!706 add some depicts

Merge pull request !706 from caozhou/add_depict

tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md

@@ -31,7 +31,7 @@ In the MindSpore model parallel scenario, each instance process stores only the
 
 In the auto parallel scenario, MindSpore automatically generates the dividing strategy. The MindSpore checkpoint module supports automatic integrating, saving, and loading.
 
-In the hybrid parallel scenario, the dividing strategy is implemented by users. MindSpore saves only the data corresponding to each node. Users need to integrate, save, and load the checkpoint files by themselves. This tutorial describes how to integrate, save, and load checkpoint files in the hybrid parallel scenario.
+In the hybrid parallel scenario, the dividing strategy is implemented by users. MindSpore saves the slice strategy of model, which is the same on each node, and the data corresponding to each node is stored respectively. Users need to integrate, save, and load the checkpoint files by themselves. This tutorial describes how to integrate, save, and load checkpoint files in the hybrid parallel scenario.
 
 ### Application Scenario
 
@@ -41,7 +41,7 @@ Scenario 1: multi-device training and single-device inference
 
 The following describes the overall process of training on 64 devices and inference on a single device:
 
-1. Execute the training to automatically generate the checkpoint files.
+1. Execute the training to automatically generate the checkpoint files and the slice strategy files.
 
 2. Integrate the saved checkpoint files.
 
@@ -55,7 +55,7 @@ Scenario 2: The training is divided into multiple stages, and the cluster size i
 
 For example, in the training stage 1, the training environment with 64 devices is used, and in the training stage 2, the training environment with 56 devices is used. The overall operation process is as follows:
 
-1. Execute the training in stage 1 to automatically generate the checkpoint files.
+1. Execute the training in stage 1 to automatically generate the checkpoint files and the slice strategy files.
 
 2. Integrate the saved checkpoint files.
 
@@ -116,7 +116,7 @@ strategy = build_searched_strategy("./strategy_train.ckpt")
 
 In the preceding information:
 
-- `strategy_train.ckpt`: name of model slice strategy, set by users calling `set_auto_parallel_context` API and customizing `strategy_ckpt_save_file` parameter before training network, and the file saved on each node are the same.
+- `strategy_train.ckpt`: name of model slice strategy, set by users calling `set_auto_parallel_context` API and customizing `strategy_ckpt_save_file` parameter before training network.
 
 ### Integrate the Model Parallel Parameters
 

tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md

@@ -38,7 +38,7 @@ MindSpore模型并行场景下，每个实例进程只保存有本节点对应
 
 对于自动切分的模型并行场景（Auto Parallel），切分逻辑由MindSpore自动生成，MindSpore的CheckPoint模块可以支持自动合并保存和基于合并保存的加载能力。
 
-对于用户手动设置的并行场景（HyBrid Parallel），切分逻辑由用户自己实现，MindSpore在每个节点只保存本节点上的数据，用户需要自己实现CheckPoint文件的合并保存与加载功能。本教程用于指导用户在手动切分场景下，实现CheckPoint的合并保存与加载能力。
+对于用户手动设置的并行场景（HyBrid Parallel），切分逻辑由用户自己实现，MindSpore在每个节点上保存相同的模型参数切分策略文件和本节点上的数据，用户需要自己实现CheckPoint文件的合并保存与加载功能。本教程用于指导用户在手动切分场景下，实现CheckPoint的合并保存与加载能力。
 
 ### 使用场景
 
@@ -48,7 +48,7 @@ MindSpore模型并行场景下，每个实例进程只保存有本节点对应
 
  以在64卡上训练，并在单卡上推理为例，整体操作流程如下：
 
-1. 执行训练，自动生成CheckPoint文件。
+1. 执行训练，自动生成CheckPoint文件和模型参数切分策略文件。
 
 2. 用户对保存的CheckPoint文件做合并处理。
 
@@ -62,7 +62,7 @@ MindSpore模型并行场景下，每个实例进程只保存有本节点对应
 
 ​ 以训练阶段一是64卡训练环境，阶段二是56卡训练环境为例，整体操作流程如下：
 
-1. 执行阶段一训练，自动生成CheckPoint文件。
+1. 执行阶段一训练，自动生成CheckPoint文件和模型参数切分策略文件。
 
 2. 用户对保存的CheckPoint文件做合并处理。
 
@@ -124,7 +124,7 @@ strategy = build_searched_strategy("./strategy_train.cpkt")
 
 其中，
 
-- `strategy_train.ckpt`：保存的模型参数切分策略文件名称，训练网络之前由用户调用`set_auto_parallel_context`接口自定义`strategy_ckpt_save_file`参数生成，各个节点上保存的策略文件相同。
+- `strategy_train.ckpt`：保存的模型参数切分策略文件名称，训练网络之前由用户调用`set_auto_parallel_context`接口自定义`strategy_ckpt_save_file`参数生成。
 
 ### 对模型并行的参数做合并处理