
-- The attribute is defined by the input parameter of the constructor function `__init__`. The operator in this test case has no attribute. Therefore, `__init__` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code.
+- The attribute is defined by the input parameter of the constructor function `__init__`. The operator in this test case has no attribute. Therefore, `__init__` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/tree/r0.6/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code.
- The input and output names are defined by the `init_prim_io_names` function.
- The shape inference method of the output tensor is defined in the `infer_shape` function, and the dtype inference method of the output tensor is defined in the `infer_dtype` function.
diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md
index 11ea33c866010d2275ede0a1831a1da03e1b1320..02672b252a5973f40122c4604c00f121b7ca5fd6 100644
--- a/tutorials/source_en/use/data_preparation/converting_datasets.md
+++ b/tutorials/source_en/use/data_preparation/converting_datasets.md
@@ -14,7 +14,7 @@
-
+ Network List
custom_operator
\ No newline at end of file
diff --git a/tutorials/source_en/use/multi_platform_inference.md b/tutorials/source_en/use/multi_platform_inference.md
index 0d03a9d41d6c83d00306664287b3e2f865cea05b..cc705c0a983e1765ba4567bb86b26229e703a1af 100644
--- a/tutorials/source_en/use/multi_platform_inference.md
+++ b/tutorials/source_en/use/multi_platform_inference.md
@@ -18,7 +18,7 @@
-.
- > Inference sample code: .
+ `model.eval` is an API for model validation. For details about the API, see .
+ > Inference sample code: .
2. Use the `model.predict` API to perform inference.
```python
model.predict(input_data)
```
In the preceding information:
- `model.predict` is an API for inference. For details about the API, see .
+ `model.predict` is an API for inference. For details about the API, see .
## Inference on the Ascend 310 AI processor
@@ -74,7 +74,7 @@ MindSpore supports the following inference scenarios based on the hardware platf
The Ascend 310 AI processor is equipped with the ACL framework and supports the OM format which needs to be converted from the model in ONNX or GEIR format. For inference on the Ascend 310 AI processor, perform the following steps:
-1. Generate a model in ONNX or GEIR format on the training platform. For details, see [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
+1. Generate a model in ONNX or GEIR format on the training platform. For details, see [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/r0.6/use/saving_and_loading_model_parameters.html#geironnx).
2. Convert the ONNX or GEIR model file into an OM model file and perform inference.
- For performing inference in the cloud environment (ModelArt), see the [Ascend 910 training and Ascend 310 inference samples](https://support.huaweicloud.com/bestpractice-modelarts/modelarts_10_0026.html).
@@ -88,7 +88,7 @@ The inference is the same as that on the Ascend 910 AI processor.
### Inference Using an ONNX File
-1. Generate a model in ONNX format on the training platform. For details, see [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
+1. Generate a model in ONNX format on the training platform. For details, see [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/r0.6/use/saving_and_loading_model_parameters.html#geironnx).
2. Perform inference on a GPU by referring to the runtime or SDK document. For example, use TensorRT to perform inference on the NVIDIA GPU. For details, see [TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt).
@@ -100,10 +100,10 @@ The inference is the same as that on the Ascend 910 AI processor.
### Inference Using an ONNX File
Similar to the inference on a GPU, the following steps are required:
-1. Generate a model in ONNX format on the training platform. For details, see [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
+1. Generate a model in ONNX format on the training platform. For details, see [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/r0.6/use/saving_and_loading_model_parameters.html#geironnx).
2. Perform inference on a CPU by referring to the runtime or SDK document. For details about how to use the ONNX Runtime, see the [ONNX Runtime document](https://github.com/microsoft/onnxruntime).
## On-Device Inference
-MindSpore Predict is an inference engine for on-device inference. For details, see [On-Device Inference](https://www.mindspore.cn/tutorial/en/master/advanced_use/on_device_inference.html).
\ No newline at end of file
+MindSpore Predict is an inference engine for on-device inference. For details, see [On-Device Inference](https://www.mindspore.cn/tutorial/en/r0.6/advanced_use/on_device_inference.html).
\ No newline at end of file
diff --git a/tutorials/source_en/use/saving_and_loading_model_parameters.md b/tutorials/source_en/use/saving_and_loading_model_parameters.md
index 9b2cc2625b9de393f2d27cf3c84b5f59397936b8..fab84d654c1b9d599e6536160dcac725cd3a5553 100644
--- a/tutorials/source_en/use/saving_and_loading_model_parameters.md
+++ b/tutorials/source_en/use/saving_and_loading_model_parameters.md
@@ -13,7 +13,7 @@
-
+> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码：
下面对任务流程中各个环节及代码关键片段进行解释说明。
@@ -146,7 +146,7 @@ tar -zvxf cifar-10-binary.tar.gz
ResNet通常是较好的选择。首先，它足够深，常见的有34层，50层，101层。通常层次越深，表征能力越强，分类准确率越高。其次，可学习，采用了残差结构，通过shortcut连接把低层直接跟高层相连，解决了反向传播过程中因为网络太深造成的梯度消失问题。此外，ResNet网络的性能很好，既表现为识别的准确率，也包括它本身模型的大小和参数量。
-MindSpore Model Zoo中已经实现了ResNet模型，可以采用[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)。调用方法如下：
+MindSpore Model Zoo中已经实现了ResNet模型，可以采用[ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/resnet/src/resnet.py)。调用方法如下：
```python
network = resnet50(class_num=10)
diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md
index 3c77fb9c5f9bee4375fd1de83921ea6746fb146c..a36eda4f8a557d372e397d96a9c4e014865701cf 100644
--- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md
+++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md
@@ -14,9 +14,9 @@
-
+> 本例面向Ascend 910 AI处理器，你可以在这里下载完整的样例代码：
## 实现阶段
diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md b/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md
index 4430e3ee5c830084b2239df8487938fa68e95c68..4a311467794895b8bf64ef8d6f5d2799cd9ccc18 100644
--- a/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md
+++ b/tutorials/source_zh_cn/advanced_use/distributed_training_ascend.md
@@ -18,14 +18,14 @@
-
+>
## 准备环节
@@ -157,7 +157,7 @@ def create_dataset(data_path, repeat_num=1, batch_size=32, rank_id=0, rank_size=
## 定义网络
-数据并行及自动并行模式下，网络定义方式与单机一致。代码请参考：
+数据并行及自动并行模式下，网络定义方式与单机一致。代码请参考：
## 定义损失函数及优化器
diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md b/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md
index 2ddf2cb05ecde1e38abe41855454efd8626e3021..bba26fa72eda3811f65acaca7af62ba174a29113 100644
--- a/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md
+++ b/tutorials/source_zh_cn/advanced_use/distributed_training_gpu.md
@@ -14,7 +14,7 @@
-。
+> 。
### 配置分布式环境
@@ -73,7 +73,7 @@ if __name__ == "__main__":
在GPU硬件平台上，网络的定义和Ascend 910 AI处理器一致。
-> 网络、优化器、损失函数的定义参考：。
+> 网络、优化器、损失函数的定义参考：。
## 运行脚本
diff --git a/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md b/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md
index 5156fd4335cc52088a699f6c4ec4f30821fb43a2..aba5c9596a41eaa70f53a697fa93999c0c9a1a1f 100644
--- a/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md
+++ b/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md
@@ -12,7 +12,7 @@
-，同样我们只需修改`context`参数即可。
+ ，同样我们只需修改`context`参数即可。
## 效果评估
diff --git a/tutorials/source_zh_cn/advanced_use/host_device_training.md b/tutorials/source_zh_cn/advanced_use/host_device_training.md
index a64ad4101e96535215cbffb1381f7061800fc069..24afa5fc416bf38aa604bc1d9e1c878be1b7bc01 100644
--- a/tutorials/source_zh_cn/advanced_use/host_device_training.md
+++ b/tutorials/source_zh_cn/advanced_use/host_device_training.md
@@ -10,17 +10,17 @@
-，其中，`train_and_eval_auto_parallel.py`为训练的主函数所在，`src/`目录中包含Wide&Deep模型的定义、数据处理和配置信息等，`script/`目录中包含不同配置下的训练脚本。
+1. 准备模型代码。Wide&Deep的代码可参见：，其中，`train_and_eval_auto_parallel.py`为训练的主函数所在，`src/`目录中包含Wide&Deep模型的定义、数据处理和配置信息等，`script/`目录中包含不同配置下的训练脚本。
2. 准备数据集。数据集下载链接：。利用脚本`src/preprocess_data.py`将数据集转换为MindRecord格式。
diff --git a/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md b/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md
index 07488191174a371c481ed7565a25cfc93947eaa4..9bd0b0ed42fb39bf3c9a8dde15bcacf138b69309 100644
--- a/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md
+++ b/tutorials/source_zh_cn/advanced_use/lineage_and_scalars_comparision.md
@@ -11,8 +11,8 @@
-
+> 本例面向CPU、GPU、Ascend 910 AI处理器，你可以在这里下载完整的样例代码：
> - `mnist_attack_fgsm.py`：包含攻击代码。
> - `mnist_defense_nad.py`：包含防御代码。
diff --git a/tutorials/source_zh_cn/advanced_use/network_migration.md b/tutorials/source_zh_cn/advanced_use/network_migration.md
index ec5d1b7da8f596e628b769ece2545d0bd7f9cec6..9fc0d867f00a3304b8ba9d08534b3f11a05d49f6 100644
--- a/tutorials/source_zh_cn/advanced_use/network_migration.md
+++ b/tutorials/source_zh_cn/advanced_use/network_migration.md
@@ -17,7 +17,7 @@
-。
+ 根据所需使用的接口，导入相应的MindSpore模块，模块列表详见。
2. 加载数据集和预处理。
- 使用MindSpore构造你需要使用的数据集。目前MindSpore已支持常见数据集，你可以通过原始格式、`MindRecord`、`TFRecord`等多种接口调用，同时还支持数据处理以及数据增强等相关功能，具体用法可参考[准备数据教程](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/data_preparation.html)。
+ 使用MindSpore构造你需要使用的数据集。目前MindSpore已支持常见数据集，你可以通过原始格式、`MindRecord`、`TFRecord`等多种接口调用，同时还支持数据处理以及数据增强等相关功能，具体用法可参考[准备数据教程](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/data_preparation/data_preparation.html)。
本例中加载了Cifar-10数据集，可同时支持单卡和多卡的场景。
@@ -77,7 +77,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
num_shards=device_num, shard_id=rank_id)
```
- 然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见。
+ 然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见。
3. 构建网络。
@@ -210,7 +210,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
6. 构造整网。
- 将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。
+ 将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。
7. 定义损失函数和优化器。
@@ -231,7 +231,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
```
- 如果希望使用`Model`内置的评估方法，则可以使用[metrics](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#mindspore-metrics)属性设置希望使用的评估方法。
+ 如果希望使用`Model`内置的评估方法，则可以使用[metrics](https://www.mindspore.cn/tutorial/zh-CN/r0.6/advanced_use/customized_debugging_information.html#mindspore-metrics)属性设置希望使用的评估方法。
```python
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
@@ -259,14 +259,14 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
#### 云上集成
-请参考[在云上使用MindSpore](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html)，将你的脚本运行在ModelArts。
+请参考[在云上使用MindSpore](https://www.mindspore.cn/tutorial/zh-CN/r0.6/advanced_use/use_on_the_cloud.html)，将你的脚本运行在ModelArts。
### 推理阶段
-在Ascend 910 AI处理器上训练后的模型，支持在不同的硬件平台上执行推理。详细步骤可参考[多平台推理教程](https://www.mindspore.cn/tutorial/zh-CN/master/use/multi_platform_inference.html)。
+在Ascend 910 AI处理器上训练后的模型，支持在不同的硬件平台上执行推理。详细步骤可参考[多平台推理教程](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/multi_platform_inference.html)。
## 样例参考
-1. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html)
+1. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/data_preparation/loading_the_datasets.html)
-2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)
+2. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.6/model_zoo)
diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md
index 7e1b05b9d3b1bbda6d013d56fa15aba23621431e..198aa8da028872917b63d003c177370548592c2e 100644
--- a/tutorials/source_zh_cn/advanced_use/nlp_application.md
+++ b/tutorials/source_zh_cn/advanced_use/nlp_application.md
@@ -21,8 +21,8 @@
-
+> 本例面向GPU或CPU硬件平台，你可以在这里下载完整的样例代码：
> - `src/config.py`：网络中的一些配置，包括`batch size`、进行几次epoch训练等。
> - `src/dataset.py`：数据集相关，包括转换成MindRecord文件，数据预处理等。
> - `src/imdb.py`： 解析IMDB数据集的工具。
@@ -156,7 +156,7 @@ if args.preprocess == "true":
```
> 转换成功后会在`preprocess_path`路径下生成`mindrecord`文件； 通常该操作在数据集不变的情况下，无需每次训练都执行。
-> `convert_to_mindrecord`函数的具体实现请参考
+> `convert_to_mindrecord`函数的具体实现请参考
> 其中包含两大步骤：
> 1. 解析文本数据集，包括编码、分词、对齐、处理GloVe原始数据，使之能够适应网络结构。
@@ -176,7 +176,7 @@ network = SentimentNet(vocab_size=embedding_table.shape[0],
weight=Tensor(embedding_table),
batch_size=cfg.batch_size)
```
-> `SentimentNet`网络结构的具体实现请参考
+> `SentimentNet`网络结构的具体实现请参考
### 预训练模型
@@ -215,7 +215,7 @@ else:
model.train(cfg.num_epochs, ds_train, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("============== Training Success ==============")
```
-> `lstm_create_dataset`函数的具体实现请参考
+> `lstm_create_dataset`函数的具体实现请参考
### 模型验证
diff --git a/tutorials/source_zh_cn/advanced_use/on_device_inference.md b/tutorials/source_zh_cn/advanced_use/on_device_inference.md
index 0cd3a6ba882f2d12a555de386b7dfaa0369b9e31..1dd0e3aafd8e2b4e64e3912e67cc6a034ffe4f1b 100644
--- a/tutorials/source_zh_cn/advanced_use/on_device_inference.md
+++ b/tutorials/source_zh_cn/advanced_use/on_device_inference.md
@@ -11,7 +11,7 @@
-，使用[MNIST数据集](http://yann.lecun.com/exdb/mnist/)，了解如何训练一个LeNet网络。
+参考，使用[MNIST数据集](http://yann.lecun.com/exdb/mnist/)，了解如何训练一个LeNet网络。
### 参数设置
@@ -43,7 +43,7 @@ Parameter Server(参数服务器)是分布式训练中一种广泛使用的架
- 通过`mindspore.nn.Cell.set_param_ps()`对`nn.Cell`中所有权重递归设置
- 通过`mindspore.common.Parameter.set_param_ps()`对此权重进行设置
-在[原训练脚本](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/train.py)基础上，设置LeNet模型所有权重通过Parameter Server训练：
+在[原训练脚本](https://gitee.com/mindspore/mindspore/blob/r0.6/model_zoo/official/cv/lenet/train.py)基础上，设置LeNet模型所有权重通过Parameter Server训练：
```python
network = LeNet5(cfg.num_classes)
network.set_param_ps()
diff --git a/tutorials/source_zh_cn/advanced_use/performance_profiling.md b/tutorials/source_zh_cn/advanced_use/performance_profiling.md
index 6eef098c28019cca717e839afe1f0f4608ea6b96..1d31637172d485333f69851c3f393d1964bb48e9 100644
--- a/tutorials/source_zh_cn/advanced_use/performance_profiling.md
+++ b/tutorials/source_zh_cn/advanced_use/performance_profiling.md
@@ -17,7 +17,7 @@
-。 |
+| 网络支持 | 已实现的网络包括LeNet、ResNet50等网络，具体请参见。 |
| 算法支持 | 在MindSpore的伪量化训练中，支持非对称和对称的量化算法。 |
| 方案支持 | 支持4、7和8比特的量化方案。 |
@@ -74,7 +74,7 @@ MindSpore的感知量化训练是在训练基础上，使用低精度数据替
接下来，以LeNet网络为例，展开叙述3、6两个步骤。
-> 你可以在这里找到完整可运行的样例代码： 。
+> 你可以在这里找到完整可运行的样例代码： 。
### 定义融合网络
@@ -173,7 +173,7 @@ net = qat.convert_quant_network(net, quant_delay=0, bn_fold=False, freeze_bn=100
2. 定义网络。
3. 定义融合网络。
4. 定义优化器和损失函数。
- 5. 加载模型文件模型重训。加载已有模型文件，基于融合网络重新训练生成融合模型。详细模型重载训练，请参见
+ 5. 加载模型文件模型重训。加载已有模型文件，基于融合网络重新训练生成融合模型。详细模型重载训练，请参见
6. 转化量化网络。
7. 进行量化训练。
@@ -181,7 +181,7 @@ net = qat.convert_quant_network(net, quant_delay=0, bn_fold=False, freeze_bn=100
使用量化模型进行推理，与普通模型推理一致，分为直接checkpoint文件推理及转化为通用模型格式（ONNX、GEIR等）进行推理。
-> 推理详细说明请参见。
+> 推理详细说明请参见。
- 使用感知量化训练后得到的checkpoint文件进行推理：
diff --git a/tutorials/source_zh_cn/advanced_use/serving.md b/tutorials/source_zh_cn/advanced_use/serving.md
index e6719fe71c3190d9ac2e3d91cd548c7d7fae402d..4bc0e5c104d83546e12c6fa759f121925b30189a 100644
--- a/tutorials/source_zh_cn/advanced_use/serving.md
+++ b/tutorials/source_zh_cn/advanced_use/serving.md
@@ -12,7 +12,7 @@
- [客户端示例](#客户端示例)
-] [--model_name ]
下面以一个简单的网络为例，演示MindSpore Serving如何使用。
### 导出模型
-使用[add_model.py](https://gitee.com/mindspore/mindspore/blob/master/serving/example/export_model/add_model.py)，构造一个只有Add算子的网络，并导出MindSpore推理部署模型。
+使用[add_model.py](https://gitee.com/mindspore/mindspore/blob/r0.6/serving/example/export_model/add_model.py)，构造一个只有Add算子的网络，并导出MindSpore推理部署模型。
```python
python add_model.py
@@ -117,5 +117,5 @@ client received: RPC OK
Status status = stub_->Predict(&context, request, &reply);
```
-完整代码参考[ms_client](https://gitee.com/mindspore/mindspore/blob/master/serving/example/cpp_client/ms_client.cc)。
+完整代码参考[ms_client](https://gitee.com/mindspore/mindspore/blob/r0.6/serving/example/cpp_client/ms_client.cc)。
diff --git a/tutorials/source_zh_cn/advanced_use/summary_record.md b/tutorials/source_zh_cn/advanced_use/summary_record.md
index 9a794f9db5be924441d3e0c852db233e860302fe..58ba2c752c357ef87c8a815e83127de97ea7528e 100644
--- a/tutorials/source_zh_cn/advanced_use/summary_record.md
+++ b/tutorials/source_zh_cn/advanced_use/summary_record.md
@@ -13,8 +13,8 @@
-，本示例中仅需要使用`copy_parallel`接口。
+> 华为云MoXing提供了丰富的API供用户使用，本示例中仅需要使用`copy_parallel`接口。
1. 将OBS中存储的数据下载至执行容器。
diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md
index 365c41ea814770a800be43064eca015652ed6b06..48132ee17690d91cb8e756dbcc88ce1527862c2c 100644
--- a/tutorials/source_zh_cn/quick_start/quick_start.md
+++ b/tutorials/source_zh_cn/quick_start/quick_start.md
@@ -24,9 +24,9 @@
- 。
+> 你可以在这里找到完整可运行的样例代码： 。
这是简单、基础的应用流程，其他高级、复杂的应用可以基于这个基本流程进行扩展。
@@ -86,7 +86,7 @@
import os
```
-详细的MindSpore的模块说明，可以在[MindSpore API页面](https://www.mindspore.cn/api/zh-CN/master/index.html)中搜索查询。
+详细的MindSpore的模块说明，可以在[MindSpore API页面](https://www.mindspore.cn/api/zh-CN/r0.6/index.html)中搜索查询。
### 配置运行信息
@@ -182,7 +182,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
先进行shuffle、batch操作，再进行repeat操作，这样能保证1个epoch内数据不重复。
-> MindSpore支持进行多种数据处理和增强的操作，各种操作往往组合使用，具体可以参考[数据处理与数据增强](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/data_processing_and_augmentation.html)章节。
+> MindSpore支持进行多种数据处理和增强的操作，各种操作往往组合使用，具体可以参考[数据处理与数据增强](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/data_preparation/data_processing_and_augmentation.html)章节。
## 定义网络
diff --git a/tutorials/source_zh_cn/quick_start/quick_video.md b/tutorials/source_zh_cn/quick_start/quick_video.md
index 7d4bd9494a40e0dd005e9042cc589cf56784ed62..2abb99493dc61f773d1ae922b0afff40b00cc035 100644
--- a/tutorials/source_zh_cn/quick_start/quick_video.md
+++ b/tutorials/source_zh_cn/quick_start/quick_video.md
@@ -10,7 +10,7 @@
-
-
-**查看完整教程**：
\ No newline at end of file
+**查看完整教程**：
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_installation_and_common_commands.md b/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_installation_and_common_commands.md
index a2c264ca12d9bda779de72c198df7d6252d0d5d9..c07275089d90cfcf9d5d399074e5d67220ff68b2 100644
--- a/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_installation_and_common_commands.md
+++ b/tutorials/source_zh_cn/quick_start/quick_video/mindInsight_installation_and_common_commands.md
@@ -8,4 +8,4 @@
**立即安装**：
-**查看更多命令**：
\ No newline at end of file
+**查看更多命令**：
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/quick_start/quick_video/quick_start_video.md b/tutorials/source_zh_cn/quick_start/quick_video/quick_start_video.md
index 6fe8e0142e72689c414c7c7ac0ab1f8d40cc7835..749caf33a29ed2aaf6b3d1e22f4c718e1cd47283 100644
--- a/tutorials/source_zh_cn/quick_start/quick_video/quick_start_video.md
+++ b/tutorials/source_zh_cn/quick_start/quick_video/quick_start_video.md
@@ -6,6 +6,6 @@
-**查看代码**：
+**查看代码**：
-**查看完整教程**：
\ No newline at end of file
+**查看完整教程**：
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/quick_start/quick_video/saving_and_loading_model_parameters.md b/tutorials/source_zh_cn/quick_start/quick_video/saving_and_loading_model_parameters.md
index 6c1e3e0479c908e7c1d9bf18def9abec234ebfa1..4986b8f233f8529355ff80111f5930c7002781cf 100644
--- a/tutorials/source_zh_cn/quick_start/quick_video/saving_and_loading_model_parameters.md
+++ b/tutorials/source_zh_cn/quick_start/quick_video/saving_and_loading_model_parameters.md
@@ -6,4 +6,4 @@
-**查看完整教程**：
\ No newline at end of file
+**查看完整教程**：
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/use/custom_operator.md b/tutorials/source_zh_cn/use/custom_operator.md
index 064b271fd8daedef06cebffb2b552f8322daebd1..60c5549bee2340c872aaf78f40a954f24bc0bddd 100644
--- a/tutorials/source_zh_cn/use/custom_operator.md
+++ b/tutorials/source_zh_cn/use/custom_operator.md
@@ -14,7 +14,7 @@
-页面申请公测。
- 算子信息：描述TBE算子的基本信息，如算子名称、支持的输入输出类型等。它是后端做算子选择和映射时的依据。
-本文将以自定义Square算子为例，介绍自定义算子的步骤。更多详细内容可参考MindSpore源码中[tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe)下的用例。
+本文将以自定义Square算子为例，介绍自定义算子的步骤。更多详细内容可参考MindSpore源码中[tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/r0.6/tests/st/ops/custom_ops_tbe)下的用例。
## 注册算子原语
每个算子的原语是一个继承于`PrimitiveWithInfer`的子类，其类型名称即是算子名称。
自定义算子原语与内置算子原语的接口定义完全一致：
-- 属性由构造函数`__init__`的入参定义。本用例的算子没有属性，因此`__init__`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。
+- 属性由构造函数`__init__`的入参定义。本用例的算子没有属性，因此`__init__`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/tree/r0.6/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。
- 输入输出的名称通过`init_prim_io_names`函数定义。
- 输出Tensor的shape推理方法在`infer_shape`函数中定义，输出Tensor的dtype推理方法在`infer_dtype`函数中定义。
diff --git a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md
index 550a58f434faa07e25ad609e167c406d869d4290..f2592b45de5a7d7becf9d91837463c51eddb8860 100644
--- a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md
+++ b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md
@@ -14,9 +14,9 @@
-
+ 网络支持
custom_operator
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/use/multi_platform_inference.md b/tutorials/source_zh_cn/use/multi_platform_inference.md
index bdc75ff2480ebbeb9dc78017fcc853fcfa07fda4..2f83db740836d5aa2bbb2eac6a14c40461f112e0 100644
--- a/tutorials/source_zh_cn/use/multi_platform_inference.md
+++ b/tutorials/source_zh_cn/use/multi_platform_inference.md
@@ -18,7 +18,7 @@
-。
- > 推理样例代码：。
+ `model.eval`为模型验证接口，对应接口说明：。
+ > 推理样例代码：。
2. 使用`model.predict`接口来进行推理操作。
```python
model.predict(input_data)
```
其中，
- `model.predict`为推理接口，对应接口说明：。
+ `model.predict`为推理接口，对应接口说明：。
## Ascend 310 AI处理器上推理
@@ -74,7 +74,7 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。
Ascend 310 AI处理器上搭载了ACL框架，他支持OM格式，而OM格式需要从ONNX或者GEIR模型进行转换。所以在Ascend 310 AI处理器上推理，需要下述两个步骤：
-1. 在训练平台上生成ONNX或GEIR格式模型，具体步骤请参考[模型导出-导出GEIR模型和ONNX模型](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html#geironnx)。
+1. 在训练平台上生成ONNX或GEIR格式模型，具体步骤请参考[模型导出-导出GEIR模型和ONNX模型](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/saving_and_loading_model_parameters.html#geironnx)。
2. 将ONNX/GEIR格式模型文件，转化为OM格式模型，并进行推理。
- 云上（ModelArt环境），请参考[Ascend910训练和Ascend310推理的样例](https://support.huaweicloud.com/bestpractice-modelarts/modelarts_10_0026.html)完成推理操作。
@@ -88,7 +88,7 @@ Ascend 310 AI处理器上搭载了ACL框架，他支持OM格式，而OM格式需
### 使用ONNX格式文件推理
-1. 在训练平台上生成ONNX格式模型，具体步骤请参考[模型导出-导出GEIR模型和ONNX模型](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html#geironnx)。
+1. 在训练平台上生成ONNX格式模型，具体步骤请参考[模型导出-导出GEIR模型和ONNX模型](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/saving_and_loading_model_parameters.html#geironnx)。
2. 在GPU上进行推理，具体可以参考推理使用runtime/SDK的文档。如在Nvidia GPU上进行推理，使用常用的TensorRT，可参考[TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt)。
@@ -100,10 +100,10 @@ Ascend 310 AI处理器上搭载了ACL框架，他支持OM格式，而OM格式需
### 使用ONNX格式文件推理
与在GPU上进行推理类似，需要以下几个步骤：
-1. 在训练平台上生成ONNX格式模型，具体步骤请参考[模型导出-导出GEIR模型和ONNX模型](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html#geironnx)。
+1. 在训练平台上生成ONNX格式模型，具体步骤请参考[模型导出-导出GEIR模型和ONNX模型](https://www.mindspore.cn/tutorial/zh-CN/r0.6/use/saving_and_loading_model_parameters.html#geironnx)。
2. 在CPU上进行推理，具体可以参考推理使用runtime/SDK的文档。如使用ONNX Runtime，可以参考[ONNX Runtime说明文档](https://github.com/microsoft/onnxruntime)。
## 端侧推理
-端侧推理需使用MindSpore Predict推理引擎，详细操作请参考[端侧推理教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/on_device_inference.html)。
\ No newline at end of file
+端侧推理需使用MindSpore Predict推理引擎，详细操作请参考[端侧推理教程](https://www.mindspore.cn/tutorial/zh-CN/r0.6/advanced_use/on_device_inference.html)。
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md
index 7ebf822df2e4333984497e0a1e3dba9f32fea5e7..a0afe32b89084815bd4cb7ad1db9c8b174fb0d19 100644
--- a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md
+++ b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md
@@ -13,7 +13,7 @@
- 
+![\"LeNet5\"](\"https://www.mindspore.cn/tutorial/zh-CN/master/_images/LeNet_5.jpg\")
"
+ "![\"LeNet5\"](\"https://www.mindspore.cn/tutorial/zh-CN/r0.6/_images/LeNet_5.jpg\")
"
]
},
{
diff --git a/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md b/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md
index b30c0fe5a900e0021e8a21b40930e8f6eaa4ca90..92d2bd3456e55528be29decb72ffd3bb659cd6b4 100644
--- a/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md
+++ b/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md
@@ -313,7 +313,7 @@ User process:
3. Execute stage 2 training: There are two devices in stage 2 training environment. The weight shape of the MatMul operator on each device is \[4, 8]. Load the initialized model parameter data from the integrated checkpoint file and then perform training.
-> For details about the distributed environment configuration and training code, see [Distributed Training](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+> For details about the distributed environment configuration and training code, see [Distributed Training](https://www.mindspore.cn/tutorial/en/r0.6/advanced_use/distributed_training.html).
>
> This document provides the example code for integrating checkpoint files and loading checkpoint files before distributed training. The code is for reference only.
diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md
index 80ea21413c44ee9eeb48ab5897121f1b6a97c447..2090b0296617275711ca85df286936c11c33fafc 100644
--- a/tutorials/source_en/advanced_use/computer_vision_application.md
+++ b/tutorials/source_en/advanced_use/computer_vision_application.md
@@ -16,7 +16,7 @@
-
+
@@ -34,7 +34,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
@@ -58,7 +58,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
@@ -82,7 +82,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
@@ -115,7 +115,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
@@ -139,7 +139,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
@@ -163,7 +163,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
@@ -187,7 +187,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi
diff --git a/tutorials/source_en/quick_start/quick_video/customized_debugging.md b/tutorials/source_en/quick_start/quick_video/customized_debugging.md
index 94d0078ca6df2521d6214509257df6ab2676339d..2570db76c96463007c7d9c3231b9b905b696e0f2 100644
--- a/tutorials/source_en/quick_start/quick_video/customized_debugging.md
+++ b/tutorials/source_en/quick_start/quick_video/customized_debugging.md
@@ -6,4 +6,4 @@
+
@@ -34,7 +34,7 @@
@@ -58,7 +58,7 @@
@@ -82,7 +82,7 @@
@@ -115,7 +115,7 @@
@@ -139,7 +139,7 @@
@@ -163,7 +163,7 @@
@@ -187,7 +187,7 @@
diff --git a/tutorials/source_zh_cn/quick_start/quick_video/customized_debugging.md b/tutorials/source_zh_cn/quick_start/quick_video/customized_debugging.md
index 56b77994279a5a1fd6fbe6f27db99901a0df1784..ffa2a4b41c00023d38bc9cc75d2512d36c988d86 100644
--- a/tutorials/source_zh_cn/quick_start/quick_video/customized_debugging.md
+++ b/tutorials/source_zh_cn/quick_start/quick_video/customized_debugging.md
@@ -6,4 +6,4 @@
