diff --git a/docs/federated/api/requirements.txt b/docs/federated/api/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/federated/api/requirements.txt
+++ b/docs/federated/api/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/federated/docs/requirements.txt b/docs/federated/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/federated/docs/requirements.txt
+++ b/docs/federated/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/federated/docs/source_en/conf.py b/docs/federated/docs/source_en/conf.py
index 80c8268cfa9b1812e047351a7e53870faaee98fd..d095c00a749e48ba243f650eaa74a176f7bb3669 100644
--- a/docs/federated/docs/source_en/conf.py
+++ b/docs/federated/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindspore
@@ -109,20 +108,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/federated/docs/source_en/index.rst b/docs/federated/docs/source_en/index.rst
index ee0024ff99784e1a0193e7021188d8d65457b297..d3b85168165ebf9635bd56ac355c3008d0235172 100644
--- a/docs/federated/docs/source_en/index.rst
+++ b/docs/federated/docs/source_en/index.rst
@@ -10,8 +10,9 @@ MindSpore Federated is an open-source federated learning framework that supports
 
 The federated learning is an encrypted distributed machine learning technology that allows users participating in federated learning to build AI models without sharing local data. MindSpore Federated preferentially focuses on the large-scale participants in the horizontal federated learning scenarios.
 
-.. image:: ./images/federated_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/federated/docs/source_en/images/federated_en.png" width="700px" alt="" >
 
 Advantages of the MindSpore Federated
 ---------------------------------------
@@ -85,7 +86,7 @@ Common Scenarios
    :maxdepth: 1
    :caption: Security and Privacy
 
-   local_differential_privacy_training
+   local_differential_privacy_training_noise
    pairwise_encryption_training
 
 .. toctree::
diff --git a/docs/federated/docs/source_en/local_differential_privacy_training.md b/docs/federated/docs/source_en/local_differential_privacy_training_noise.md
similarity index 94%
rename from docs/federated/docs/source_en/local_differential_privacy_training.md
rename to docs/federated/docs/source_en/local_differential_privacy_training_noise.md
index 1627c7a5a0d160e56a17c5e064c1fe63b9cc92e5..cef452ff9e1cb31a8a99a9a60a9df46094c8b272 100644
--- a/docs/federated/docs/source_en/local_differential_privacy_training.md
+++ b/docs/federated/docs/source_en/local_differential_privacy_training_noise.md
@@ -1,6 +1,6 @@
-# Differential privacy training for privacy protection
+# Local differential privacy perturbation training
 
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/local_differential_privacy_training.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/local_differential_privacy_training_noise.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
 
 During federated learning, user data is used only for local device training and does not need to be uploaded to the central server. This prevents personal data leakage.
 However, in the conventional federated learning framework, models are migrated to the cloud in plaintext. There is still a risk of indirect disclosure of user privacy.
diff --git a/docs/federated/docs/source_en/pairwise_encryption_training.md b/docs/federated/docs/source_en/pairwise_encryption_training.md
index fec9656ee1a0311e02ceae8700649a26fd02caba..1ecf816e6fcdb394639761cffc4cdf0afb827b14 100644
--- a/docs/federated/docs/source_en/pairwise_encryption_training.md
+++ b/docs/federated/docs/source_en/pairwise_encryption_training.md
@@ -1,4 +1,4 @@
-# Pairwise encryption training for privacy protection
+# Pairwise encryption training
 
 <a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_en/pairwise_encryption_training.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
 
diff --git a/docs/federated/docs/source_zh_cn/conf.py b/docs/federated/docs/source_zh_cn/conf.py
index 09ce2c7f0b8ed1392e5e83bf25b75069399c1cdf..95fd913fde15e8cd07439f5f3ea3131b33675ec4 100644
--- a/docs/federated/docs/source_zh_cn/conf.py
+++ b/docs/federated/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 # -- Project information -----------------------------------------------------
@@ -107,20 +106,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/federated/docs/source_zh_cn/index.rst b/docs/federated/docs/source_zh_cn/index.rst
index 22abee5fab1a368e11dbefdea595c225d97ab495..a437f33f89176df01cdab29372e9c0d1b59adac4 100644
--- a/docs/federated/docs/source_zh_cn/index.rst
+++ b/docs/federated/docs/source_zh_cn/index.rst
@@ -10,8 +10,9 @@ MindSpore Federated是一款开源联邦学习框架，支持千万级无状态
 
 联邦学习是一种加密的分布式机器学习技术，它是指参与联邦学习的各用户在不共享本地数据的前提下共建AI模型。MindSpore Federated优先专注于大规模参与方的横向联邦的应用场景。
 
-.. image:: ./images/federated_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/federated/docs/source_zh_cn/images/federated_cn.png" width="700px" alt="" >
 
 使用MindSpore Federated框架的优势
 ----------------------------------
@@ -87,7 +88,8 @@ MindSpore Federated是一款开源联邦学习框架，支持千万级无状态
    :maxdepth: 1
    :caption: 安全和隐私
 
-   local_differential_privacy_training
+   local_differential_privacy_training_noise
+   local_differential_privacy_training_signds
    pairwise_encryption_training
 
 .. toctree::
diff --git a/docs/federated/docs/source_zh_cn/local_differential_privacy_training.md b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md
similarity index 93%
rename from docs/federated/docs/source_zh_cn/local_differential_privacy_training.md
rename to docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md
index 5b77a1f7d18afb59d1a5cad5db97b6b60c661fe4..fc6afe04d5182926dac152e40cdb3afc710dd65d 100644
--- a/docs/federated/docs/source_zh_cn/local_differential_privacy_training.md
+++ b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md
@@ -1,6 +1,6 @@
-# 隐私安全保护算法之差分隐私训练
+# 局部差分隐私加噪训练
 
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/local_differential_privacy_training.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/local_differential_privacy_training_noise.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
 
 联邦学习过程中，用户数据仅用于本地设备训练，不需要上传至中心服务器，可以避免用户个人数据的直接泄露。然而传统联邦学习框架中，模型以明文形式上云，仍然存在间接泄露用户隐私的风险。敌手获取到用户上传的明文模型后，可以通过重构、模型逆向等攻击恢复用户的个人训练数据，导致用户隐私泄露。
 
diff --git a/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md
new file mode 100644
index 0000000000000000000000000000000000000000..53a39a97dbe6cd30af37e2f06712fee8451c9a68
--- /dev/null
+++ b/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md
@@ -0,0 +1,145 @@
+# 局部差分隐私SignDS训练
+
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/local_differential_privacy_training_signds.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
+
+## 隐私保护背景
+
+联邦学习通过让参与方只上传本地训练后的新模型或更新模型的update信息，实现了client用户不上传原始数据集就能参与全局模型训练的目的，打通了数据孤岛。这种普通场景的联邦学习对应MindSpore联邦学习框架中的默认方案（[云侧部署](https://www.mindspore.cn/federated/docs/zh-CN/master/deploy_federated_server.html#id5)启动`server`时，`encrypt_type`开关默认为`not_encrypt`，联邦学习教程中的`安装部署`与`应用实践`都默认使用这种方式），是没有任何加密扰动等保护隐私处理的普通联邦求均方案，为方便描述，下文以`not_encrypt`来特指这种默认方案。
+
+这种联邦学习方案并不是毫无隐私泄漏的，使用上述`not_encrypt`方案进行训练，服务端server收到用户client的训练后模型，仍可通过一些攻击方法[1]重构用户训练数据，从而泄露用户隐私，所以`not_encrypt`方案需要进一步进行用户隐私保护。
+
+联邦学习每轮client接收的当前模型`oldModel`都是server下发的，不涉及到用户隐私问题。但每个client本地跑若干epoch后得到的新模型`newModel`拟合了该用户本地隐私数据，所以更具体的是要保护权重差值`newModel`-`oldModel`=`update`的隐私。
+
+MindSpore联邦学习框架中已实现的`DP_ENCRYPT`差分噪声方案通过在用户`update`加高斯随机噪声进行扰动，从而保护隐私。但随着模型维度增大，`update`范数增大会使噪声增大，同时需要较多的client用户参与同一轮聚合去中和噪声，否则模型收敛性和精度会降低。如果设置的噪声过小，虽然收敛性和精度与`not_encrypt`方案性能接近，但隐私保护力度不够。同时每个client用户都需要发送扰动后的模型，随着模型增大，通信开销也要随之增大。我们希望端侧手机用户尽可能少的去通信就可实现全局模型的收敛。
+
+## 算法流程介绍
+
+SignDS[2]是Sign Dimension Select的缩写，处理对象是client用户的`update`。准备工作：把`update`的每一层Tensor拉平展开成一维向量，连接在一起，拼接向量维度数量记为$d$。
+
+一句话概括算法：**选择`update`的$h(h<d)$个维度，用sign值（符号值：正负1）代替这些维度原始的update值，未选中的用0代替。**
+
+下面举例来说明：现有3个client用户1，2，3，其`update`拉平展开后为$d=8$维向量，server计算这3个client用户的`avg`，并用该值更新全局模型，即完成一轮联邦学习。
+
+| Client | d_1  | d_2  | d_3  | d_4  | d_5  | d_6  | d_7  |  d_8  |
+| :----: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :---: |
+|   1    | 0.4  | 0.1  | -0.2 | 0.3  | 0.5  | 0.1  | -0.2 | -0.3  |
+|   2    | 0.5  | 0.2  |  0   | 0.1  | 0.3  | 0.2  | -0.1 | -0.2  |
+|   3    | 0.3  | 0.1  | -0.1 | 0.5  | 0.2  | 0.3  |  0   |  0.1  |
+|  avg   | 0.4  | 0.13 | -0.1 | 0.3  | 0.33 | 0.2  | -0.1 | -0.13 |
+
+选择维度要挑重要的维度，重要性衡量是**值的大小**，需要对update进行排序。真实update值有正负，代表不同的更新方向，故每轮client用户的sign值各有**0.5的概率**取`1`或`-1`。如果sign=1，那么就把最大的$k$个`update`维度记为`topk`集合，剩余的记为`non-topk`集合；如果sign=-1，则把最小的$k$个记为`topk`集合。
+
+如果server指定总共选择的维度数量`h`，client用户会直接使用该值，否则每个client用户会本地会计算出最优的输出维度`h`。
+
+随后SignDS算法会输出应该从`topk`集合中挑多少维（数量记为$v$），从`non-topk`中挑多少维，下表格示例中，两个集合总共挑选维度$h=3$，
+
+Client用户按照算法输出的指定维度去均匀随机挑选维度，将维度序号和sign值发到server即可，维度序号如果按照先从`topk`挑，再从`non-topk`挑的顺序输出，则需要对维度序号列表`index`进行洗牌打乱的操作，下表为该算法每个client用户最终要传到server端的信息：
+
+| Client | index | sign |
+| :----: | :---: | :--: |
+|   1    | 1,5,8 |  1   |
+|   2    | 2,3,4 |  -1  |
+|   3    | 3,6,7 |  1   |
+
+server根据每个client用户的维度和sign值构建隐私保护的`update`，对所有`update`进行聚合平均并更新当前`oldModel`即完成一轮联邦学习。
+
+| Client |  d_1  |  d_2   |  d_3   |  d_4   |  d_5  |  d_6  |  d_7  |  d_8  |
+| :----: | :---: | :----: | :----: | :----: | :---: | :---: | :---: | :---: |
+|   1    | **1** |   0    |   0    |   0    | **1** |   0   |   0   | **1** |
+|   2    |   0   | **-1** | **-1** | **-1** |   0   |   0   |   0   |   0   |
+|   3    |   0   |   0    | **1**  |   0    |   0   | **1** | **1** |   0   |
+|  avg   |  1/3  |  -1/3  |   0    |  -1/3  |  1/3  |  1/3  |  1/3  |  1/3  |
+
+由于MindSpore联邦框架目前支持client用户发送**完整模型**到server进行聚合求均，所以client用户在挑选维度之后要本地重构出隐私保护的`update`，并乘上全局学习率`Global lr`，用于模型更新。最终更新后模型会返回server进行聚合平均。
+
+## 隐私保护证明
+
+差分隐私噪声方案通过加噪的方式，让攻击者无法确定原始信息从而实现隐私保护效果；而差分隐私SignDS方案只激活部分维度，且用sign值代替原始值，很大程度上保护了用户隐私。进一步的，利用差分隐私指数机制让攻击者无法确认激活的维度是否是重要（来自`topk`集合），且无法确认输出维度中来自`topk`的维度数量是否超过给定阈值。
+
+对于每个client用户的任意两个update $\Delta$ 和 $\Delta'$  ，它们的`topk`维度集合分别是  $S_{topk}$ ， ${S'}_{topk}$ ，该算法任意可能的输出维度集合是 ${J}\in {\mathcal{J}} $ ，记 $\nu=|{S}_{topk}\cap {J}|$ ,  $\nu'=|{S'}_{topk}\cap {J}|$  是 ${J}$ 和`topk` 集合交集的数量，算法使得以下不等式成立：
+
+$$
+\frac{{Pr}[{J}|\Delta]}{{Pr}[{J}|\Delta']}=\frac{{Pr}[{J}|{S}_{topk}]}{{Pr}[{J}|{S'}_{topk}]}=\frac{\frac{{exp}(\frac{\epsilon}{\phi_u}\cdot u({S}_{topk},{J}))}{\sum_{{J'}\in {\mathcal{J}}}{exp}(\frac{\epsilon}{\phi_u}\cdot u({S}_{topk}, {J'}))}}{\frac{{exp}(\frac{\epsilon}{\phi_u}\cdot u({S'}_{topk}, {J}))}{\sum_{ {J'}\in {\mathcal{J}}}{exp}(\frac{\epsilon}{\phi_u}\cdot u( {S'}_{topk},{J'}))}}=\frac{\frac{{exp}(\epsilon\cdot \unicode{x1D7D9}(\nu \geq \nu_{th}))}{\sum_{\tau=0}^{\tau=\nu_{th}-1}\omega_{\tau} + \sum_{\tau=\nu_{th}}^{\tau=h}\omega_{\tau}\cdot {exp}(\epsilon)}}{\frac{ {exp}(\epsilon\cdot \unicode{x1D7D9}(\nu' \geq\nu_{th}))}{\sum_{\tau=0}^{\tau=\nu_{th}-1}\omega_{\tau}+\sum_{\tau=\nu_{th}}^{\tau=h}\omega_{\tau}\cdot {exp}(\epsilon)}}\\= \frac{{exp}(\epsilon\cdot \unicode{x1D7D9} (\nu \geq \nu_{th}))}{ {exp}(\epsilon\cdot \unicode{x1D7D9} (\nu' \geq \nu_{th}))} \leq \frac{{exp}(\epsilon\cdot 1)}{{exp}(\epsilon\cdot 0)} = {exp}(\epsilon),
+$$
+
+证明该算法满足局部差分隐私。
+
+## 准备工作
+
+若要使用该算法，首先需要成功完成任一端云联邦场景的训练聚合过程，[实现一个端云联邦的图像分类应用(x86)](https://www.mindspore.cn/federated/docs/zh-CN/master/image_classification_application.html)和[实现一个情感分类应用(Android)](https://www.mindspore.cn/federated/docs/zh-CN/master/sentiment_classification_application.html)都详细介绍了包括数据集和网络模型等准备工作和模拟启动多客户端参与联邦学习的流程。
+
+## 算法开启脚本
+
+本地差分隐私SignDS训练目前只支持端云联邦学习场景。开启方式需要在启动云侧服务时，使用`context.set_fl_context()`设置`encrypt_type='SIGNDS'`即可，云侧完整启动脚本可参考云侧部署的[run_mobile_server.py脚本](https://gitee.com/mindspore/mindspore/blob/master/tests/st/fl/mobile/run_mobile_server.py)，这里给出启动该算法的相关参数配置。以LeNet任务为例，在确定参数配置后，用户需要在执行训练前调用`set_fl_context`接口，传入算法参数，调用方式如下：
+
+```python
+...
+# 打开开关
+parser.add_argument("--encrypt_type", type=str, default="SIGNDS")
+# SIGNDS方案的参数设计
+parser.add_argument("--sign_k", type=float, default=0.01)
+parser.add_argument("--sign_eps", type=float, default=100)
+parser.add_argument("--sign_thr_ratio", type=float, default=0.6)
+parser.add_argument("--sign_global_lr", type=float, default=5)
+parser.add_argument("--sign_dim_out", type=int, default=0)
+...
+sign_k = args.sign_k
+sign_eps = args.sign_eps
+sign_thr_ratio = args.sign_thr_ratio
+sign_global_lr = args.sign_global_lr
+sign_dim_out = args.sign_dim_out
+ctx = {
+    ...
+    "sign_k": sign_k,
+    "sign_eps": sign_eps,
+    "sign_thr_ratio": sign_thr_ratio,
+    "sign_global_lr": sign_global_lr,
+    "sign_dim_out": sign_dim_out
+}
+context.set_fl_context(**fl_ctx)
+...
+```
+
+以下是部分关键参数传入server脚本的示例：
+
+```python
+...
+# 打开开关
+parser.add_argument("--encrypt_type", type=str, default="SIGNDS")
+# SIGNDS方案的参数设计
+parser.add_argument("--sign_k", type=float, default=0.01)
+parser.add_argument("--sign_eps", type=float, default=100)
+parser.add_argument("--sign_thr_ratio", type=float, default=0.6)
+parser.add_argument("--sign_global_lr", type=float, default=5)
+parser.add_argument("--sign_dim_out", type=int, default=0)
+if __name__ == "__main__":
+    ...
+    sign_k = args.sign_k
+    sign_eps = args.sign_eps
+    sign_thr_ratio = args.sign_thr_ratio
+    sign_global_lr = args.sign_global_lr
+    sign_dim_out = args.sign_dim_out
+    ...
+    for i in range(local_server_num):
+        ...
+        cmd_server += " --sign_k=" + str(sign_k)
+        cmd_server += " --sign_eps=" + str(sign_eps)
+        cmd_server += " --sign_thr_ratio=" + str(sign_thr_ratio)
+        cmd_server += " --sign_global_lr=" + str(sign_global_lr)
+        cmd_server += " --sign_dim_out=" + str(sign_dim_out)
+        ...
+```
+
+云侧代码实现给出了各个参数的定义域，不在定义域内的，server会报错提示定义域。以下参数改动的前提是保持其余4个参数不变：
+
+- `sign_k`：(0,0.25]，k*inputDim>50. default=0.01，`inputDim`是模型或update的拉平长度，若不满足，端侧警告。排序update，占比前k（%）的组成`topk`集合。减少k，则意味着要从更重要的维度中以较大概率挑选，输出的维度会减少，但维度更重要，无法确定收敛性的变化，用户需观察模型update稀疏度来确定该值，当比较稀疏时（update有很多0），则应取小一点。
+- `sign_eps`：(0,100]，default=100。隐私保护预算，数序符号为$\epsilon$，简写为eps。eps减少，挑选不重要的维度概率会增大，隐私保护力度增强，输出维度减少，占比不变，精度降低。
+- `sign_thr_ratio`：[0.5,1]，default=0.6。激活的维度中来自`topk`的维度占比阈值下界。增大会减少输出维度，但输出维度中来自`topk`的占比会增加，当过度增大该值，要求输出中更多的来自`topk`，为了满足要求只能减少总的输出维度，当client用户数量不够多时，精度下降。
+- `sign_global_lr`：(0,)，default=1。该值乘上sign来代替update，直接影响收敛快慢与精度，适度增大该值会提高收敛速度，但有可能让模型震荡，梯度爆炸。
+- `sign_dim_out`：[0,50]，default=0。若给出非0值，client端直接使用该值，增大该值输出的维度增多，但来自`topk`的维度占比会减少；若为0，client用户要计算出最优的输出参数。eps不够大时，若增大该值，则会输出很多`non-topk`的不重要维度导致影响模型收敛，精度下降；当eps足够大时，增大该值会让更多的用户重要的维度信息离开本地，精度提升。
+
+## 参考文献
+
+[1] Ligeng Zhu, Zhijian Liu, and Song Han. [Deep Leakage from Gradients](http://arxiv.org/pdf/1906.08935.pdf). NeurIPS, 2019.
+
+[2] Xue Jiang, Xuebing Zhou, and Jens Grossklags. "SignDS-FL: Local Differentially-Private Federated Learning with Sign-based Dimension Selection." ACM Transactions on Intelligent Systems and Technology, 2022.
\ No newline at end of file
diff --git a/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md b/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md
index a54753cd29fe8c3f2b9dabc12aa25cb5b85bc769..31225058df648885e9bd7f5cdf84dc66bbf49cf5 100644
--- a/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md
+++ b/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md
@@ -1,4 +1,4 @@
-# 隐私安全保护算法之安全聚合训练
+# 安全聚合训练
 
 <a href="https://gitee.com/mindspore/docs/blob/master/docs/federated/docs/source_zh_cn/pairwise_encryption_training.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
 
diff --git a/docs/federated/faq/requirements.txt b/docs/federated/faq/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/federated/faq/requirements.txt
+++ b/docs/federated/faq/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/graphlearning/docs/requirements.txt b/docs/graphlearning/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/graphlearning/docs/requirements.txt
+++ b/docs/graphlearning/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/graphlearning/docs/source_en/conf.py b/docs/graphlearning/docs/source_en/conf.py
index ea232089d6808b699e5409f29b2724363c849cfd..646ff5ff34d5b2b11eb302986d69f1dc96f72701 100644
--- a/docs/graphlearning/docs/source_en/conf.py
+++ b/docs/graphlearning/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindspore_gl
@@ -110,20 +109,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/graphlearning/docs/source_en/images/MindSpore_GraphLearning_architecture.PNG b/docs/graphlearning/docs/source_en/images/MindSpore_GraphLearning_architecture.PNG
index 54b3fcc7be96ec6a95a7245f0d93a3e2d719f0a4..9473d28a237f1b43c9aa33df14afcd8bbe39b4a8 100644
Binary files a/docs/graphlearning/docs/source_en/images/MindSpore_GraphLearning_architecture.PNG and b/docs/graphlearning/docs/source_en/images/MindSpore_GraphLearning_architecture.PNG differ
diff --git a/docs/graphlearning/docs/source_en/images/graphlearning_en.png b/docs/graphlearning/docs/source_en/images/graphlearning_en.png
index 54b3fcc7be96ec6a95a7245f0d93a3e2d719f0a4..9473d28a237f1b43c9aa33df14afcd8bbe39b4a8 100644
Binary files a/docs/graphlearning/docs/source_en/images/graphlearning_en.png and b/docs/graphlearning/docs/source_en/images/graphlearning_en.png differ
diff --git a/docs/graphlearning/docs/source_en/index.rst b/docs/graphlearning/docs/source_en/index.rst
index d0ca168bdb1eab16edbb9ee9dfb5069e5fda4d6c..77a3f9829ec7c270ae341cadaff3aae99bfd47c4 100644
--- a/docs/graphlearning/docs/source_en/index.rst
+++ b/docs/graphlearning/docs/source_en/index.rst
@@ -10,11 +10,11 @@ Design Features
 ----------------
 
 1. Point-centric programming paradigm
-   
+
    A graph neural network model transfers and aggregates information on a given graph structure, which cannot be intuitively expressed through entire graph computing. MindSpore Graph Learning provides a point-centric programming paradigm that better complies with the graph learning algorithm logic and Python language style. It can directly translate formulas into code, reducing the gap between algorithm design and implementation.
 
 2. Accelerated graph models
-   
+
    MindSpore Graph Learning combines the features of MindSpore graph kernel fusion and auto kernel generator (AKG) to automatically identify the specific execution pattern of graph neural network tasks for fusion and kernel-level optimization, covering the fusion of existing operators and new combined operators in the existing framework. The performance is improved by 3 to 4 times compared with that of the existing popular frameworks.
 
 Future Roadmap
@@ -26,7 +26,7 @@ Typical MindSpore Graph Learning Application Scenarios
 -------------------------------------------------------
 
 1. Graph convolutional neural network (GCN) for entire graph training
-   
+
    Entire graph training requires that the graph nodes and edge features of the user be stored in the GPU. The GCN is a type of graph neural network that uses convolution operations.
 
 2. Graph isomorphism network (GIN) for graph sampling training
@@ -50,9 +50,7 @@ Typical MindSpore Graph Learning Application Scenarios
    :maxdepth: 1
    :caption: API References
 
-   mindspore_gl.dataloader
    mindspore_gl.dataset
-   mindspore_gl.graph
    mindspore_gl.nn
    mindspore_gl.parser
    mindspore_gl.sampling
diff --git a/docs/graphlearning/docs/source_en/mindspore_gl.dataloader.rst b/docs/graphlearning/docs/source_en/mindspore_gl.dataloader.rst
deleted file mode 100644
index 6bdee18a4e79291504b4c78f3c9877e868eebd96..0000000000000000000000000000000000000000
--- a/docs/graphlearning/docs/source_en/mindspore_gl.dataloader.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-mindspore_gl.dataloader
-=======================
-
-.. automodule:: mindspore_gl.dataloader
-    :members:
diff --git a/docs/graphlearning/docs/source_en/mindspore_gl.graph.rst b/docs/graphlearning/docs/source_en/mindspore_gl.graph.rst
deleted file mode 100644
index 8e49753aa36bc791891aa55845ab9e8c4dbf8aa2..0000000000000000000000000000000000000000
--- a/docs/graphlearning/docs/source_en/mindspore_gl.graph.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-mindspore_gl.graph
-===================
-
-.. automodule:: mindspore_gl.graph
-    :members:
diff --git a/docs/graphlearning/docs/source_en/random_sampling_training_of_gin.md b/docs/graphlearning/docs/source_en/random_sampling_training_of_gin.md
index 8c81f36b917f0d156f538381b277021e47428bb5..366b51ecc8e6f82ef7adb2a671e03f1407befdc1 100644
--- a/docs/graphlearning/docs/source_en/random_sampling_training_of_gin.md
+++ b/docs/graphlearning/docs/source_en/random_sampling_training_of_gin.md
@@ -13,7 +13,7 @@ MindSpore Graph Learning also provides graph sampling APIs and supports random b
 3. Generate batch graph data. The following provides an example of constructing graph sampling data.
 4. Train and validate the graph sampling network.
 
-The following shows how to use MindSpore Graph Learning to implement GIN random batch training with the IMDBinary dataset. For details about the complete code, see <https://gitee.com/mindspore/graphlearning/blob/master/examples/vc_gin.py>.
+The following shows how to use MindSpore Graph Learning to implement GIN random batch training with the IMDBinary dataset. For details about the complete code, see <https://gitee.com/mindspore/graphlearning/tree/research/examples/vc_gin.py>.
 
 ## GIN Principles
 
@@ -504,4 +504,4 @@ Epoch 40, Train loss 0.5511463, Test acc 0.714
 
 Accuracy validated on IMDB-BINARY: 0.734 (paper: 0.75)
 
-This is the usage guide of random batch training by graph sampling. For more examples, see [examples](<https://gitee.com/mindspore/graphlearning/tree/master/examples>).
+This is the usage guide of random batch training by graph sampling. For more examples, see [examples](<https://gitee.com/mindspore/graphlearning/tree/research/examples>).
diff --git a/docs/graphlearning/docs/source_zh_cn/conf.py b/docs/graphlearning/docs/source_zh_cn/conf.py
index cf67ef7d97ff3008050252209bbacb6d2ff8a068..6f1697aada7a2bb8a4e40cb08f613d761b5aa61d 100644
--- a/docs/graphlearning/docs/source_zh_cn/conf.py
+++ b/docs/graphlearning/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindspore_gl
@@ -114,20 +113,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/graphlearning/docs/source_zh_cn/index.rst b/docs/graphlearning/docs/source_zh_cn/index.rst
index ae206d9192a23d0e3755d88ccfd5bc7ce123c913..f28651db94252af679299ecc4436d438bb734c8d 100644
--- a/docs/graphlearning/docs/source_zh_cn/index.rst
+++ b/docs/graphlearning/docs/source_zh_cn/index.rst
@@ -50,9 +50,7 @@ MindSpore Graph Learning初始版本包含以点为中心的编程范式，并
    :maxdepth: 1
    :caption: API参考
 
-   mindspore_gl.dataloader
    mindspore_gl.dataset
-   mindspore_gl.graph
    mindspore_gl.nn
    mindspore_gl.parser
    mindspore_gl.sampling
diff --git a/docs/graphlearning/docs/source_zh_cn/mindspore_gl.dataloader.rst b/docs/graphlearning/docs/source_zh_cn/mindspore_gl.dataloader.rst
deleted file mode 100644
index 6bdee18a4e79291504b4c78f3c9877e868eebd96..0000000000000000000000000000000000000000
--- a/docs/graphlearning/docs/source_zh_cn/mindspore_gl.dataloader.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-mindspore_gl.dataloader
-=======================
-
-.. automodule:: mindspore_gl.dataloader
-    :members:
diff --git a/docs/graphlearning/docs/source_zh_cn/mindspore_gl.graph.rst b/docs/graphlearning/docs/source_zh_cn/mindspore_gl.graph.rst
deleted file mode 100644
index 8e49753aa36bc791891aa55845ab9e8c4dbf8aa2..0000000000000000000000000000000000000000
--- a/docs/graphlearning/docs/source_zh_cn/mindspore_gl.graph.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-mindspore_gl.graph
-===================
-
-.. automodule:: mindspore_gl.graph
-    :members:
diff --git a/docs/graphlearning/docs/source_zh_cn/random_sampling_training_of_GIN.md b/docs/graphlearning/docs/source_zh_cn/random_sampling_training_of_GIN.md
index 0707761d7f9a60381476cb5865db3a91cd3bfbf5..daad45061560e53118c62ac261aeff78e576eb11 100644
--- a/docs/graphlearning/docs/source_zh_cn/random_sampling_training_of_GIN.md
+++ b/docs/graphlearning/docs/source_zh_cn/random_sampling_training_of_GIN.md
@@ -13,7 +13,7 @@ Learning也提供了图采样的接口并支持随机批次训练，用户使用
 3. 生成batch图数据，这里提供了一个构造图采样数据的示例。
 4. 图采样网络训练和验证。
 
-本文档展示了使用MindSpore Graph Learning实现GIN在IMDBBinary数据集下图采样随机批次训练。完整代码详见<https://gitee.com/mindspore/graphlearning/blob/master/examples/vc_gin.py>。
+本文档展示了使用MindSpore Graph Learning实现GIN在IMDBBinary数据集下图采样随机批次训练。完整代码详见<https://gitee.com/mindspore/graphlearning/tree/research/examples/vc_gin.py>。
 
 ## GIN原理
 
@@ -504,5 +504,5 @@ Epoch 40, Train loss 0.5511463, Test acc 0.714
 
 在IMDBBinary上验证精度：0.734 (论文：0.75)
 
-以上就是图采样随机批次训练的使用指南。更多样例可参考[examples目录](<https://gitee.com/mindspore/graphlearning/tree/master/examples>)。
+以上就是图采样随机批次训练的使用指南。更多样例可参考[examples目录](<https://gitee.com/mindspore/graphlearning/tree/research/examples>)。
 
diff --git a/docs/hub/api/requirements.txt b/docs/hub/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/hub/api/requirements.txt
+++ b/docs/hub/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/hub/docs/requirements.txt b/docs/hub/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/hub/docs/requirements.txt
+++ b/docs/hub/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/hub/docs/source_en/conf.py b/docs/hub/docs/source_en/conf.py
index 402888035a7a21848be4322e4180495912e07951..19fdf83790089465d9efc78530f2d79469f87587 100644
--- a/docs/hub/docs/source_en/conf.py
+++ b/docs/hub/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindspore_hub
@@ -110,20 +109,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/hub/docs/source_zh_cn/conf.py b/docs/hub/docs/source_zh_cn/conf.py
index 94652ebe54ef8ade861cf44903f4ff80e5c77dcb..bb6c716dedb926643c35372ff5789835205b74a7 100644
--- a/docs/hub/docs/source_zh_cn/conf.py
+++ b/docs/hub/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindspore_hub
@@ -114,20 +113,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/lite/api/requirements.txt b/docs/lite/api/requirements.txt
index beb60acbe3ca97d814509fff2f2aef74f7e54b1b..2c8c3153a0df1c0fe464a7b2b9c2a45d0ef8b687 100644
--- a/docs/lite/api/requirements.txt
+++ b/docs/lite/api/requirements.txt
@@ -1,7 +1,8 @@
 sphinx == 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 exhale == 0.2.3
 breathe == 4.13.0
 numpy
diff --git a/docs/lite/api/source_en/conf.py b/docs/lite/api/source_en/conf.py
index df03bea328251534c956fecd81c4a3f2409b2d11..361eee44a2c567e4ad46a1ce1fa134a7bd474e34 100644
--- a/docs/lite/api/source_en/conf.py
+++ b/docs/lite/api/source_en/conf.py
@@ -66,6 +66,9 @@ html_theme = 'sphinx_rtd_theme'
 
 html_search_language = 'en'
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+
 # Tell sphinx what the primary language being documented is.
 # primary_domain = 'cpp'
 
diff --git a/docs/lite/api/source_zh_cn/api_cpp/lite_cpp_example.rst b/docs/lite/api/source_zh_cn/api_cpp/lite_cpp_example.rst
index 4491d9ebf0614bcb2565e5c8b0e5e17f2c30ac4d..58c80e6e368a4ac049fc99fe6edb1e573755a974 100644
--- a/docs/lite/api/source_zh_cn/api_cpp/lite_cpp_example.rst
+++ b/docs/lite/api/source_zh_cn/api_cpp/lite_cpp_example.rst
@@ -5,5 +5,5 @@
   :maxdepth: 1
 
   极简Demo↗ <https://www.mindspore.cn/lite/docs/zh-CN/master/quick_start/quick_start_cpp.html>
-  基于JNI接口的Android应用开发↗ <https://www.mindspore.cn/lite/docs/zh-CN/master/quick_start/quick_start.html#id10>
+  基于JNI接口的Android应用开发↗ <https://www.mindspore.cn/lite/docs/zh-CN/master/quick_start/quick_start.html#编写端侧推理代码>
   高阶用法↗ <https://www.mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html>
\ No newline at end of file
diff --git a/docs/lite/api/source_zh_cn/api_cpp/mindspore.md b/docs/lite/api/source_zh_cn/api_cpp/mindspore.md
index 2edf9829d7aa573120cec1a794cbe756a7ec7b6d..cd242041b5907136e99ce35334faaf90e4570290 100644
--- a/docs/lite/api/source_zh_cn/api_cpp/mindspore.md
+++ b/docs/lite/api/source_zh_cn/api_cpp/mindspore.md
@@ -445,7 +445,7 @@ Status Load(const std::string &file, ModelType model_type, Graph *graph, const K
 - 参数
 
     - `file`: 模型文件路径。
-    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Opt`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Opt`类型。
+    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Lite`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Lite`类型。
     - `graph`: 输出参数，保存图数据的对象。
     - `dec_key`: 解密密钥，用于解密密文模型，密钥长度为16、24或32。
     - `dec_mode`: 解密模式，可选有`AES-GCM`、`AES-CBC`。
@@ -466,7 +466,7 @@ Status Load(const std::vector<std::string> &files, ModelType model_type, std::ve
 - 参数
 
     - `files`: 多个模型文件路径，用vector存储。
-    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Opt`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Opt`类型。
+    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Lite`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Lite`类型。
     - `graphs`: 输出参数，依次保存图数据的对象。
     - `dec_key`: 解密密钥，用于解密密文模型，密钥长度为16、24或32。
     - `dec_mode`: 解密模式，可选有`AES-GCM`、`AES-CBC`。
@@ -488,7 +488,7 @@ Status Load(const void *model_data, size_t data_size, ModelType model_type, Grap
 
     - `model_data`：模型数据指针。
     - `data_size`：模型数据字节数。
-    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Opt`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Opt`类型。
+    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Lite`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Lite`类型。
     - `graph`：输出参数，保存图数据的对象。
     - `dec_key`: 解密密钥，用于解密密文模型，密钥长度为16、24或32。
     - `dec_mode`: 解密模式，可选有`AES-GCM`、`AES-CBC`。
@@ -700,7 +700,7 @@ Status Build(const void *model_data, size_t data_size, ModelType model_type,
 
     - `model_data`: 指向存储读入模型文件缓冲区的指针。
     - `data_size`: 缓冲区大小。
-    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Opt`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Opt`类型。
+    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Lite`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Lite`类型。
     - `model_context`: 模型[Context](#context)。
     - `dec_key`: 解密密钥，用于解密密文模型，密钥长度为16、24或32。
     - `dec_mode`: 解密模式，可选有`AES-GCM`、`AES-CBC`。
@@ -722,7 +722,7 @@ Status Build(const std::string &model_path, ModelType model_type,
 - 参数
 
     - `model_path`: 模型文件路径。
-    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Opt`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Opt`类型。
+    - `model_type`: 模型文件类型，可选有`ModelType::kMindIR`、`ModelType::kMindIR_Lite`、`ModelType::kOM`。MindSpore Lite支持`ModelType::kMindIR`、`ModelType::kMindIR_Lite`类型。
     - `model_context`: 模型[Context](#context)。
     - `dec_key`: 解密密钥，用于解密密文模型，密钥长度为16、24或32。
     - `dec_mode`: 解密模式，可选有`AES-GCM`、`AES-CBC`。
diff --git a/docs/lite/api/source_zh_cn/api_cpp/mindspore_registry.md b/docs/lite/api/source_zh_cn/api_cpp/mindspore_registry.md
index 12272843726de25e85448b443e98166a2b34a9e4..43310d291b28448c2d97dba354aa8783024b83ef 100644
--- a/docs/lite/api/source_zh_cn/api_cpp/mindspore_registry.md
+++ b/docs/lite/api/source_zh_cn/api_cpp/mindspore_registry.md
@@ -322,7 +322,7 @@ static PassBasePtr GetPassFromStoreRoom(const std::string &pass_name)
 
 > MindSpore Lite开放了部分内置Pass，请见以下说明。用户可以在`names`参数中添加内置Pass的命名标识，以在指定运行处调用内置Pass。
 >
-> - `ConstFoldPass`: 将输入均是常量的节点进行离线计算，导出的模型将不含该节点。特别地，针对shape算子，在[inputShape](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#id5)给定的情形下，也会触发预计算。
+> - `ConstFoldPass`: 将输入均是常量的节点进行离线计算，导出的模型将不含该节点。特别地，针对shape算子，在[inputShape](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#参数说明)给定的情形下，也会触发预计算。
 > - `DumpGraph`: 导出当前状态下的模型。请确保当前模型为NHWC或者NCHW格式的模型，例如卷积算子等。
 > - `ToNCHWFormat`: 将当前状态下的模型转换为NCHW的格式，例如，四维的图输入、卷积算子等。
 > - `ToNHWCFormat`: 将当前状态下的模型转换为NHWC的格式，例如，四维的图输入、卷积算子等。
diff --git a/docs/lite/api/source_zh_cn/conf.py b/docs/lite/api/source_zh_cn/conf.py
index 71de12831ceedfd4db605def43e88ab39a13f089..69199cae86f4cc1544b0d392581a83f743a052e7 100644
--- a/docs/lite/api/source_zh_cn/conf.py
+++ b/docs/lite/api/source_zh_cn/conf.py
@@ -63,6 +63,8 @@ pygments_style = 'sphinx'
 #
 html_theme = 'sphinx_rtd_theme'
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
@@ -189,7 +191,7 @@ with open("./SourceFileNames.txt") as f:
         else:
             shutil.copy(os.path.join(ms_path, name), "../include/")
 
-#Remove "MS_API" in classes. 
+#Remove "MS_API" in classes.
 files_copyed = glob.glob("../include/*.h")
 for file in files_copyed:
     with open(file, "r+", encoding="utf8") as f:
@@ -198,4 +200,4 @@ for file in files_copyed:
             content_new = content.replace("MS_API", "")
             f.seek(0)
             f.truncate()
-            f.write(content_new) 
+            f.write(content_new)
diff --git a/docs/lite/docs/requirements.txt b/docs/lite/docs/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/lite/docs/requirements.txt
+++ b/docs/lite/docs/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/lite/docs/source_en/conf.py b/docs/lite/docs/source_en/conf.py
index 66bc29880d4dcde1d19e7581c5cc8cbf7b3716d8..7e409efe6a1854d18cfab514060e54b9fe92468e 100644
--- a/docs/lite/docs/source_en/conf.py
+++ b/docs/lite/docs/source_en/conf.py
@@ -11,6 +11,7 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import sys
 
 
@@ -55,9 +56,19 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 #
 html_theme = 'sphinx_rtd_theme'
+#modify layout.html for sphinx_rtd_theme.
+import sphinx_rtd_theme
+layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
+layout_src = '../../../../resource/_static/layout.html'
+if os.path.exists(layout_target):
+    os.remove(layout_target)
+shutil.copy(layout_src, layout_target)
 
 html_static_path = ['_static']
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+
 sys.path.append(os.path.abspath('../../../../resource/custom_directives'))
 from custom_directives import IncludeCodeDirective
 
diff --git a/docs/lite/docs/source_en/operator_list_lite.md b/docs/lite/docs/source_en/operator_list_lite.md
index a61809ac2db44e03384cdcc740533eeb7c772da4..22b95ec85202b9c739a3bf93c9b081a451880e0c 100644
--- a/docs/lite/docs/source_en/operator_list_lite.md
+++ b/docs/lite/docs/source_en/operator_list_lite.md
@@ -6,187 +6,190 @@
 
 This article lists the operators supported by MindSpore Lite.
 
-| Operation<br/>&nbsp;              | CPU<br/>FP16 | CPU<br/>FP32 | CPU<br/>Int8 | CPU<br/>UInt8 | GPU<br/>FP16 | GPU<br/>FP32 | NPU<br/>&nbsp;   | TensorRT<br/>&nbsp;    | TensorFlow Lite<br/>operators supported   | Caffe<br/>operators supported | Onnx<br/>operators supported | TensorFlow<br/>operators supported | Ascend<br/>operators supported |
-|-----------------------|:----------:|:----------:|:----------:|:-----------:|:----------:|:-------------------:|:----------:|:----------:|----------|---------|---------|---------|---------|
-| Abs                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Abs                             |                          | Abs                                             | Abs                                             | Abs |
-| Add                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Add                             |                          | Add, Int8Add                                    | Add, AddV2                                      | Add |
-| Adder                 |       | ✅    |       |        |       |       |    |     |                                 |                          | adder_f                                       |                                                 |  |
-| AddGrad |  | ✅ |  |  |  |  |  | | | |  |  |  |
-| AddN                  | ✅             | ✅    |              |               |              |              |          | | AddN                            |                          |                                                 |                                                 |  |
-| Assert                |  ✅  | ✅    |              |               |              |              |           |                           |      |                          |                                                 | Assert                                          |  |
-| Argmax                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Argmax                          | ArgMax                   | ArgMax                                          | ArgMax                                          | ArgMax |
-| Argmin                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |           | | Argmin                          |                          |                                                 | ArgMin                                          | ArgMin |
-| AvgPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | MeanPooling                     | Pooling                  | AveragePool,<br/>GlobalAveragePool,<br/>Int8AveragePool | AvgPool                                         | AvgPool |
+| Operation<br/>&nbsp;              | CPU<br/>FP16 | CPU<br/>FP32 | CPU<br/>Int8 | CPU<br/>UInt8 | GPU<br/>FP16 | GPU<br/>FP32 | NPU<br/>&nbsp;   | TensorRT<br/>&nbsp;    | Ascend<br/>(Ascend310) | TensorFlow Lite<br/>operators supported   | Caffe<br/>operators supported | Onnx<br/>operators supported | TensorFlow<br/>operators supported |
+|-----------------------|:----------:|:----------:|:----------:|:-----------:|:----------:|:-------------------:|:----------:|:----------:|:--------:|---------|---------|---------|-----------------------|
+| Abs                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                            | Abs                             |                          | Abs                                             | Abs                                             |
+| Add                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                            | Add                             |                          | Add, Int8Add                                    | Add, AddV2                                      |
+| Adder                 |       | ✅    |       |        |       |       |    |     |                                 |                                 |                          | adder_f                                       |                                                 |
+| AddGrad |  | ✅ |  |  |  |  |  | | | | |  |  |
+| AddN                  | ✅             | ✅    |              |               |              |              |          | |                             | AddN                            |                          |                                                 |                                                 |
+| Assert                |  ✅  | ✅    |              |               |              |              |           |                           |      |      |                          |                                                 | Assert                                          |
+| Argmax                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                         | Argmax                          | ArgMax                   | ArgMax                                          | ArgMax                                          |
+| Argmin                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |           | | ✅                         | Argmin                          |                          |                                                 | ArgMin                                          |
+| AvgPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                    | MeanPooling                     | Pooling                  | AveragePool,<br/>GlobalAveragePool,<br/>Int8AveragePool | AvgPool                                         |
 | AvgPoolGrad | ✅ | ✅ |  |  |  |  |  | |  |  |  |  |  |
-| BatchNorm             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |                               |  | BatchNorm                | BatchNormalization                              |                                                 | BatchNorm |
-| BatchNormGrad | ✅ | ✅ |  |  |  |  | | | |  |  | |  |
-| BatchToSpace          |              | ✅    | ✅    | ✅     | ✅    | ✅    |          | | BatchToSpace,<br/>BatchToSpaceND    |                          |                                                 | BatchToSpace,<br/>BatchToSpaceND                    |  |
-| BiasAdd               | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ |                      |    | BiasAdd                                         | BiasAdd                                         | BiasAdd |
-| BiasAddGrad | ✅ | ✅ |  |  |  |  | | | | |  |  |  |
-| BroadcastTo             |              | ✅    |              |               |              |              |          | | BroadcastTo                     |                          | Expand                                          | BroadcastTo                                     |  |
-| Cast                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Cast,<br/>QUANTIZE,<br/>DEQUANTIZE      |                          | Cast                                            | Cast                                            | Cast |
-| Ceil                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Ceil                            |                          | Ceil                                            | Ceil                                            | Ceil |
-| Concat                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Concat                          | Concat                   | Concat                                          | ConcatV2                                        | Concat |
-| ConstantOfShape       |  ✅  | ✅    |              |               |              |              |           |                                 |                      |    | ConstantOfShape                                 |                                                 |  |
-| Conv2d                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ |  Conv2D                          | Convolution              | Conv, Int8Conv,<br/>ConvRelu,<br/>Int8ConvRelu          | Conv2D                                          | Conv2D |
+| BatchNorm             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |                               | ✅ |  | BatchNorm                | BatchNormalization                              |                                                 |
+| BatchNormGrad | ✅ | ✅ |  |  |  |  | | | | |  |  | |
+| BatchToSpace          |              | ✅    | ✅    | ✅     | ✅    | ✅    |          | |     | BatchToSpace,<br/>BatchToSpaceND    |                          |                                                 | BatchToSpace,<br/>BatchToSpaceND                    |
+| BiasAdd               | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ | ✅ |                      |    | BiasAdd                                         | BiasAdd                                         |
+| BiasAddGrad | ✅ | ✅ |  |  |  |  | | | | | |  |  |
+| BroadcastTo             |              | ✅    |              |               |              |              |          | |                      | BroadcastTo                     |                          | Expand                                          | BroadcastTo                                     |
+| Cast                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅     | Cast,<br/>QUANTIZE,<br/>DEQUANTIZE      |                          | Cast                                            | Cast                                            |
+| Ceil                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                           | Ceil                            |                          | Ceil                                            | Ceil                                            |
+| Concat                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                         | Concat                          | Concat                   | Concat                                          | ConcatV2                                        |
+| ConstantOfShape       |  ✅  | ✅    |              |               |              |              |           |                                 |                      |                      |    | ConstantOfShape                                 |                                                 |
+| Conv2d                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ |  ✅                         |  Conv2D                          | Convolution              | Conv, Int8Conv,<br/>ConvRelu,<br/>Int8ConvRelu          | Conv2D                                          |
 | Conv2DBackpropFilterFusion | ✅ | ✅ |  |   |  |  |  |  |  |  |  |  |  |
 | Conv2DBackpropInputFusion | ✅ | ✅ |  |   |  |  |  |  |  |  |  |  |  |
-| Conv2dGrad |  | ✅ |  |  |  |  |  |  | | |  |  |  |
-| Conv2dTranspose       | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | DeConv2D                        | Deconvolution            | ConvTranspose                                   | Conv2DBackpropInput                             | Conv2dTranspose |
-| Conv2dTransposeGrad |  | ✅ |  |  |  |  |  | | |  |  |  |  |
-| Cos                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |Cos                             |                          | Cos                                             | Cos                                             | Cos |
-| Crop                  | ✅    | ✅    | ✅    | ✅     |              |              |           |                              |   | Crop                     |                                                 |                                                 |  |
-| CropAndResize         |              | ✅    |              |               |              |              | ✅         |                                 |                          |                                      |           | CropAndResize                                   |  |
-| CumSum                |              | ✅    |             |               |              |              |           |                  |                          |                                             |    |        Cumsum                                         |  |
-| CustomExtractFeatures |              | ✅    |              |               |              |              |         |  | ExtractFeatures                 |                          |                                                 |                                                 |  |
-| CustomNormalize       |              | ✅    |              |               |              |              |          | | Normalize                       |                          |                                                 |                                                 |  |
-| CustomPredict         |              | ✅    |              |               |              |              |          | | Predict                         |                          |                                                 |                                                 |  |
-| DeDepthwiseConv2D     |              | ✅    | ✅    | ✅     | ✅ | ✅ |           |                              |   | Deconvolution            |                                                 |                                                 | Deconvolution |
-| DepthToSpace          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |          | | DepthToSpace                    |                          | DepthToSpace                                    | DepthToSpace                                    |  |
-| DepthwiseConv2dNative | ✅    | ✅    | ✅    | ✅     |     |     | ✅ | | DepthwiseConv2D                 | Convolution              |                                                 | DepthwiseConv2dNative                           | DepthwiseConv2dNative |
-| DetectionPostProcess  |              | ✅    | ✅    | ✅     |              |              |          | | Custom                          |                          |                                                 |                                                 |  |
-| Div                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Div, RealDiv                    |                          | Div                                             | Div, RealDiv                                    | Div |
-| DivGrad |  | ✅ |  |  |  |  |  | | | |  |  |  |
-| DropoutGrad | ✅ | ✅ |  |  |  |  |  | | | |  |  |  |
-| Eltwise               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |                  ✅ |               |   Eltwise                  | Sum, Max<sup>[3]</sup>                          |                                                 | Eltwise |
-| Elu                   |  ✅  | ✅    |              |               |              |              |           |                            |     | ELU                      | Elu,<br/>NonMaxSuppression                          | NonMaxSuppressionV3                             | Elu |
-| EluGrad | | ✅ | | | | | | | | |  |  |  |
-| Equal                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Equal                           |                          | Equal                                           | Equal                                           | Equal |
-| ExpFusion             |   ✅   | ✅    |              |               | ✅    | ✅    |        |   | Exp                             | Exp                      | Exp                                             | Exp                                             |  |
-| ExpandDims            |   ✅   | ✅    | ✅    | ✅     | ✅ | ✅ | ✅       |  ✅ | ExpandDims                      |                          |                                                 | ExpandDims                                      | ExpandDims |
-| Fill                  | ✅             | ✅    |              |               | ✅ | ✅ |        |   | Fill                            |                          |                                                 | Fill                                            | Fill |
-| Flatten               |  ✅    | ✅    |              |               |              |              |           |              ✅                 |   | Flatten                  |                                                 |                                                 | Flatten |
-| FlattenGrad           |  ✅    | ✅    |    |    |   |   |  |   |   |   |    |    |  |
-| Floor                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | flOOR                           |                          | Floor                                           | Floor                                           | Floor |
-| FloorDiv              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | FloorDiv                        |                          |                                                 | FloorDiv                                        |  |
-| FloorMod              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | FloorMod                        |                          |                                                 | FloorMod                                        |  |
-| FullConnection        | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅ | ✅ | FullyConnected                  | InnerProduct             |                                                 |                                                 | FullConnection |
-| FusedBatchNorm        | ✅    | ✅    | ✅    | ✅     |              |              | ✅ | | FusedBatchNorm                  |                          |                                                 | FusedBatchNorm,<br/>FusedBatchNormV3                | FusedBatchNorm |
-| GatherNd              |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           | | GatherND                        |                          |                                                 | GatherNd                                        | GatherNd |
-| Gather                |   ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  | ✅ | Gather                          |                          | Gather                                          | GatherV2                                        | Gather |
-| Greater               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Greater                         |                          | Greater                                         | Greater                                         | Greater |
-| GreaterEqual          | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | GreaterEqual                    |                          |                                                 | GreaterEqual                                    | GreaterEqual |
-| GRU                   | ✅    | ✅    |              |               |              |              |           |            |                     |                          |                                                 |                                                 |  |
-| HardTanh              | ✅    | ✅    |              |               |              |              |           |              |                   |                          |                                                 |                                                 |  |
-| HashtableLookup       |              | ✅    |              |               |              |              |           | | HashtableLookup                 |                          |                                                 |                                                 |  |
-| HSigmoid              |  ✅  | ✅    |              | ✅     |              |              |           |              |                   |                          |                                                 |                                                 |  |
-| Hswish                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |  HardSwish                       |                          |                                                 |                                                 |  |
-| HswishGrad |  | ✅ |  |  |  |  | |  |  | | | |  |
-| InstanceNorm          | ✅ | ✅    |              |               |              |              | ✅       |  | InstanceNorm                    |                          | InstanceNormalization                           |                                                 |  |
-| InvertPermutation     |  ✅  | ✅    |              |               |              |              |           |                |                 |                          |                                                 | InvertPermutation                               |  |
-| L2Norm                |              | ✅    |   ✅  |               |              |              |       |    | L2_NORMALIZATION                |                          |                                                 |                                                 |  |
-| LayerNorm             | ✅ | ✅    | ✅    |               | ✅ | ✅ |           |                   |              |                          |                                                 |                                                 |  |
-| LayerNormGrad         | ✅    | ✅    |     |      |      |      |       |       |       |     |        |       |  |
-| LeakyReLU             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | LeakyRelu                       |                          | LeakyRelu                                       | LeakyRelu                                       | LeakyRelu |
-| LeakyReLUGrad |  | ✅ |  |  |  |  | |  |  | |  |  |  |
-| Less                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Less                            |                          | Less                                            | Less                                            |  |
-| LessEqual             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | LessEqual                       |                          |                                                 | LessEqual                                       |  |
-| LRN                   |              | ✅    |              |               |              |              |        |   | LocalResponseNorm               |                          | Lrn, LRN                                        |                                                 |  |
-| Log                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Log                             |                          | Log                                             | Log                                             |  |
-| LogGrad | ✅ | ✅ |  |  |  |  |  | | | |  |  |  |
-| LogicalAnd            | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | LogicalAnd                      |                          | And                                             | LogicalAnd                                      |  |
-| LogicalNot            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | LogicalNot                      |                          | Not                                             | LogicalNot                                      |  |
-| LogicalOr             | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |  LogicalOr                       |                          | Or                                              | LogicalOr                                       |  |
-| LogSoftmax             | ✅ | ✅    |              |               |     |     |   | |  LogSoftmax        |                          | LogSoftmax                |                                        |  |
-| LshProjection         |              | ✅    |              |               |              |              |       |    | LshProjection                   |                          |                                                 |                                                 |  |
-| LSTM                  |    ✅   | ✅    |              |               |              |              |           |                                 |               |           | LSTM                                            |                                                 |  |
-| MatMul                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | BatchMatMul                     |                          | MatMul,<br/>Gemm                         | MatMul,<br/>BatchMatMul,<br/>BatchMatMulV2           | MatMul |  
-| MatMulGrad |  | ✅ |  |  |  | |  |  | | |  |  |  |
-| Maximum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | Maximum                         |                          |  Max                                            | Maximum                                         | Maximum |
-| MaximumGrad | ✅ | ✅ | | |  |  |  |  | | | | |  |
-| MaxPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | MaxPooling                      | Pooling                  | MaxPool,<br/>GlobalMaxPool                          | MaxPool                                         | MaxPool |
+| Conv2dGrad |  | ✅ |  |  |  |  |  |  | | | |  |  |
+| Conv2dTranspose       | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                       | DeConv2D                        | Deconvolution            | ConvTranspose                                   | Conv2DBackpropInput                             |
+| Conv2dTransposeGrad |  | ✅ |  |  |  |  |  | | | |  |  |  |
+| Cos                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |✅                             |Cos                             |                          | Cos                                             | Cos                                             |
+| Crop                  | ✅    | ✅    | ✅    | ✅     |              |              |           |                              |   |   | Crop                     |                                                 |                                                 |
+| CropAndResize         |              | ✅    |              |               |              |              | ✅         |                                 |                          |                          |                                      |           | CropAndResize                                   |
+| CumSum                |              | ✅    |             |               |              |              |           |                  |                          |                          |                                             |    |        Cumsum                                         |
+| CustomExtractFeatures |              | ✅    |              |               |              |              |         |  |                  | ExtractFeatures                 |                          |                                                 |                                                 |
+| CustomNormalize       |              | ✅    |              |               |              |              |          | |                        | Normalize                       |                          |                                                 |                                                 |
+| CustomPredict         |              | ✅    |              |               |              |              |          | |                          | Predict                         |                          |                                                 |                                                 |
+| Deconvolution | |  | | | | | | | ✅ |  | | | |
+| DeDepthwiseConv2D     |              | ✅    | ✅    | ✅     | ✅ | ✅ |           |                              |   |   | Deconvolution            |                                                 |                                                 |
+| DepthToSpace          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |          | |                     | DepthToSpace                    |                          | DepthToSpace                                    | DepthToSpace                                    |
+| DepthwiseConv2dNative | ✅    | ✅    | ✅    | ✅     |     |     | ✅ | | ✅                | DepthwiseConv2D                 | Convolution              |                                                 | DepthwiseConv2dNative                           |
+| DetectionPostProcess  |              | ✅    | ✅    | ✅     |              |              |          | |                           | Custom                          |                          |                                                 |                                                 |
+| Div                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                   | Div, RealDiv                    |                          | Div                                             | Div, RealDiv                                    |
+| DivGrad |  | ✅ |  |  |  |  |  | | | | |  |  |
+| DropoutGrad | ✅ | ✅ |  |  |  |  |  | | | | |  |  |
+| Eltwise               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |                  ✅ | ✅ |               |   Eltwise                  | Sum, Max<sup>[3]</sup>                          |                                                 |
+| Elu                   |  ✅  | ✅    |              |               |              |              |           |                            | ✅ |     | ELU                      | Elu,<br/>NonMaxSuppression                          | NonMaxSuppressionV3                             |
+| EluGrad | | ✅ | | | | | | | | | |  |  |
+| Equal                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                          | Equal                           |                          | Equal                                           | Equal                                           |
+| ExpFusion             |   ✅   | ✅    |              |               | ✅    | ✅    |        |   |                              | Exp                             | Exp                      | Exp                                             | Exp                                             |
+| ExpandDims            |   ✅   | ✅    | ✅    | ✅     | ✅ | ✅ | ✅       |  ✅ | ✅                     | ExpandDims                      |                          |                                                 | ExpandDims                                      |
+| Fill                  | ✅             | ✅    |              |               | ✅ | ✅ |        |   | ✅                           | Fill                            |                          |                                                 | Fill                                            |
+| Flatten               |  ✅    | ✅    |              |               |              |              |           |              ✅                 | ✅ |   | Flatten                  |                                                 |                                                 |
+| FlattenGrad           |  ✅    | ✅    |    |    |   |   |  |   |   |   |   |    |    |
+| Floor                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                          | flOOR                           |                          | Floor                                           | Floor                                           |
+| FloorDiv              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                         | FloorDiv                        |                          |                                                 | FloorDiv                                        |
+| FloorMod              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                         | FloorMod                        |                          |                                                 | FloorMod                                        |
+| FullConnection        | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅ | ✅ | ✅                 | FullyConnected                  | InnerProduct             |                                                 |                                                 |
+| FusedBatchNorm        | ✅    | ✅    | ✅    | ✅     |              |              | ✅ | | ✅                 | FusedBatchNorm                  |                          |                                                 | FusedBatchNorm,<br/>FusedBatchNormV3                |
+| GatherNd              |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           | | ✅                       | GatherND                        |                          |                                                 | GatherNd                                        |
+| Gather                |   ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  | ✅ | ✅                         | Gather                          |                          | Gather                                          | GatherV2                                        |
+| Greater               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                        | Greater                         |                          | Greater                                         | Greater                                         |
+| GreaterEqual          | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                   | GreaterEqual                    |                          |                                                 | GreaterEqual                                    |
+| GRU                   | ✅    | ✅    |              |               |              |              |           |            |                     |                     |                          |                                                 |                                                 |
+| HardTanh              | ✅    | ✅    |              |               |              |              |           |              |                   |                   |                          |                                                 |                                                 |
+| HashtableLookup       |              | ✅    |              |               |              |              |           | |                  | HashtableLookup                 |                          |                                                 |                                                 |
+| HSigmoid              |  ✅  | ✅    |              | ✅     |              |              |           |              |                   |                   |                          |                                                 |                                                 |
+| Hswish                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                         |  HardSwish                       |                          |                                                 |                                                 |
+| HswishGrad |  | ✅ |  |  |  |  | |  |  |  | | | |
+| InstanceNorm          | ✅ | ✅    |              |               |              |              | ✅       |  |                     | InstanceNorm                    |                          | InstanceNormalization                           |                                                 |
+| InvertPermutation     |  ✅  | ✅    |              |               |              |              |           |                |                 |                 |                          |                                                 | InvertPermutation                               |
+| L2Norm                |              | ✅    |   ✅  |               |              |              |       |    |                 | L2_NORMALIZATION                |                          |                                                 |                                                 |
+| LayerNorm             | ✅ | ✅    | ✅    |               | ✅ | ✅ |           |                   |              |              |                          |                                                 |                                                 |
+| LayerNormGrad         | ✅    | ✅    |     |      |      |      |       |       |       |       |     |        |       |
+| LeakyReLU             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                      | LeakyRelu                       |                          | LeakyRelu                                       | LeakyRelu                                       |
+| LeakyReLUGrad |  | ✅ |  |  |  |  | |  |  |  | |  |  |
+| Less                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                             | Less                            |                          | Less                                            | Less                                            |
+| LessEqual             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                        | LessEqual                       |                          |                                                 | LessEqual                                       |
+| LRN                   |              | ✅    |              |               |              |              |        |   |                | LocalResponseNorm               |                          | Lrn, LRN                                        |                                                 |
+| Log                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                              | Log                             |                          | Log                                             | Log                                             |
+| LogGrad | ✅ | ✅ |  |  |  |  |  | | | | |  |  |
+| LogicalAnd            | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                       | LogicalAnd                      |                          | And                                             | LogicalAnd                                      |
+| LogicalNot            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                       | LogicalNot                      |                          | Not                                             | LogicalNot                                      |
+| LogicalOr             | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                         |  LogicalOr                       |                          | Or                                              | LogicalOr                                       |
+| LogSoftmax             | ✅ | ✅    |              |               |     |     |   | |          |  LogSoftmax        |                          | LogSoftmax                |                                        |
+| LshProjection         |              | ✅    |              |               |              |              |       |    |                    | LshProjection                   |                          |                                                 |                                                 |
+| LSTM                  |    ✅   | ✅    |              |               |              |              |           |                                 |               |               |           | LSTM                                            |                                                 |
+| MatMul                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                    | BatchMatMul                     |                          | MatMul,<br/>Gemm                         | MatMul,<br/>BatchMatMul,<br/>BatchMatMulV2           |
+| MatMulGrad |  | ✅ |  |  |  | |  |  | | | |  |  |
+| Maximum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | ✅                        | Maximum                         |                          |  Max                                            | Maximum                                         |
+| MaximumGrad | ✅ | ✅ | | |  |  |  |  | | | | | |
+| MaxPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                     | MaxPooling                      | Pooling                  | MaxPool,<br/>GlobalMaxPool                          | MaxPool                                         |
 | MaxPoolGrad | ✅ | ✅ |  |  |  |  | | |  |  |  |  |  |
-| Merge                 | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | Merge                                           |  |
-| Minimum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | Minimum                         |                          | Min                                             | Minimum                                         | Minimum |
-| MinimumGrad | ✅ | ✅ | | |  |  |  | |  | |  |  |  |
-| Mul                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Mul                             |                          | Mul                                             | Mul                                             | Mul |
-| MulGrad |  | ✅ |  |  |  |  | |  |  | |  |  |  |
-| Neg                   | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | Neg                             |                          | Neg                                             | Neg                                             |  |
-| NegGrad | ✅ | ✅ | | |  |  | |  |  | |  | |  |
-| NotEqual              | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | NotEqual                        |                          |                                                 | NotEqual                                        |  |
-| OneHot                |  ✅  | ✅    |              |               | ✅    | ✅    |       |    | OneHot                          |                          | OneHot                                          | OneHot                                          |  |
-| Pad                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Pad, MirrorPad, PadV2                  |                          | Pad                                             | MirrorPad, Pad, PadV2                                  | Pad |
-| Pow                   | ✅             | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | Pow                             | Power                    | Pow<sup>[2]</sup>                               | Pow                                             | Pow |
-| PowGrad | | ✅ |  |  |  |  | |  |  | |  |  |  |
-| PReLU                 |  ✅  | ✅    |              |               | ✅    | ✅    |           | | PRELU                           | PReLU                    | PRelu                                           |                                                 | PReLU |
-| QuantDTypeCast        | ✅ | ✅ | ✅ | ✅ |   |   |   |   |   |   |   |   |  |
-| RaggedRange        | ✅ | ✅ |   |   |   |   |   |   |   |   |   | RaggedRange |  |
-| RandomStandardNormal  |  ✅ | ✅    |              |               |              |              |       |    |                                 |                          |                                                 | RandomStandardNormal                            |  |
-| RandomUniform         |              | ✅    |              |               |              |              |        |   |                                 |                          |                                                 | RandomUniform                                   |  |
-| Range                 |  ✅  | ✅    |              |               |              |              |         |  | Range                           |                          | Range                                           | Range |  |
-| Rank                  |  ✅  | ✅    |              |               |              |              |         |  | Rank                            |                          |                                                 | Rank                                            |  |
-| RealDiv               | ✅ | ✅ |   |   |   |   |   |   |   |   |   |   |  |
-| Reciprocal            | ✅    | ✅    | ✅    |               |              |              | ✅ |              |                   |                          | Reciprocal                                      |                                                 |  |
-| ReduceAll             |              | ✅    |              |               |              |              |           |              |                   |                          |                                                 | All                                             |  |
-| ReduceASum            |  ✅  | ✅    |              |               | ✅    | ✅    |           |                |                 | Reduction                |                                                 |                                                 |  |
-| ReduceMax             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ | ReduceMax                       |                          | ReduceMax                                       | Max                                             |  |
-| ReduceMean            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | Mean                            | Reduction                | ReduceMean                                      | Mean                                            | ReduceMean |
-| ReduceMin             | ✅ | ✅   | ✅    | ✅     | ✅    | ✅    |           | ✅ | ReduceMin                       |                          | ReduceMin                                       | Min                                             |  |
-| ReduceProd            | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ | ReduceProd                      |                          | ReduceProd                                      | Prod                                            |  |
-| ReduceSum             | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ | Sum                             | Reduction                | ReduceSum                                       | Sum                                             | ReduceSum |
-| ReduceSumSquare       | ✅ | ✅    | ✅    | ✅     |              |              |           |                      |           | Reduction                | ReduceSumSquare                                 |                                                 |  |
-| ReLU                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Relu                            | ReLU                     | Relu                                            | Relu                                            | ReLU |
-| ReLUGrad | ✅ | ✅ |  |  |  |  |  | | |  |  |  |  |
-| ReLU6                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Relu6                           | ReLU6                    | Clip<sup>[1]</sup>                              | Relu6                                           | ReLU6 |
-| ReLU6Grad | ✅ | ✅ |  |  |  |  |  |  | | |  |  |  |
-| Reshape               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Reshape                         | Reshape                  | Reshape,<br/>Flatten                                 | Reshape                                         | Reshape |
-| Resize                |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ResizeBilinear,<br/>NearestNeighbor | Interp                   |      Resize, Upsample                                   | ResizeBilinear,<br/>ResizeBicubic,<br/>ResizeNearestNeighbor | Upsample,<br/>ResizeNearestNeighbor |
-| ResizeGrad | ✅ | ✅ |  |  |  |  | | |  |  | |  |  |
-| Reverse               |              | ✅    |              |               |              |              |       |    | reverse                         |                          |                                                 | ReverseV2                                       |  |
-| ReverseSequence       |              | ✅    |              |               |              |              |       |    | ReverseSequence                 |                          |                                                 | ReverseSequence                                 |  |
-| Round                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Round                           |                          | Round                                           | Round                                           | Round |
-| Rsqrt                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Rsqrt                           |                          |                                                 | Rsqrt                                           |  |
-| Select                |              | ✅    |              |               |              |              |           |                                 |                          |                                  |               | Select                                          |  |
-| Selu                  |              |              |              |               |              |              |           |                                 |                          |                       |                          | Selu                                            |  |
-| Scale                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅  | ✅ |                                | Scale                    |                                                 |                                                 | Scale |
-| ScatterNd | ✅ | ✅ |  |  | | |  |  | ScatterNd |  | ScatterND |  |  |
-| ScatterNdUpdate | ✅ | ✅ |  |  | | |  |  | ScatterNdUpdate |  | ScatterNdUpdate |  |  |
-| Shape                 |   ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |   | | Shape                           |                          | Shape                                           | Shape                                           | Shape |
-| Sigmoid               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  | | Logistic                        | Sigmoid                  | Sigmoid                                         | Sigmoid                                         | Sigmoid |
+| Merge                 | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | Merge                                           |
+| Minimum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | ✅                        | Minimum                         |                          | Min                                             | Minimum                                         |
+| MinimumGrad | ✅ | ✅ | | |  |  |  | |  |  | |  |  |
+| Mul                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                            | Mul                             |                          | Mul                                             | Mul                                             |
+| MulGrad |  | ✅ |  |  |  |  | |  |  |  | |  |  |
+| Neg                   | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                              | Neg                             |                          | Neg                                             | Neg                                             |
+| NegGrad | ✅ | ✅ | | |  |  | |  |  |  | |  | |
+| NotEqual              | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                         | NotEqual                        |                          |                                                 | NotEqual                                        |
+| OneHot                |  ✅  | ✅    |              |               | ✅    | ✅    |       |    |                           | OneHot                          |                          | OneHot                                          | OneHot                                          |
+| Pad                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                 | Pad, MirrorPad, PadV2                  |                          | Pad                                             | MirrorPad, Pad, PadV2                                  |
+| Pow                   | ✅             | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ✅                            | Pow                             | Power                    | Pow<sup>[2]</sup>                               | Pow                                             |
+| PowGrad | | ✅ |  |  |  |  | |  |  |  | |  |  |
+| PReLU                 |  ✅  | ✅    |              |               | ✅    | ✅    |           | | ✅                          | PRELU                           | PReLU                    | PRelu                                           |                                                 |
+| QuantDTypeCast        | ✅ | ✅ | ✅ | ✅ |   |   |   |   |   |   |   |   |   |
+| RaggedRange        | ✅ | ✅ |   |   |   |   |   |   |   |   |   |   | RaggedRange |
+| RandomStandardNormal  |  ✅ | ✅    |              |               |              |              |       |    |                                 |                                 |                          |                                                 | RandomStandardNormal                            |
+| RandomUniform         |              | ✅    |              |               |              |              |        |   |                                 |                                 |                          |                                                 | RandomUniform                                   |
+| Range                 |  ✅  | ✅    |              |               |              |              |         |  |                            | Range                           |                          | Range                                           | Range |
+| Rank                  |  ✅  | ✅    |              |               |              |              |         |  |                             | Rank                            |                          |                                                 | Rank                                            |
+| RealDiv               | ✅ | ✅ |   |   |   |   |   |   | ✅ |   |   |   |   |
+| Reciprocal            | ✅    | ✅    | ✅    |               |              |              | ✅ |              |                   |                   |                          | Reciprocal                                      |                                                 |
+| ReduceAll             |              | ✅    |              |               |              |              |           |              |                   |                   |                          |                                                 | All                                             |
+| ReduceASum            |  ✅  | ✅    |              |               | ✅    | ✅    |           |                |                 |                 | Reduction                |                                                 |                                                 |
+| ReduceMax             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ |                        | ReduceMax                       |                          | ReduceMax                                       | Max                                             |
+| ReduceMean            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ✅                           | Mean                            | Reduction                | ReduceMean                                      | Mean                                            |
+| ReduceMin             | ✅ | ✅   | ✅    | ✅     | ✅    | ✅    |           | ✅ |                        | ReduceMin                       |                          | ReduceMin                                       | Min                                             |
+| ReduceProd            | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ |                       | ReduceProd                      |                          | ReduceProd                                      | Prod                                            |
+| ReduceSum             | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ | ✅                            | Sum                             | Reduction                | ReduceSum                                       | Sum                                             |
+| ReduceSumSquare       | ✅ | ✅    | ✅    | ✅     |              |              |           |                      |           |           | Reduction                | ReduceSumSquare                                 |                                                 |
+| ReLU                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                           | Relu                            | ReLU                     | Relu                                            | Relu                                            |
+| ReLUGrad | ✅ | ✅ |  |  |  |  |  | | | |  |  |  |
+| ReLU6                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                          | Relu6                           | ReLU6                    | Clip<sup>[1]</sup>                              | Relu6                                           |
+| ReLU6Grad | ✅ | ✅ |  |  |  |  |  |  | | | |  |  |
+| Reshape               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                        | Reshape                         | Reshape                  | Reshape,<br/>Flatten                                 | Reshape                                         |
+| Resize                |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ |  | ResizeBilinear,<br/>NearestNeighbor | Interp                   |      Resize, Upsample                                   | ResizeBilinear,<br/>ResizeBicubic,<br/>ResizeNearestNeighbor |
+| ResizeGrad | ✅ | ✅ |  |  |  |  | | |  |  |  | |  |
+| ResizeNearestNeighbor |  |  | | | | | | | ✅ | | | | |
+| Reverse               |              | ✅    |              |               |              |              |       |    |                          | reverse                         |                          |                                                 | ReverseV2                                       |
+| ReverseSequence       |              | ✅    |              |               |              |              |       |    |                  | ReverseSequence                 |                          |                                                 | ReverseSequence                                 |
+| Round                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                          | Round                           |                          | Round                                           | Round                                           |
+| Rsqrt                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                            | Rsqrt                           |                          |                                                 | Rsqrt                                           |
+| Select                |              | ✅    |              |               |              |              |           |                                 |                          |                          |                                  |               | Select                                          |
+| Selu                  |              |              |              |               |              |              |           |                                 |                          |                          |                       |                          | Selu                                            |
+| Scale                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅  | ✅ | ✅ |                                | Scale                    |                                                 |                                                 |
+| ScatterNd | ✅ | ✅ |  |  | | |  |  |  | ScatterNd |  | ScatterND |  |
+| ScatterNdUpdate | ✅ | ✅ |  |  | | |  |  |  | ScatterNdUpdate |  | ScatterNdUpdate |  |
+| Shape                 |   ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |   | | ✅                          | Shape                           |                          | Shape                                           | Shape                                           |
+| Sigmoid               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  | | ✅                       | Logistic                        | Sigmoid                  | Sigmoid                                         | Sigmoid                                         |
 | SigmoidGrad | ✅ | ✅ |  |  |  |  | |  |  |  |  |  |  |
-| Sin                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Sin                             |                          | Sin                                             | Sin                                             | Sin |
-| Size                  |  ✅  | ✅    |              |               |              |              |           |                                 |                          |                                        |         | Size                                            |  |
-| Slice                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Slice                           | Slice                    | Slice                                           | Slice                                           |  |
-| SkipGram              |              | ✅    |              |               |              |              |           | | SKipGram                        |                          |                                                 |                                                 |  |
-| Softmax               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Softmax                         | Softmax                  | Softmax                                         | Softmax                                         | Softmax |
+| Sin                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                            | Sin                             |                          | Sin                                             | Sin                                             |
+| Size                  |  ✅  | ✅    |              |               |              |              |           |                                 |                          |                          |                                        |         | Size                                            |
+| Slice                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                            | Slice                           | Slice                    | Slice                                           | Slice                                           |
+| SkipGram              |              | ✅    |              |               |              |              |           | |                         | SKipGram                        |                          |                                                 |                                                 |
+| Softmax               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                        | Softmax                         | Softmax                  | Softmax                                         | Softmax                                         |
 | SoftmaxGrad |  | ✅ |  |  |  |  | | |  |  |  |  |  |
-| Softplus | ✅ | ✅ |  |  |  |  |  | | |  |  | Softplus |  |
-| SpaceToBatch          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |         |  | SpaceToBatch                    |                          |                                                 |                                                 |  |
-| SpaceToBatchND        |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |       |    | SpaceToBatchND                  |                          |                                                 | SpaceToBatchND                                  |  |
-| SpaceToDepth          |  ✅  | ✅    |              |               | ✅    | ✅    |         |  | SpaceToDepth                    |                          | SpaceToDepth                                    |                                                 |  |
-| SparseToDense         |  ✅  | ✅    |              |               | ✅    | ✅    |          | | SpareToDense                    |                          |                                                 |                                                 |  |
-| Splice                |  ✅  | ✅    |       |        |              |              |    | |                    |                          | Splice                                           |                                    |  |
-| Split                 | ✅    | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ | | Split, SplitV                   |                          | Split                                           | Split, SplitV                                   |  |
-| SplitWithOverlap      | ✅    | ✅    |      |      |     |     |  |   |   |   |    |   |  |
-| Sqrt                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Sqrt                            |                          | Sqrt                                            | Sqrt                                            | Sqrt |
-| Square                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Square                          |                          |                                                 | Square                                          | Square |
-| SquaredDifference     | ✅    | ✅    |              |               | ✅    | ✅    |   | | SquaredDifference               |                          |                                                 | SquaredDifference                               |  |
-| Squeeze               |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | Squeeze                         |                          | Squeeze                                         | Squeeze                                         |  |
-| StridedSlice          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | StridedSlice                    |                          | Slice,<br/>DynamicSlice                              | StridedSlice                                    | StridedSlice |
-| StridedSliceGrad      |  ✅  | ✅    |       |       |       |      |    |   |         |        |        |         |  |
-| Stack                 | ✅    | ✅    |              |               | ✅    | ✅    |           | | Stack                           |                          |                                                 | Pack                                            | Stack |
-| Sub                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Sub                             |                          | Sub                                             | Sub                                             | Sub |
-| SubGrad |  | ✅ |  |  |  |  |  | | | |  |  |  |
-| Swish | ✅ | ✅ | | | | | | | | | | |  |
-| Switch                | ✅ | ✅    |              |               |              |              |           |                                 |                          |                     |                            | Switch                                          |
-| Tanh                  | ✅    | ✅    |              |               | ✅    | ✅    | ✅ |  ✅ | Tanh                            | TanH                     | Tanh, Sign                                      | Tanh                                            | Tanh |
-| TanhGrad |  | ✅ | | |  |  | |  | | |  |  |  |
-| TensorListFromTensor  | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListFromTensor                            |  |
-| TensorListGetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListGetItem                               |  |
-| TensorListReserve     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListReserve                               |  |
-| TensorListSetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListSetItem                               |  |
-| TensorListStack       | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListStack                                 |  |
-| Tile                  | ✅ | ✅    |              |               |              |              | ✅       |  | Tile                            | Tile                     | Tile                                    | Tile                                            |  |
-| TopK                  | ✅ | ✅    | ✅    | ✅     |              |              |       |    | TopKV2                          |                          | TopK                                            | TopKV2                                          |  |
-| Transpose             | ✅    | ✅    |   ✅    |               | ✅    | ✅    | ✅ | ✅ |  Transpose                       | Permute                  | Transpose,Int8Transpose                         | Transpose                                       | Transpose |
-| UniformReal           |              | ✅    |              |               |              |              |           |                                 |                          |                   |                              |                                                 |  |
-| Unique                |  ✅  | ✅    |              |               |              |              |      |     | Unique                          |                          |                                                 |                                                 |  |
-| UnsortedSegmentSum    |  ✅  | ✅    |       |       |              |              |    |                          |               |                      |                                        |  UnsortedSegmentSum |  |
-| Unsqueeze             |  ✅  | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ |          ✅               |               |                      | Unsqueeze                                       |                                                 |  |
-| Unstack               |  ✅  | ✅    |              |               |              |              |           | | Unstack                         |                          |                                                 |  |                                                 |
-| Where                 |  ✅  | ✅    |              |               |              |              |           | | Where                           |                          | NonZero,Where                | Where                           |  |
-| ZerosLike             |  ✅  | ✅    |              |               |              |              |           |  | ZerosLike                       |                          |                                                 | ZerosLike                                       |  |
-| Other operators supported by the converter.<sup>[4]</sup> |   |   |    |        |            |            |         |          |         |          | Constant,<br/>Atan, Asin, Tan, Erf,<br/>Loop, Dropout, If, Identity,<br/>Int8GivenIntTensorFill,<br/>Int8GivenTensorFill,<br/>Int8Quantize,<br/>Int8Dequantize,<br/>LpNormalization    | Dropout, Enter,<br/>Exit, If,<br/>IsFinite,<br/>LinSpace,<br/>LoopCond,<br/>NextIteration,<br/>StatelessIf,<br/>StatelessWhile,<br/>TensorArrayGatherV3,<br/>TensorArrayReadV3,<br/>TensorArrayScatterV3,<br/>TensorArraySizeV3,<br/>TensorArrayV3,<br/>TensorArrayWriteV3,<br/>While |  |
+| Softplus | ✅ | ✅ |  |  |  |  |  | | | |  |  | Softplus |
+| SpaceToBatch          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |         |  |                     | SpaceToBatch                    |                          |                                                 |                                                 |
+| SpaceToBatchND        |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |       |    |                   | SpaceToBatchND                  |                          |                                                 | SpaceToBatchND                                  |
+| SpaceToDepth          |  ✅  | ✅    |              |               | ✅    | ✅    |         |  |                     | SpaceToDepth                    |                          | SpaceToDepth                                    |                                                 |
+| SparseToDense         |  ✅  | ✅    |              |               | ✅    | ✅    |          | |                     | SpareToDense                    |                          |                                                 |                                                 |
+| Splice                |  ✅  | ✅    |       |        |              |              |    | |                    |                    |                          | Splice                                           |                                    |
+| Split                 | ✅    | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ | |                    | Split, SplitV                   |                          | Split                                           | Split, SplitV                                   |
+| SplitWithOverlap      | ✅    | ✅    |      |      |     |     |  |   |   |   |   |    |   |
+| Sqrt                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                           | Sqrt                            |                          | Sqrt                                            | Sqrt                                            |
+| Square                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                         | Square                          |                          |                                                 | Square                                          |
+| SquaredDifference     | ✅    | ✅    |              |               | ✅    | ✅    |   | |                | SquaredDifference               |                          |                                                 | SquaredDifference                               |
+| Squeeze               |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ |                          | Squeeze                         |                          | Squeeze                                         | Squeeze                                         |
+| StridedSlice          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                   | StridedSlice                    |                          | Slice,<br/>DynamicSlice                              | StridedSlice                                    |
+| StridedSliceGrad      |  ✅  | ✅    |       |       |       |      |    |   |         |         |        |        |         |
+| Stack                 | ✅    | ✅    |              |               | ✅    | ✅    |           | | ✅                          | Stack                           |                          |                                                 | Pack                                            |
+| Sub                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                            | Sub                             |                          | Sub                                             | Sub                                             |
+| SubGrad |  | ✅ |  |  |  |  |  | | | | |  |  |
+| Swish | ✅ | ✅ | | | | | | | | | | | |
+| Switch                | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                     |                            | Switch                                          |
+| Tanh                  | ✅    | ✅    |              |               | ✅    | ✅    | ✅ |  ✅ | ✅                           | Tanh                            | TanH                     | Tanh, Sign                                      | Tanh                                            |
+| TanhGrad |  | ✅ | | |  |  | |  | | | |  |  |
+| TensorListFromTensor  | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListFromTensor                            |
+| TensorListGetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListGetItem                               |
+| TensorListReserve     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListReserve                               |
+| TensorListSetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListSetItem                               |
+| TensorListStack       | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListStack                                 |
+| Tile                  | ✅ | ✅    |              |               |              |              | ✅       |  |                             | Tile                            | Tile                     | Tile                                    | Tile                                            |
+| TopK                  | ✅ | ✅    | ✅    | ✅     |              |              |       |    |                           | TopKV2                          |                          | TopK                                            | TopKV2                                          |
+| Transpose             | ✅    | ✅    |   ✅    |               | ✅    | ✅    | ✅ | ✅ |  ✅                      |  Transpose                       | Permute                  | Transpose,Int8Transpose                         | Transpose                                       |
+| UniformReal           |              | ✅    |              |               |              |              |           |                                 |                          |                          |                   |                              |                                                 |
+| Unique                |  ✅  | ✅    |              |               |              |              |      |     |                           | Unique                          |                          |                                                 |                                                 |
+| UnsortedSegmentSum    |  ✅  | ✅    |       |       |              |              |    |                          |               |               |                      |                                        |  UnsortedSegmentSum |
+| Unsqueeze             |  ✅  | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ |          ✅               |               |               |                      | Unsqueeze                                       |                                                 |
+| Unstack               |  ✅  | ✅    |              |               |              |              |           | |                          | Unstack                         |                          |                                                 |  |
+| Upsample |  |  | | | | | | | ✅ |  | | | |
+| Where                 |  ✅  | ✅    |              |               |              |              |           | |                            | Where                           |                          | NonZero,Where                | Where                           |
+| ZerosLike             |  ✅  | ✅    |              |               |              |              |           |  |                        | ZerosLike                       |                          |                                                 | ZerosLike                                       |
+| Other operators supported by the converter.<sup>[4]</sup> |   |   |    |        |            |            |         |          |         |         |          | Constant,<br/>Atan, Asin, Tan, Erf,<br/>Loop, Dropout, If, Identity,<br/>Int8GivenIntTensorFill,<br/>Int8GivenTensorFill,<br/>Int8Quantize,<br/>Int8Dequantize,<br/>LpNormalization    | Dropout, Enter,<br/>Exit, If,<br/>IsFinite,<br/>LinSpace,<br/>LoopCond,<br/>NextIteration,<br/>StatelessIf,<br/>StatelessWhile,<br/>TensorArrayGatherV3,<br/>TensorArrayReadV3,<br/>TensorArrayScatterV3,<br/>TensorArraySizeV3,<br/>TensorArrayV3,<br/>TensorArrayWriteV3,<br/>While |
 
 [1] Clip: Only support converting clip(0, 6) to Relu6.
 
diff --git a/docs/lite/docs/source_en/quick_start/image_segmentation.md b/docs/lite/docs/source_en/quick_start/image_segmentation.md
index af42ced8fe8e5617e06520572ee8c031c5b23306..8ccd99807a06b4ea4e180897b5a81ca8a5847340 100644
--- a/docs/lite/docs/source_en/quick_start/image_segmentation.md
+++ b/docs/lite/docs/source_en/quick_start/image_segmentation.md
@@ -80,11 +80,7 @@ The Android demo for on-device image segmentation uses the Java layer. Therefore
 app
 ├── src/main
 │   ├── assets # Resource file
-|   |   └── model # Model file
-|   |       └── segment_model.ms # Stored model file
-│   |
-│   ├── libs # Binary archive file of the Android library project
-|   |   └── mindspore-lite-version.aar # MindSpore Lite archive file of the Android version
+|   |   └── segment_model.ms # Stored model file
 │   |
 │   ├── java # Application code at the Java layer
 │   │   └── com.mindspore.imagesegmentation
@@ -95,7 +91,9 @@ app
 │   ├── res # Resource files related to Android
 │   └── AndroidManifest.xml # Android configuration file
 │
-│
+├── libs # Binary archive file of the Android library project
+|     └── mindspore-lite-version.aar # MindSpore Lite archive file of the Android version
+|
 ├── build.gradle # Other Android configuration file
 ├── download.gradle # Downloading the files that the project depends on
 └── ...
@@ -121,18 +119,20 @@ Note: If the download fails, manually download the model file [segment_model.ms]
 
 The inference code and process are as follows. For details about the complete code, see [src/java/com/mindspore/imagesegmentation/TrackingMobile](https://gitee.com/mindspore/models/blob/master/official/lite/image_segmentation/app/src/main/java/com/mindspore/imagesegmentation/help/TrackingMobile.java).
 
-1. Load the MindSpore Lite model file and build the context, session, and computational graph for inference.
+1. Load the MindSpore Lite model file and build the context, model, and computational graph for inference.
 
-    - Create a session.
+    - Create a model.
 
       ```java
       // Create and init config.
       MSContext context = new MSContext();
-      context.init(2, CpuBindMode.HIGHER_CPU, false);
-      boolean ret = context.addDeviceInfo(com.mindspore.config.DeviceType.DT_CPU, false, 0);
-      if (!ret) {
-          Log.e(TAG, "Create CPU Config failed.");
-          return null;
+      if (!context.init(2, CpuBindMode.MID_CPU, false)) {
+          Log.e(TAG, "Init context failed");
+          return;
+      }
+      if (!context.addDeviceInfo(DeviceType.DT_CPU, false, 0)) {
+          Log.e(TAG, "Add device info failed");
+          return;
       }
       ```
 
@@ -140,11 +140,15 @@ The inference code and process are as follows. For details about the complete co
 
       ```java
       // build model.
-      boolean ret = model.build(filePath, ModelType.MT_MINDIR, msContext);
-      if (!ret) {
-          model.free();
-          Log.e(TAG, "Compile graph failed");
-          return null;
+      MappedByteBuffer modelBuffer = loadModel(mContext, IMAGESEGMENTATIONMODEL);
+      if(modelBuffer == null) {
+          Log.e(TAG, "Load model failed");
+          return;
+      }
+      // build model.
+      boolean ret = model.build(modelBuffer, ModelType.MT_MINDIR,context);
+      if(!ret) {
+          Log.e(TAG, "Build model failed");
       }
       ```
 
@@ -168,16 +172,15 @@ The inference code and process are as follows. For details about the complete co
       inTensor.setData(contentArray);
       ```
 
-3. Run the session and execute the computational graph.
+3. Run the model and execute the computational graph.
 
     ```java
     // Run graph to infer results.
-    boolean ret = model.predict();
-    if (!ret) {
-        Log.e(TAG, "MindSpore Lite run failed.");
-        return false;
+    if (!model.predict()) {
+        Log.e(TAG, "Run graph failed");
+        return null;
     }
-     ```
+    ```
 
 4. Process the output data.
 
diff --git a/docs/lite/docs/source_en/quick_start/quick_start.md b/docs/lite/docs/source_en/quick_start/quick_start.md
index 2214120466d918599308acc1e4e2c72e26b00e43..58a9e2113a13a481b26194993e24712a602a54d4 100644
--- a/docs/lite/docs/source_en/quick_start/quick_start.md
+++ b/docs/lite/docs/source_en/quick_start/quick_start.md
@@ -208,7 +208,7 @@ Call MindSpore Lite C++ APIs at the JNI layer to implement on-device inference.
 
 The inference process code is as follows. For details about the complete code, see [MindSporeNetnative.cpp](https://gitee.com/mindspore/models/blob/master/official/lite/image_classification/app/src/main/cpp/MindSporeNetnative.cpp).
 
-1. Load the MindSpore Lite model file and build the context, session, and computational graph for inference.  
+1. Load the MindSpore Lite model file and build the context, model, and computational graph for inference.  
 
     - Load model file:
 
@@ -229,9 +229,9 @@ The inference process code is as follows. For details about the complete code, s
         }
         ```
 
-    - Build context, session, and computational graph for inference:
+    - Build context, model, and computational graph for inference:
 
-        Build context and set session parameters. Create a session from context and model data.
+        Build context and set model parameters. Create a model from context and model data.
 
         ```cpp
         // To create a Mindspore network inference environment.
@@ -239,139 +239,119 @@ The inference process code is as follows. For details about the complete code, s
         MSNetWork *labelNet = new MSNetWork;
         *labelEnv = labelNet;
 
-        mindspore::lite::Context *context = new mindspore::lite::Context;
-        context->thread_num_ = num_thread;
-        context->device_list_[0].device_info_.cpu_device_info_.cpu_bind_mode_ = mindspore::lite::NO_BIND;
-        context->device_list_[0].device_info_.cpu_device_info_.enable_float16_ = false;
-        context->device_list_[0].device_type_ = mindspore::lite::DT_CPU;
+        auto context = std::make_shared<mindspore::Context>();
+        if (context == nullptr) {
+            MS_PRINT("context create failed!");
+            delete labelNet;
+            delete labelEnv;
+            return (jlong) nullptr;
+        }
 
-        labelNet->CreateSessionMS(modelBuffer, bufferLen, context);
-        delete context;
+        context->SetThreadNum(num_thread);
+        context->SetThreadAffinity(0);
+        auto &device_list = context->MutableDeviceInfo();
+        auto cpuDeviceInfo = std::make_shared<mindspore::CPUDeviceInfo>();
+        cpuDeviceInfo->SetEnableFP16(false);
+        device_list.push_back(cpuDeviceInfo);
         ```
 
     - Based on the model file `modelBuffer`, the computational graph for inference is constructed.
 
         ```cpp
-       void MSNetWork::CreateSessionMS(char *modelBuffer, size_t bufferLen, mindspore::lite::Context *ctx) {
-           session_ = mindspore::session::LiteSession::CreateSession(ctx);
-           if (session_ == nullptr) {
-               MS_PRINT("Create Session failed.");
-               return;
-           }
-
-           // Compile model.
-           model_ = mindspore::lite::Model::Import(modelBuffer, bufferLen);
-           if (model_ == nullptr) {
-               ReleaseNets();
-               MS_PRINT("Import model failed.");
-               return;
-           }
-
-           int ret = session_->CompileGraph(model_);
-           if (ret != mindspore::lite::RET_OK) {
-                ReleaseNets();
-                MS_PRINT("CompileGraph failed.");
-                return;
-           }
-         }
+        bool MSNetWork::BuildModel(char *modelBuffer, size_t bufferLen,
+                                   std::shared_ptr<mindspore::Context> ctx) {
+        model_ = std::make_shared<mindspore::Model>();
+        if (model_ == nullptr) {
+            MS_PRINT("MindSpore build model failed!.");
+            return false;
+        }
+        auto ret = model_->Build(modelBuffer, bufferLen, mindspore::ModelType::kMindIR, ctx);
+        return ret.IsOk();
+        }
         ```
 
 2. Convert the input image into the Tensor format of the MindSpore model.
 
-    Cut the size of the image `srcbitmap` to be detected and convert it to LiteMat format `lite_norm_mat_cut`. The width, height and channel number information are converted into float format data `dataHWC`. Finally, copy the `dataHWC` to the input `inTensor` of MindSpore model.
-
-    ```cpp
-    if (!BitmapToLiteMat(env, srcBitmap, &lite_mat_bgr)) {
-          MS_PRINT("BitmapToLiteMat error");
-          return NULL;
-       }
-      if (!PreProcessImageData(lite_mat_bgr, &lite_norm_mat_cut)) {
-          MS_PRINT("PreProcessImageData error");
-          return NULL;
-       }
-
-      ImgDims inputDims;
-      inputDims.channel = lite_norm_mat_cut.channel_;
-      inputDims.width = lite_norm_mat_cut.width_;
-      inputDims.height = lite_norm_mat_cut.height_;
-
-      // Get the MindSpore inference environment which created in loadModel().
-      void **labelEnv = reinterpret_cast<void **>(netEnv);
-      if (labelEnv == nullptr) {
-          MS_PRINT("MindSpore error, labelEnv is a nullptr.");
-          return NULL;
-       }
-      MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
-
-      auto mSession = labelNet->session();
-      if (mSession == nullptr) {
-          MS_PRINT("MindSpore error, Session is a nullptr.");
-          return NULL;
+    - Cut the size of the image `srcbitmap` to be detected and convert it to LiteMat format `lite_norm_mat_cut`. The width, height and channel number information are converted into float format data `dataHWC`. Finally, copy the `dataHWC` to the input `inTensor` of MindSpore model.
+
+        ```cpp
+        void **labelEnv = reinterpret_cast<void **>(netEnv);
+        if (labelEnv == nullptr) {
+            MS_PRINT("MindSpore error, labelEnv is a nullptr.");
+            return NULL;
         }
-       MS_PRINT("MindSpore get session.");
+        MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
 
-       auto msInputs = mSession->GetInputs();
-       if (msInputs.size() == 0) {
-          MS_PRINT("MindSpore error, msInputs.size() equals 0.");
-          return NULL;
+        auto mModel = labelNet->model();
+        if (mModel == nullptr) {
+            MS_PRINT("MindSpore error, Model is a nullptr.");
+            return NULL;
+        }
+        MS_PRINT("MindSpore get model.");
+
+        auto msInputs = mModel->GetInputs();
+        if (msInputs.empty()) {
+            MS_PRINT("MindSpore error, msInputs.size() equals 0.");
+            return NULL;
         }
         auto inTensor = msInputs.front();
 
         float *dataHWC = reinterpret_cast<float *>(lite_norm_mat_cut.data_ptr_);
         // Copy dataHWC to the model input tensor.
-        memcpy(inTensor->MutableData(), dataHWC,
-           inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
-   ```
-
-   Adjust the size of the input image, as well as the detailed algorithm of data processing.
-
-   ```cpp
-   bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) {
-         bool ret = false;
-         LiteMat lite_mat_resize;
-         LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr;
-         ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 256, 256);
-         if (!ret) {
+        memcpy(inTensor.MutableData(), dataHWC,
+                inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
+        ```
+
+    - Adjust the size of the input image, as well as the detailed algorithm of data processing.
+
+        ```cpp
+        bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) {
+            bool ret = false;
+            LiteMat lite_mat_resize;
+            LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr;
+            ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 256, 256);
+            if (!ret) {
             MS_PRINT("ResizeBilinear error");
             return false;
-         }
-         LiteMat lite_mat_convert_float;
-         ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0);
-         if (!ret) {
-             MS_PRINT("ConvertTo error");
-             return false;
-         }
-         LiteMat lite_mat_cut;
-         ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224);
-         if (!ret) {
-             MS_PRINT("Crop error");
-             return false;
-         }
-         std::vector<float> means = {0.485, 0.456, 0.406};
-         std::vector<float> stds = {0.229, 0.224, 0.225};
-         SubStractMeanNormalize(lite_mat_cut, lite_norm_mat_cut, means, stds);
-         return true;
+            }
+            LiteMat lite_mat_convert_float;
+            ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0);
+            if (!ret) {
+                MS_PRINT("ConvertTo error");
+                return false;
+            }
+            LiteMat lite_mat_cut;
+            ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224);
+            if (!ret) {
+                MS_PRINT("Crop error");
+                return false;
+            }
+            std::vector<float> means = {0.485, 0.456, 0.406};
+            std::vector<float> stds = {0.229, 0.224, 0.225};
+            SubStractMeanNormalize(lite_mat_cut, lite_norm_mat_cut, means, stds);
+            return true;
         }
-   ```
+        ```
 
 3. The input tensor is inferred according to the model, and the output tensor is obtained and post processed.
 
    - The graph and model are loaded and on device inference is performed.
 
         ```cpp
+        std::vector<mindspore::MSTensor> outputs;
         // After the model and image tensor data is loaded, run inference.
-        auto status = mSession->RunGraph();
+        auto status = mModel->Predict(msInputs, &outputs);
         ```
 
    - Get the tensor output `msOutputs` of MindSpore model. The text information `resultCharData` displayed in the APP is calculated through `msOutputs` and classification array information.
 
         ```cpp
-       auto names = mSession->GetOutputTensorNames();
-        std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs;
+        auto names = mModel->GetOutputTensorNames();
+        std::unordered_map<std::string, mindspore::MSTensor> msOutputs;
         for (const auto &name : names) {
-            auto temp_dat =mSession->GetOutputByTensorName(name);
-            msOutputs.insert(std::pair<std::string, mindspore::tensor::MSTensor *> {name, temp_dat});
-          }
+            auto temp_dat = mModel->GetOutputByTensorName(name);
+            msOutputs.insert(std::pair<std::string, mindspore::MSTensor>{name, temp_dat});
+        }
         std::string resultStr = ProcessRunnetResult(::RET_CATEGORY_SUM,::labels_name_map, msOutputs);
 
         const char *resultCharData = resultStr.c_str();
@@ -381,50 +361,55 @@ The inference process code is as follows. For details about the complete code, s
    - Perform post-processing of the output data. Obtain the output object `outputTensor` through `msOutputs`, and parse it with the thing category array `labels_name_map` to obtain the training score array `scores[]` of each element. Set the credibility threshold value to `unifiedThre`, and count the credibility threshold value according to the training data. Above the threshold, it belongs to this type. On the contrary, it is not. Finally, a corresponding category name and corresponding score data `categoryScore` are returned.
 
         ```cpp
-         std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[], std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs) {
+        std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[],
+                                        std::unordered_map<std::string, mindspore::MSTensor> msOutputs) {
         // Get the branch of the model output.
         // Use iterators to get map elements.
-        std::unordered_map<std::string, mindspore::tensor::MSTensor *>::iterator iter;
+        std::unordered_map<std::string, mindspore::MSTensor>::iterator iter;
         iter = msOutputs.begin();
 
         // The mobilenetv2.ms model output just one branch.
         auto outputTensor = iter->second;
 
-        int tensorNum = outputTensor->ElementsNum();
+        int tensorNum = outputTensor.ElementNum();
         MS_PRINT("Number of tensor elements:%d", tensorNum);
 
         // Get a pointer to the first score.
-        float *temp_scores = static_cast<float *>(outputTensor->MutableData());
+        float *temp_scores = static_cast<float *>(outputTensor.MutableData());
         float scores[RET_CATEGORY_SUM];
         for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
-             scores[i] = temp_scores[i];
+            scores[i] = temp_scores[i];
         }
 
         const float unifiedThre = 0.5;
         const float probMax = 1.0;
         for (size_t i = 0; i < RET_CATEGORY_SUM; ++i) {
-             float threshold = g_thres_map[i];
-             float tmpProb = scores[i];
-        if (tmpProb < threshold) {
+            float threshold = g_thres_map[i];
+            float tmpProb = scores[i];
+            if (tmpProb < threshold) {
             tmpProb = tmpProb / threshold * unifiedThre;
-        } else {
+            } else {
             tmpProb = (tmpProb - threshold) / (probMax - threshold) * unifiedThre + unifiedThre;
+            }
+            scores[i] = tmpProb;
         }
-        scores[i] = tmpProb;
+
+        for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
+            if (scores[i] > 0.5) {
+            MS_PRINT("MindSpore scores[%d] : [%f]", i, scores[i]);
+            }
         }
 
-         // Score for each category.
-         // Converted to text information that needs to be displayed in the APP.
-         std::string categoryScore = "";
-         for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
-             categoryScore += labels_name_map[i];
-             categoryScore += ":";
-             std::string score_str = std::to_string(scores[i]);
-             categoryScore += score_str;
-             categoryScore += ";";
-          }
-         return categoryScore;
-      }
-       ```
-
-   ​
+        // Score for each category.
+        // Converted to text information that needs to be displayed in the APP.
+        std::string categoryScore = "";
+        for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
+            categoryScore += labels_name_map[i];
+            categoryScore += ":";
+            std::string score_str = std::to_string(scores[i]);
+            categoryScore += score_str;
+            categoryScore += ";";
+        }
+        return categoryScore;
+        }
+        ```
diff --git a/docs/lite/docs/source_en/use/benchmark_tool.md b/docs/lite/docs/source_en/use/benchmark_tool.md
index e193bd3a50585a79dc5fae1dbb68ecab71fa4181..f9cd0cbcd360f923f3b4fe040d22bae7a32ab0f3 100644
--- a/docs/lite/docs/source_en/use/benchmark_tool.md
+++ b/docs/lite/docs/source_en/use/benchmark_tool.md
@@ -268,7 +268,7 @@ np.fromfile("/path/to/dump.bin", np.float32)
 
 To use the Benchmark tool, you need to prepare the environment as follows:
 
-- Compilation: Install build dependencies and perform build. The code of the Benchmark tool is stored in the `mindspore/lite/tools/benchmark` directory of the MindSpore source code. For details about the build operations, see the [Environment Requirements](https://www.mindspore.cn/lite/docs/en/master/use/build.html#id1) and [Compilation Example](https://www.mindspore.cn/lite/docs/en/master/use/build.html#id3) in the build document.
+- Compilation: Install build dependencies and perform build. The code of the Benchmark tool is stored in the `mindspore/lite/tools/benchmark` directory of the MindSpore source code. For details about the build operations, see the [Environment Requirements](https://www.mindspore.cn/lite/docs/en/master/use/build.html#environment-requirements) and [Compilation Example](https://www.mindspore.cn/lite/docs/en/master/use/build.html#compilation-example) in the build document.
 - Add the path of dynamic library required by the benchmark to the environment variables PATH.
 
     ````bash
diff --git a/docs/lite/docs/source_en/use/build.md b/docs/lite/docs/source_en/use/build.md
index 1fbd9c430ac728812721f62cdd753704e8780044..9f632725465ce79accbc660ad25176cad9970077 100644
--- a/docs/lite/docs/source_en/use/build.md
+++ b/docs/lite/docs/source_en/use/build.md
@@ -82,11 +82,15 @@ The construction of modules is controlled by environment variables. Users can co
 | MSLITE_ENABLE_TRAIN | Whether to compile the training version | on, off | on |
 | MSLITE_ENABLE_SSE | Whether to enable SSE instruction set, only valid when `-I x86_64` | on, off | off |
 | MSLITE_ENABLE_AVX | Whether to enable AVX instruction set, only valid when `-I x86_64` | on, off | off |
+| MSLITE_ENABLE_AVX512 | Whether to enable AVX512 instruction set, only valid when `-I x86_64` | on, off | off |
 | MSLITE_ENABLE_CONVERTER | Whether to compile the model conversion tool, only valid when `-I x86_64` | on, off | on |
 | MSLITE_ENABLE_TOOLS | Whether to compile supporting tools | on, off | on |
 | MSLITE_ENABLE_TESTCASES | Whether to compile test cases | on, off | off |
+| MSLITE_ENABLE_SERVER_INFERENCE | Whether to enable inference interfaces on server | on, off | off |
 
 > - For TensorRT and NPU compilation environment configuration, refer to [Application Specific Integrated Circuit Integration Instructions](https://www.mindspore.cn/lite/docs/en/master/use/asic.html).
+> - When the AVX instruction set is enabled, the CPU of the running environment needs to support both AVX and FMA features.
+> - The compilation time of the model conversion tool is long. If it is not necessary, it is recommended to use `MSLITE_ENABLE_CONVERTER` to turn off the compilation of the conversion tool to speed up the compilation.
 
 - Runtime feature compilation options
 
@@ -245,11 +249,13 @@ The script `build.bat` in the root directory of MindSpore can be used to compile
 | -------- | ----- | ---- | ---- |
 | MSLITE_ENABLE_SSE | Whether to enable SSE instruction set | on, off | off |
 | MSLITE_ENABLE_AVX | Whether to enable AVX instruction set (This option does not currently support the Visual Studio compiler) | on, off | off |
+| MSLITE_ENABLE_AVX512 | Whether to enable AVX512 instruction set (This option does not currently support the Visual Studio compiler) | on, off | off |
 | MSLITE_ENABLE_CONVERTER | Whether to compile the model conversion tool (This option does not currently support the Visual Studio compiler) | on, off | on |
 | MSLITE_ENABLE_TOOLS | Whether to compile supporting tools | on, off | on |
 | MSLITE_ENABLE_TESTCASES | Whether to compile test cases | on, off | off |
 
 > - The above options can be modified by setting the environment variable with the same name or the file `mindspore/lite/CMakeLists.txt`.
+> - The compilation time of the model conversion tool is long. If it is not necessary, it is recommended to use `MSLITE_ENABLE_CONVERTER` to turn off the compilation of the conversion tool to speed up the compilation.
 
 ### Compilation Example
 
diff --git a/docs/lite/docs/source_en/use/downloads.md b/docs/lite/docs/source_en/use/downloads.md
index e5dcc5bd8d367ff842b6604db4d4e0b3f26fc159..81adb14431c9a6299e138269294fb761bee1a0d0 100644
--- a/docs/lite/docs/source_en/use/downloads.md
+++ b/docs/lite/docs/source_en/use/downloads.md
@@ -6,6 +6,22 @@
 
 Welcome to MindSpore Lite. We provide functions such as model conversion, model inference, image processing, etc. that support multiple operating systems and hardware platforms. You can download the version package suitable for the local environment and use it directly.
 
+The Linux-x86_64 target running system is Linux, and the underlying architecture is x86-64. It has been tested and verified on the Linux distribution versions Euleros2.0, Centos7.8 and Ubuntu18.04.
+
+## 1.6.0
+
+|  Module Name  | Hardware Platform |  Operating System  | Download Links |   SHA-256   |
+|      ---      |       ---         |         ---        |      ---       |    ---      |
+| Inference/training runtime, inference/training aar package, and benchmark tools | CPU     | Android-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/android/cpu/mindspore-lite-1.6.0-android-aarch32.tar.gz> | d043803cffc8a0b75409aab3e4039f1e86756cf618af1538a76865e9fa4fd481 |
+| Inference/training runtime, inference/training aar package, and benchmark tools | CPU/GPU | Android-aarch64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/android/gpu/mindspore-lite-1.6.0-android-aarch64.tar.gz> | 25188266621f4cfedb24970a9a98ef6190fe02c9d034b7285f360da425ffe9d6 |
+| Inference/training runtime, inference/training jar package, and benchmark/codegen/converter/cropper tools | CPU     | Linux-x86_64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/linux/x86_64/mindspore-lite-1.6.0-linux-x64.tar.gz> | 90472996359f64509f38036ed8100605c76dcdc42453c2fc7156048eb981708c |
+| Inference runtime and benchmark/codegen/converter tools | CPU     | Windows-x86_64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/windows/mindspore-lite-1.6.0-win-x64.zip> | 4460b8f1bf321eca005074dccffb54d6d3164ba3f78ce34530ec20db4dbc9980 |
+| iOS inference runtime | CPU | iOS-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/ios/mindspore-lite-1.6.0-ios-aarch32.tar.gz> | 72fe007660abe9c51d0a1852b094fb52d8bbd1610c989e79c9858937102aa59f |
+| iOS inference runtime | CPU | iOS-aarch64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/ios/mindspore-lite-1.6.0-ios-aarch64.tar.gz> | 51bd5f7c21477d7856bea33d31e059f578b6b964a7c43e440e97c44b186db4a4 |
+| NNIE converter tool | CPU     | Linux-x86_64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/linux/nnie/mindspore-lite-1.6.0-linux-x64.tar.gz> | 81c2a5dadf51978d1c80f75c63fde4edefa2897792ac571fd33ffd35e338736b |
+| NNIE inference runtime and benchmark tools | Hi3516D | Linux-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/linux/nnie/Hi3516D/mindspore-lite-1.6.0-linux-aarch32.tar.gz> | 8133c2326e2defa3614f86592d5691fdb410a4296898e254a33cd33a7e519b16 |
+| HarmonyOS lite runtime | Hi3516D | OpenHarmony-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/ohos_aarch32/mindspore-lite-1.6.0-ohos-aarch32.tar.gz> | d5daafac4bdcd0d03158e2a7cd3f881869b49cfb77d9654a24ddd967edbe5e91 |
+
 ## 1.5.0
 
 |  Module Name  | Hardware Platform |  Operating System  | Download Links |   SHA-256   |
diff --git a/docs/lite/docs/source_en/use/nnie.md b/docs/lite/docs/source_en/use/nnie.md
index e04b5945df352fcf1b06ccba2abe44e6efcf95a1..458574b3df146f6d48266ce8d9c74443233213f7 100644
--- a/docs/lite/docs/source_en/use/nnie.md
+++ b/docs/lite/docs/source_en/use/nnie.md
@@ -37,7 +37,7 @@ mindspore-lite-{version}-linux-aarch32
         └── libmslite_proposal.so  # Sample dynamic library of the integrated proposal
 ```
 
-The preceding shows the integration directory structure of the NNIE. For details about other directory structures of the inference tool runtime, see [Directory Structure](https://www.mindspore.cn/lite/docs/en/master/use/build.html#id4).
+The preceding shows the integration directory structure of the NNIE. For details about other directory structures of the inference tool runtime, see [Directory Structure](https://www.mindspore.cn/lite/docs/en/master/use/build.html#directory-structure).
 
 ## Using Tools
 
@@ -142,7 +142,7 @@ The following is an example of the `nnie.cfg` file:
      CONVERTER RESULT SUCCESS:0
      ```
 
-     For details about the parameters of the converter_lite conversion tool, see ["Parameter Description" in Converting Models for Inference](https://www.mindspore.cn/lite/docs/en/master/use/converter_tool.html#id4).
+     For details about the parameters of the converter_lite conversion tool, see ["Parameter Description" in Converting Models for Inference](https://www.mindspore.cn/lite/docs/en/master/use/converter_tool.html#example).
 
 ### Runtime
 
diff --git a/docs/lite/docs/source_zh_cn/conf.py b/docs/lite/docs/source_zh_cn/conf.py
index 903a0ea9f2e7efc1e79e1056252d484a8754a3d8..58ce41ec652bb5764c6b6fcb6111e854409513de 100644
--- a/docs/lite/docs/source_zh_cn/conf.py
+++ b/docs/lite/docs/source_zh_cn/conf.py
@@ -11,6 +11,7 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import sys
 from sphinx.search import jssplitter as sphinx_split
 from sphinx import errors as searchtools_path
@@ -57,6 +58,13 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 #
 html_theme = 'sphinx_rtd_theme'
+#modify layout.html for sphinx_rtd_theme.
+import sphinx_rtd_theme
+layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
+layout_src = '../../../../resource/_static/layout.html'
+if os.path.exists(layout_target):
+    os.remove(layout_target)
+shutil.copy(layout_src, layout_target)
 
 html_search_language = 'zh'
 
@@ -64,6 +72,9 @@ html_search_options = {'dict': '../../../resource/jieba.txt'}
 
 html_static_path = ['_static']
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+
 sys.path.append(os.path.abspath('../../../../resource/custom_directives'))
 from custom_directives import IncludeCodeDirective
 
diff --git a/docs/lite/docs/source_zh_cn/index.rst b/docs/lite/docs/source_zh_cn/index.rst
index 9a097d1b09ef713dc350ddf3f53f1af461da40a2..06a8289e45cbc82159b0b5ca0a7a31a66cae5011 100644
--- a/docs/lite/docs/source_zh_cn/index.rst
+++ b/docs/lite/docs/source_zh_cn/index.rst
@@ -201,7 +201,7 @@ MindSpore Lite文档
 									</div>
 								</a>
 							</div>
-							<div class="doc-article-item all os-Android language-cpp stage-Whole-Process stage-Model-Converting stage-Model-Loading stage-Data-Preparation stage-Inference user-Beginner user-Intermediate user-Expert hidden">
+							<div class="doc-article-item all os-Windows os-Linux os-Android language-cpp stage-Whole-Process stage-Model-Converting stage-Model-Loading stage-Data-Preparation stage-Inference user-Beginner user-Intermediate user-Expert hidden">
 								<a href="https://www.mindspore.cn/lite/docs/zh-CN/master/quick_start/one_hour_introduction.html" class="article-link">
 									<div>
 										<div class="doc-article-head">
diff --git a/docs/lite/docs/source_zh_cn/operator_list_lite.md b/docs/lite/docs/source_zh_cn/operator_list_lite.md
index 00143e871b5dfad1f6f30a37cedf4e38cdd13745..35879b6f4a1206c1e75e8e9225922cb9202961ff 100644
--- a/docs/lite/docs/source_zh_cn/operator_list_lite.md
+++ b/docs/lite/docs/source_zh_cn/operator_list_lite.md
@@ -6,187 +6,190 @@
 
 本文列举MindSpore Lite支持的算子。
 
-| 操作名 <br/>&nbsp; | CPU<br/>FP16 | CPU<br/>FP32 | CPU<br/>Int8 | CPU<br/>UInt8 | GPU<br/>FP16 | GPU<br/>FP32 | NPU<br/>&nbsp;  |  TensorRT<br/>&nbsp;  |支持的TensorFlow Lite算子 | 支持的Caffe算子 | 支持的Onnx算子 | 支持的TensorFlow算子 | 支持的Ascend310算子 |
-| --------------------- | :------------: | :------------: | :------------: | :-------------: | :------------: | :------------: | :---------: | :---------: | ------------------------------- | ------------------------ | ----------------------------------------------- | ----------------------------------------------- | ----------------------------------------------- |
-| Abs                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Abs                             |                          | Abs                                             | Abs                                             | Abs |
-| Add                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Add                             |                          | Add, Int8Add                                    | Add, AddV2                                      | Add |
-| Adder                 |       | ✅    |      |        |       |       |    |     |                                 |                          | adder_f                                |                                                 | |
-| AddGrad |  | ✅ |  |  |  |  |  | | | |  |  |
-| AddN                  |   ✅         | ✅    |              |               |              |              |          | | AddN                            |                          |                                                 |                                                 |  |
-| Assert                |  ✅  | ✅    |              |               |              |              |           |                           |      |                          |                                                 | Assert                                          |  |
-| Argmax                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Argmax                          | ArgMax                   | ArgMax                                          | ArgMax                                          | ArgMax |
-| Argmin                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |           | | Argmin                          |                          |                                                 | ArgMin                                          | ArgMin |
-| AvgPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | MeanPooling                     | Pooling                  | AveragePool,<br/>GlobalAveragePool,<br/>Int8AveragePool | AvgPool                                         | AvgPool |
-| AvgPoolGrad | ✅ | ✅ |  |  |  |  |  | |  |  |  |  |
-| BatchNorm             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |                               |  | BatchNorm                | BatchNormalization                              |                                                 | BatchNorm |
-| BatchNormGrad | ✅ | ✅ |  |  |  |  | | | |  |  | | |
-| BatchToSpace          |              | ✅    | ✅    | ✅     | ✅    | ✅    |          | | BatchToSpace,<br/>BatchToSpaceND    |                          |                                                 | BatchToSpace,<br/>BatchToSpaceND                    |  |
-| BiasAdd               | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ |                      |    | BiasAdd                                         | BiasAdd                                         | BiasAdd |
-| BiasAddGrad | ✅ | ✅ |  |  |  |  | | | | |  |  | |
-| BroadcastTo | ✅ | ✅ | | | | | | | BroadcastTo |  | Expand | BroadcastTo | |
-| Cast                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Cast,<br/>QUANTIZE,<br/>DEQUANTIZE      |                          | Cast                                            | Cast                                            | Cast |
-| Ceil                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Ceil                            |                          | Ceil                                            | Ceil                                            | Ceil |
-| Concat                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Concat                          | Concat                   | Concat                                          | ConcatV2                                        | Concat |
-| ConstantOfShape       |   ✅  | ✅    |              |               |              |              |           |                                 |                      |    | ConstantOfShape                                 |                                                 |  |
-| Conv2d                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ |  Conv2D                          | Convolution              | Conv, Int8Conv,<br/>ConvRelu,<br/>Int8ConvRelu          | Conv2D                                          | Conv2D |
+| 操作名 <br/>&nbsp; | CPU<br/>FP16 | CPU<br/>FP32 | CPU<br/>Int8 | CPU<br/>UInt8 | GPU<br/>FP16 | GPU<br/>FP32 | NPU<br/>&nbsp;  |  TensorRT<br/>&nbsp;  | Ascend<br/>(Ascend310) |支持的TensorFlow Lite算子 | 支持的Caffe算子 | 支持的Onnx算子 | 支持的TensorFlow算子 |
+| --------------------- | :------------: | :------------: | :------------: | :-------------: | :------------: | :------------: | :---------: | :---------: | :-----------------------------: | ------------------------ | ----------------------------------------------- | ----------------------------------------------- | --------------------- |
+| Abs                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                       | Abs                             |                          | Abs                                             | Abs                                             |
+| Add                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                            | Add                             |                          | Add, Int8Add                                    | Add, AddV2                                      |
+| Adder                 |       | ✅    |      |        |       |       |    |     |                                 |                                 |                          | adder_f                                |                                                 |
+| AddGrad |  | ✅ |  |  |  |  |  | | | | |  |  |
+| AddN                  |   ✅         | ✅    |              |               |              |              |          | |                             | AddN                            |                          |                                                 |                                                 |
+| Assert                |  ✅  | ✅    |              |               |              |              |           |                           |      |      |                          |                                                 | Assert                                          |
+| Argmax                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                         | Argmax                          | ArgMax                   | ArgMax                                          | ArgMax                                          |
+| Argmin                |  ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |           | | ✅                         | Argmin                          |                          |                                                 | ArgMin                                          |
+| AvgPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                    | MeanPooling                     | Pooling                  | AveragePool,<br/>GlobalAveragePool,<br/>Int8AveragePool | AvgPool                                         |
+| AvgPoolGrad | ✅ | ✅ |  |  |  |  |  | |  |  |  |  |  |
+| BatchNorm             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |                               | ✅ |  | BatchNorm                | BatchNormalization                              |                                                 |
+| BatchNormGrad | ✅ | ✅ |  |  |  |  | | | | |  |  | |
+| BatchToSpace          |              | ✅    | ✅    | ✅     | ✅    | ✅    |          | |     | BatchToSpace,<br/>BatchToSpaceND    |                          |                                                 | BatchToSpace,<br/>BatchToSpaceND                    |
+| BiasAdd               | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅ | ✅ |                      |    | BiasAdd                                         | BiasAdd                                         |
+| BiasAddGrad | ✅ | ✅ |  |  |  |  | | | | | |  |  |
+| BroadcastTo | ✅ | ✅ | | | | | | |  | BroadcastTo |  | Expand | BroadcastTo |
+| Cast                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅     | Cast,<br/>QUANTIZE,<br/>DEQUANTIZE      |                          | Cast                                            | Cast                                            |
+| Ceil                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                           | Ceil                            |                          | Ceil                                            | Ceil                                            |
+| Concat                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                         | Concat                          | Concat                   | Concat                                          | ConcatV2                                        |
+| ConstantOfShape       |   ✅  | ✅    |              |               |              |              |           |                                 |                      |                      |    | ConstantOfShape                                 |                                                 |
+| Conv2d                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ |  ✅                         |  Conv2D                          | Convolution              | Conv, Int8Conv,<br/>ConvRelu,<br/>Int8ConvRelu          | Conv2D                                          |
 | Conv2DBackpropFilterFusion | ✅ | ✅ |  |   |  |  |  |  |  |  |  |  |  |
 | Conv2DBackpropInputFusion | ✅ | ✅ |  |   |  |  |  |  |  |  |  |  |  |
-| Conv2dGrad |  | ✅ |  |  |  |  |  |  | | |  |  |  |
-| Conv2dTranspose       | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | DeConv2D                        | Deconvolution            | ConvTranspose                                   | Conv2DBackpropInput                             | Conv2dTranspose |
-| Conv2dTransposeGrad |  | ✅ |  |  |  |  |  | | |  |  |  |  |  |
-| Cos                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |Cos                             |                          | Cos                                             | Cos                                             | Cos |
-| Crop                  | ✅    | ✅    | ✅    | ✅     |              |              |           |                              |   | Crop                     |                                                 |                                                 |  |
-| CropAndResize         |              | ✅    |              |               |              |              | ✅         |                                 |                          |                                      |           | CropAndResize                                   |  |
-| CumSum                |              | ✅    |             |               |              |              |           |                  |                          |                                             |    |        Cumsum                                         |  |
-| CustomExtractFeatures |              | ✅    |              |               |              |              |         |  | ExtractFeatures                 |                          |                                                 |                                                 |  |
-| CustomNormalize       |              | ✅    |              |               |              |              |          | | Normalize                       |                          |                                                 |                                                 |  |
-| CustomPredict         |              | ✅    |              |               |              |              |          | | Predict                         |                          |                                                 |                                                 |  |
-| DeDepthwiseConv2D     |              | ✅    | ✅    | ✅     | ✅ | ✅ |           |                              |   | Deconvolution            |                                                 |                                                 | Deconvolution |
-| DepthToSpace          |  ✅  | ✅   | ✅    | ✅     | ✅    | ✅    |          | | DepthToSpace                    |                          | DepthToSpace                                    | DepthToSpace                                    |  |
-| DepthwiseConv2dNative | ✅    | ✅    | ✅    | ✅     |     |     | ✅ | | DepthwiseConv2D                 | Convolution              |                                                 | DepthwiseConv2dNative                           | DepthwiseConv2dNative |
-| DetectionPostProcess  |              | ✅    | ✅    | ✅     |              |              |          | | Custom                          |                          |                                                 |                                                 |  |
-| Div                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Div, RealDiv                    |                          | Div                                             | Div, RealDiv                                    | Div |
-| DivGrad |  | ✅ |  |  |  |  |  | | | |  |  |  |
-| DropoutGrad | ✅ | ✅ |  |  |  |  |  | | | |  |  |  |
-| Eltwise               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |                  ✅ |             |   Eltwise                  | Sum, Max<sup>[3]</sup>                          |                                                 | Eltwise |
-| Elu                   |   ✅   | ✅    |              |               |              |              |           |                            |     | ELU                      | Elu,<br/>NonMaxSuppression                          | NonMaxSuppressionV3                             | Elu |
-| EluGrad | | ✅ | | | | | | | | |  |  |  |
-| Equal                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Equal                           |                          | Equal                                           | Equal                                           | Equal |
-| ExpFusion             |   ✅  | ✅    |              |               | ✅    | ✅    |        |   | Exp                             | Exp                      | Exp                                             | Exp                                             |  |
-| ExpandDims            |   ✅   | ✅    | ✅    | ✅     | ✅ | ✅ | ✅       | ✅   | ExpandDims                      |                          |                                                 | ExpandDims                                      | ExpandDims |
-| Fill                  |   ✅   | ✅    |              |               | ✅ | ✅ |        |   | Fill                            |                          |                                                 | Fill                                            | Fill |
-| Flatten               |  ✅    | ✅    |              |               |              |              |           |            ✅                     |    | Flatten                  |                                                 |                                                 | Flatten |
-| FlattenGrad           |  ✅    | ✅    |    |    |   |   |  |   |   |   |    |    |  |
-| Floor                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | flOOR                           |                          | Floor                                           | Floor                                           | Floor |
-| FloorDiv              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | FloorDiv                        |                          |                                                 | FloorDiv                                        |  |
-| FloorMod              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | FloorMod                        |                          |                                                 | FloorMod                                        |  |
-| FullConnection        | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅ |  ✅ | FullyConnected                  | InnerProduct             |                                                 |                                                 | FullConnection |
-| FusedBatchNorm        | ✅    | ✅    | ✅    | ✅     |              |              | ✅ | | FusedBatchNorm                  |                          |                                                 | FusedBatchNorm,<br/>FusedBatchNormV3                | FusedBatchNorm |
-| GatherNd              |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           | | GatherND                        |                          |                                                 | GatherNd                                        | GatherNd |
-| Gather                |   ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  |  ✅ | Gather                          |                          | Gather                                          | GatherV2                                        | Gather |
-| Greater               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Greater                         |                          | Greater                                         | Greater                                         | Greater |
-| GreaterEqual          | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | GreaterEqual                    |                          |                                                 | GreaterEqual                                    | GreaterEqual |
-| GRU                   | ✅    | ✅    |              |               |              |              |           |            |                     |                          |                                                 |                                                 |  |
-| HardTanh              | ✅    | ✅    |              |               |              |              |           |              |                   |                          |                                                 |                                                 |  |
-| HashtableLookup       |              | ✅    |              |               |              |              |           | | HashtableLookup                 |                          |                                                 |                                                 |  |
-| HSigmoid              |  ✅  | ✅    |              | ✅     |              |              |           |              |                   |                          |                                                 |                                                 |  |
-| Hswish                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |  HardSwish                       |                          |                                                 |                                                 |  |
-| HswishGrad |  | ✅ |  |  |  |  | |  |  | | | |  |
-| InstanceNorm          | ✅ | ✅    |              |               |              |              | ✅        |  | InstanceNorm                    |                          | InstanceNormalization                           |                                                 |  |
-| InvertPermutation     |  ✅   | ✅    |              |               |              |              |           |                |                 |                          |                                                 | InvertPermutation                               |  |
-| L2Norm                |              | ✅    |   ✅  |               |              |              |       |    | L2_NORMALIZATION                |                          |                                                 |                                                 |  |
-| LayerNorm             | ✅    | ✅    | ✅    |               | ✅ | ✅ |           |                   |              |                          |                                                 |                                                 |  |
-| LayerNormGrad         | ✅    | ✅    |     |      |      |      |       |       |       |     |        |       |  |
-| LeakyReLU             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | LeakyRelu                       |                          | LeakyRelu                                       | LeakyRelu                                       | LeakyRelu |
-| LeakyReLUGrad |  | ✅ |  |  |  |  | |  |  | |  |  |  |
-| Less                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Less                            |                          | Less                                            | Less                                            |  |
-| LessEqual             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | LessEqual                       |                          |                                                 | LessEqual                                       |  |
-| LRN                   |              | ✅    |              |               |              |              |        |   | LocalResponseNorm               |                          | Lrn, LRN                                        |                                                 |  |
-| Log                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Log                             |                          | Log                                             | Log                                             |  |
-| LogGrad | ✅ | ✅ |  |  |  |  |  | | | |  |  | |
-| LogicalAnd            | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | LogicalAnd                      |                          | And                                             | LogicalAnd                                      |  |
-| LogicalNot            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | LogicalNot                      |                          | Not                                             | LogicalNot                                      |  |
-| LogicalOr             | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |  LogicalOr                       |                          | Or                                              | LogicalOr                                       |  |
-| LogSoftmax            | ✅    | ✅    |              |               |     |     |   | |  LogSoftmax        |                          | LogSoftmax                |                                        |  |
-| LshProjection         |              | ✅    |              |               |              |              |       |    | LshProjection                   |                          |                                                 |                                                 |  |
-| LSTM                  |    ✅   | ✅    |              |               |              |              |           |                                 |               |           | LSTM                                            |                                                 |  |
-| MatMul                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | BatchMatMul                     |                          | MatMul,<br/>Gemm                         | MatMul,<br/>BatchMatMul,<br/>BatchMatMulV2            | MatMul |
-| MatMulGrad |  | ✅ |  |  |  | |  |  | | |  |  |  |
-| Maximum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | Maximum                         |                          |  Max                                            | Maximum                                         | Maximum |
-| MaximumGrad | ✅ | ✅ | | |  |  |  |  | | | | |  |
-| MaxPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | MaxPooling                      | Pooling                  | MaxPool,<br/>GlobalMaxPool                          | MaxPool                                         | MaxPool |
-| MaxPoolGrad | ✅ | ✅ |  |  |  |  | | |  |  |  |  | |
-| Merge                 | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | Merge                                           |  |
-| Minimum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | Minimum                         |                          | Min                                             | Minimum                                         | Minimum |
-| MinimumGrad | ✅ | ✅ | | |  |  |  | |  | |  |  | |
-| Mul                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Mul                             |                          | Mul                                             | Mul                                             | Mul |
-| MulGrad |  | ✅ |  |  |  |  | |  |  | |  |  |  |
-| Neg                   | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | Neg                             |                          | Neg                                             |Neg                                              |  |
-| NegGrad | ✅ | ✅ | | |  |  | |  |  | |  | |  |
-| NotEqual              | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | NotEqual                        |                          |                                                 | NotEqual                                        |  |
-| OneHot |  ✅  | ✅    |              |               | ✅    | ✅    |       |    | OneHot                          |                          | OneHot                                          | OneHot                                          |  |
-| Pad                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Pad, MirrorPad, PadV2                  |                          | Pad                                             | MirrorPad, Pad, PadV2                                  | Pad |
-| Pow                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | Pow                             | Power                    | Pow<sup>[2]</sup>                               | Pow                                             | Pow |
-| PowGrad | | ✅ |  |  |  |  | |  |  | |  |  | |
-| PReLU  | ✅ | ✅ |  |  | ✅    | ✅    |  | | PRELU | PReLU | PRelu |   | PReLU |
-| QuantDTypeCast        | ✅ | ✅ | ✅ | ✅ |   |   |   |   |   |   |   |   |  |
-| RaggedRange        | ✅ | ✅ |   |   |   |   |   |   |   |   |   | RaggedRange |  |
-| RandomStandardNormal  |  ✅  | ✅    |              |               |              |              |       |    |                                 |                          |                                                 | RandomStandardNormal                            |  |
-| RandomUniform         |              | ✅    |              |               |              |              |        |   |                                 |                          |                                                 | RandomUniform                                   |  |
-| Range                 |  ✅  | ✅    |              |               |              |              |         |  | Range                           |                          | Range                                           | Range |  |
-| Rank                  |  ✅  | ✅    |              |               |              |              |         |  | Rank                            |                          |                                                 | Rank                                            |  |
-| RealDiv               | ✅ | ✅ |   |   |   |   |   |   |   |   |   |   | RealDiv |
-| Reciprocal            | ✅    | ✅    | ✅    |               |              |              | ✅ |              |                   |                          | Reciprocal                                      |                                                 |  |
-| ReduceAll             |              | ✅    |              |               |              |              |           |              |                   |                          |                                                 | All                                             |  |
-| ReduceASum            |  ✅  | ✅    |              |               | ✅    | ✅    |           |                |                 | Reduction                |                                                 |                                                 |  |
-| ReduceMax             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ReduceMax                       |                          | ReduceMax                                       | Max                                             |  |
-| ReduceMean            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | Mean                            | Reduction                | ReduceMean                                      | Mean                                            | ReduceMean |
-| ReduceMin             |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ReduceMin                       |                          | ReduceMin                                       | Min                                             |  |
-| ReduceProd            |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ReduceProd                      |                          | ReduceProd                                      | Prod                                            |  |
-| ReduceSum             |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅  | Sum                             | Reduction                | ReduceSum                                       | Sum                                             | ReduceSum |
-| ReduceSumSquare       | ✅ | ✅    | ✅    | ✅     |              |              |           |                      |           | Reduction                | ReduceSumSquare                                 |                                                 |  |
-| ReLU                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Relu                            | ReLU                     | Relu                                            | Relu                                            | ReLU |
-| ReLUGrad | ✅ | ✅ |  |  |  |  |  | | |  |  |  |  |
-| ReLU6                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Relu6                           | ReLU6                    | Clip<sup>[1]</sup>                              | Relu6                                           | ReLU6 |
-| ReLU6Grad | ✅ | ✅ |  |  |  |  |  |  | | |  |  |  |
-| Reshape               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Reshape                         | Reshape                  | Reshape,<br/>Flatten                                 | Reshape                                         | Reshape |
-| Resize |  ✅  | ✅  | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ResizeBilinear,<br/>NearestNeighbor | Interp |  Resize, Upsample | ResizeBilinear,<br/>ResizeBicubic,<br/>ResizeNearestNeighbor | Upsample,<br/>ResizeNearestNeighbor |
-| ResizeGrad | ✅ | ✅ |  |  |  |  | | |  |  | |  |  |
-| Reverse               |              | ✅    |              |               |              |              |       |    | reverse                         |                          |                                                 | ReverseV2                                       |  |
-| ReverseSequence       |              | ✅    |              |               |              |              |       |    | ReverseSequence                 |                          |                                                 | ReverseSequence                                 |  |
-| Round                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Round                           |                          | Round                                           | Round                                           | Round |
-| Rsqrt                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Rsqrt                           |                          |                                                 | Rsqrt                                           |  |
-| Select                |              | ✅    |              |               |              |              |           |                                 |                          |                                  |               | Select                                          |  |
-| Selu                  |              |              |              |               |              |              |           |                                 |                          |                       |                          | Selu                                            |  |
-| Scale                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅  | ✅ |                                | Scale                    |                                                 |                                                 | Scale |
-| ScatterNd | ✅ | ✅ |  |  |  |  |  |  | ScatterNd |  | ScatterND  |  |  |
-| ScatterNdUpdate | ✅ | ✅ |  |  |  |  |  |  | ScatterNdUpdate |  | ScatterNdUpdate  |  |  |
-| Shape                 |   ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |   | | Shape                           |                          | Shape                                           | Shape                                           | Shape |
-| Sigmoid               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  | | Logistic                        | Sigmoid                  | Sigmoid                                         | Sigmoid                                         | Sigmoid |
+| Conv2dGrad |  | ✅ |  |  |  |  |  |  | | | |  |  |
+| Conv2dTranspose       | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                       | DeConv2D                        | Deconvolution            | ConvTranspose                                   | Conv2DBackpropInput                             |
+| Conv2dTransposeGrad |  | ✅ |  |  |  |  |  | | | |  |  |  |
+| Cos                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |✅                             |Cos                             |                          | Cos                                             | Cos                                             |
+| Crop                  | ✅    | ✅    | ✅    | ✅     |              |              |           |                              |   |   | Crop                     |                                                 |                                                 |
+| CropAndResize         |              | ✅    |              |               |              |              | ✅         |                                 |                          |                          |                                      |           | CropAndResize                                   |
+| CumSum                |              | ✅    |             |               |              |              |           |                  |                          |                          |                                             |    |        Cumsum                                         |
+| CustomExtractFeatures |              | ✅    |              |               |              |              |         |  |                  | ExtractFeatures                 |                          |                                                 |                                                 |
+| CustomNormalize       |              | ✅    |              |               |              |              |          | |                        | Normalize                       |                          |                                                 |                                                 |
+| CustomPredict         |              | ✅    |              |               |              |              |          | |                          | Predict                         |                          |                                                 |                                                 |
+| Deconvolution | |  | | | | | | | ✅ |  | | | |
+| DeDepthwiseConv2D     |              | ✅    | ✅    | ✅     | ✅ | ✅ |           |                              |   |   | Deconvolution            |                                                 |                                                 |
+| DepthToSpace          |  ✅  | ✅   | ✅    | ✅     | ✅    | ✅    |          | |                     | DepthToSpace                    |                          | DepthToSpace                                    | DepthToSpace                                    |
+| DepthwiseConv2dNative | ✅    | ✅    | ✅    | ✅     |     |     | ✅ | | ✅                | DepthwiseConv2D                 | Convolution              |                                                 | DepthwiseConv2dNative                           |
+| DetectionPostProcess  |              | ✅    | ✅    | ✅     |              |              |          | |                           | Custom                          |                          |                                                 |                                                 |
+| Div                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                   | Div, RealDiv                    |                          | Div                                             | Div, RealDiv                                    |
+| DivGrad |  | ✅ |  |  |  |  |  | | | | |  |  |
+| DropoutGrad | ✅ | ✅ |  |  |  |  |  | | | | |  |  |
+| Eltwise               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |                  ✅ | ✅ |             |   Eltwise                  | Sum, Max<sup>[3]</sup>                          |                                                 |
+| Elu                   |   ✅   | ✅    |              |               |              |              |           |                            | ✅ |     | ELU                      | Elu,<br/>NonMaxSuppression                          | NonMaxSuppressionV3                             |
+| EluGrad | | ✅ | | | | | | | | | |  |  |
+| Equal                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                          | Equal                           |                          | Equal                                           | Equal                                           |
+| ExpFusion             |   ✅  | ✅    |              |               | ✅    | ✅    |        |   |                              | Exp                             | Exp                      | Exp                                             | Exp                                             |
+| ExpandDims            |   ✅   | ✅    | ✅    | ✅     | ✅ | ✅ | ✅       | ✅   | ✅                     | ExpandDims                      |                          |                                                 | ExpandDims                                      |
+| Fill                  |   ✅   | ✅    |              |               | ✅ | ✅ |        |   | ✅                           | Fill                            |                          |                                                 | Fill                                            |
+| Flatten               |  ✅    | ✅    |              |               |              |              |           |            ✅                     | ✅ |    | Flatten                  |                                                 |                                                 |
+| FlattenGrad           |  ✅    | ✅    |    |    |   |   |  |   |   |   |   |    |    |
+| Floor                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                          | flOOR                           |                          | Floor                                           | Floor                                           |
+| FloorDiv              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                         | FloorDiv                        |                          |                                                 | FloorDiv                                        |
+| FloorMod              | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                         | FloorMod                        |                          |                                                 | FloorMod                                        |
+| FullConnection        | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅ |  ✅ | ✅                 | FullyConnected                  | InnerProduct             |                                                 |                                                 |
+| FusedBatchNorm        | ✅    | ✅    | ✅    | ✅     |              |              | ✅ | | ✅                 | FusedBatchNorm                  |                          |                                                 | FusedBatchNorm,<br/>FusedBatchNormV3                |
+| GatherNd              |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           | | ✅                       | GatherND                        |                          |                                                 | GatherNd                                        |
+| Gather                |   ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  |  ✅ | ✅                         | Gather                          |                          | Gather                                          | GatherV2                                        |
+| Greater               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                        | Greater                         |                          | Greater                                         | Greater                                         |
+| GreaterEqual          | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                   | GreaterEqual                    |                          |                                                 | GreaterEqual                                    |
+| GRU                   | ✅    | ✅    |              |               |              |              |           |            |                     |                     |                          |                                                 |                                                 |
+| HardTanh              | ✅    | ✅    |              |               |              |              |           |              |                   |                   |                          |                                                 |                                                 |
+| HashtableLookup       |              | ✅    |              |               |              |              |           | |                  | HashtableLookup                 |                          |                                                 |                                                 |
+| HSigmoid              |  ✅  | ✅    |              | ✅     |              |              |           |              |                   |                   |                          |                                                 |                                                 |
+| Hswish                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                         |  HardSwish                       |                          |                                                 |                                                 |
+| HswishGrad |  | ✅ |  |  |  |  | |  |  |  | | | |
+| InstanceNorm          | ✅ | ✅    |              |               |              |              | ✅        |  |                     | InstanceNorm                    |                          | InstanceNormalization                           |                                                 |
+| InvertPermutation     |  ✅   | ✅    |              |               |              |              |           |                |                 |                 |                          |                                                 | InvertPermutation                               |
+| L2Norm                |              | ✅    |   ✅  |               |              |              |       |    |                 | L2_NORMALIZATION                |                          |                                                 |                                                 |
+| LayerNorm             | ✅    | ✅    | ✅    |               | ✅ | ✅ |           |                   |              |              |                          |                                                 |                                                 |
+| LayerNormGrad         | ✅    | ✅    |     |      |      |      |       |       |       |       |     |        |       |
+| LeakyReLU             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                      | LeakyRelu                       |                          | LeakyRelu                                       | LeakyRelu                                       |
+| LeakyReLUGrad |  | ✅ |  |  |  |  | |  |  |  | |  |  |
+| Less                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                             | Less                            |                          | Less                                            | Less                                            |
+| LessEqual             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                        | LessEqual                       |                          |                                                 | LessEqual                                       |
+| LRN                   |              | ✅    |              |               |              |              |        |   |                | LocalResponseNorm               |                          | Lrn, LRN                                        |                                                 |
+| Log                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                              | Log                             |                          | Log                                             | Log                                             |
+| LogGrad | ✅ | ✅ |  |  |  |  |  | | | | |  |  |
+| LogicalAnd            | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                       | LogicalAnd                      |                          | And                                             | LogicalAnd                                      |
+| LogicalNot            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                       | LogicalNot                      |                          | Not                                             | LogicalNot                                      |
+| LogicalOr             | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                         |  LogicalOr                       |                          | Or                                              | LogicalOr                                       |
+| LogSoftmax            | ✅    | ✅    |              |               |     |     |   | |          |  LogSoftmax        |                          | LogSoftmax                |                                        |
+| LshProjection         |              | ✅    |              |               |              |              |       |    |                    | LshProjection                   |                          |                                                 |                                                 |
+| LSTM                  |    ✅   | ✅    |              |               |              |              |           |                                 |               |               |           | LSTM                                            |                                                 |
+| MatMul                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                    | BatchMatMul                     |                          | MatMul,<br/>Gemm                         | MatMul,<br/>BatchMatMul,<br/>BatchMatMulV2            |
+| MatMulGrad |  | ✅ |  |  |  | |  |  | | | |  |  |
+| Maximum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | ✅                        | Maximum                         |                          |  Max                                            | Maximum                                         |
+| MaximumGrad | ✅ | ✅ | | |  |  |  |  | | | | | |
+| MaxPool               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                     | MaxPooling                      | Pooling                  | MaxPool,<br/>GlobalMaxPool                          | MaxPool                                         |
+| MaxPoolGrad | ✅ | ✅ |  |  |  |  | | |  |  |  |  |  |
+| Merge                 | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | Merge                                           |
+| Minimum               | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | | ✅                        | Minimum                         |                          | Min                                             | Minimum                                         |
+| MinimumGrad | ✅ | ✅ | | |  |  |  | |  |  | |  |  |
+| Mul                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                            | Mul                             |                          | Mul                                             | Mul                                             |
+| MulGrad |  | ✅ |  |  |  |  | |  |  |  | |  |  |
+| Neg                   | ✅    | ✅    |              |               | ✅    | ✅    | ✅ | |                              | Neg                             |                          | Neg                                             |Neg                                              |
+| NegGrad | ✅ | ✅ | | |  |  | |  |  |  | |  | |
+| NotEqual              | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                         | NotEqual                        |                          |                                                 | NotEqual                                        |
+| OneHot |  ✅  | ✅    |              |               | ✅    | ✅    |       |    |                           | OneHot                          |                          | OneHot                                          | OneHot                                          |
+| Pad                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                 | Pad, MirrorPad, PadV2                  |                          | Pad                                             | MirrorPad, Pad, PadV2                                  |
+| Pow                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ✅                            | Pow                             | Power                    | Pow<sup>[2]</sup>                               | Pow                                             |
+| PowGrad | | ✅ |  |  |  |  | |  |  |  | |  |  |
+| PReLU  | ✅ | ✅ |  |  | ✅    | ✅    |  | | ✅ | PRELU | PReLU | PRelu |   |
+| QuantDTypeCast        | ✅ | ✅ | ✅ | ✅ |   |   |   |   |   |   |   |   |   |
+| RaggedRange        | ✅ | ✅ |   |   |   |   |   |   |   |   |   |   | RaggedRange |
+| RandomStandardNormal  |  ✅  | ✅    |              |               |              |              |       |    |                                 |                                 |                          |                                                 | RandomStandardNormal                            |
+| RandomUniform         |              | ✅    |              |               |              |              |        |   |                                 |                                 |                          |                                                 | RandomUniform                                   |
+| Range                 |  ✅  | ✅    |              |               |              |              |         |  |                            | Range                           |                          | Range                                           | Range |
+| Rank                  |  ✅  | ✅    |              |               |              |              |         |  |                             | Rank                            |                          |                                                 | Rank                                            |
+| RealDiv               | ✅ | ✅ |   |   |   |   |   |   | ✅ |   |   |   |   |
+| Reciprocal            | ✅    | ✅    | ✅    |               |              |              | ✅ |              |                   |                   |                          | Reciprocal                                      |                                                 |
+| ReduceAll             |              | ✅    |              |               |              |              |           |              |                   |                   |                          |                                                 | All                                             |
+| ReduceASum            |  ✅  | ✅    |              |               | ✅    | ✅    |           |                |                 |                 | Reduction                |                                                 |                                                 |
+| ReduceMax             | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ |                        | ReduceMax                       |                          | ReduceMax                                       | Max                                             |
+| ReduceMean            | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | ✅ | Mean                            | Reduction                | ReduceMean                                      | Mean                                            |
+| ReduceMin             |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ |                        | ReduceMin                       |                          | ReduceMin                                       | Min                                             |
+| ReduceProd            |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ |                       | ReduceProd                      |                          | ReduceProd                                      | Prod                                            |
+| ReduceSum             |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           | ✅  | ✅ | Sum                             | Reduction                | ReduceSum                                       | Sum                                             |
+| ReduceSumSquare       | ✅ | ✅    | ✅    | ✅     |              |              |           |                      |           |           | Reduction                | ReduceSumSquare                                 |                                                 |
+| ReLU                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅ | Relu                            | ReLU                     | Relu                                            | Relu                                            |
+| ReLUGrad | ✅ | ✅ |  |  |  |  |  | | | |  |  |  |
+| ReLU6                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅ | Relu6                           | ReLU6                    | Clip<sup>[1]</sup>                              | Relu6                                           |
+| ReLU6Grad | ✅ | ✅ |  |  |  |  |  |  | | | |  |  |
+| Reshape               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅ | Reshape                         | Reshape                  | Reshape,<br/>Flatten                                 | Reshape                                         |
+| Resize |  ✅  | ✅  | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  | ResizeBilinear,<br/>NearestNeighbor | Interp |  Resize, Upsample | ResizeBilinear,<br/>ResizeBicubic,<br/>ResizeNearestNeighbor |
+| ResizeGrad | ✅ | ✅ |  |  |  |  | | |  |  |  | |  |
+| ResizeNearestNeighbor |  |  | | | | | | | ✅ | | | | |
+| Reverse               |              | ✅    |              |               |              |              |       |    |                          | reverse                         |                          |                                                 | ReverseV2                                       |
+| ReverseSequence       |              | ✅    |              |               |              |              |       |    |                  | ReverseSequence                 |                          |                                                 | ReverseSequence                                 |
+| Round                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                          | Round                           |                          | Round                                           | Round                                           |
+| Rsqrt                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                            | Rsqrt                           |                          |                                                 | Rsqrt                                           |
+| Select                |              | ✅    |              |               |              |              |           |                                 |                          |                          |                                  |               | Select                                          |
+| Selu                  |              |              |              |               |              |              |           |                                 |                          |                          |                       |                          | Selu                                            |
+| Scale                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅  | ✅ | ✅ |                                | Scale                    |                                                 |                                                 |
+| ScatterNd | ✅ | ✅ |  |  |  |  |  |  |  | ScatterNd |  | ScatterND  |  |
+| ScatterNdUpdate | ✅ | ✅ |  |  |  |  |  |  |  | ScatterNdUpdate |  | ScatterNdUpdate  |  |
+| Shape                 |   ✅   | ✅    | ✅    | ✅     | ✅    | ✅    |   | | ✅                          | Shape                           |                          | Shape                                           | Shape                                           |
+| Sigmoid               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    |  ✅  | | ✅                       | Logistic                        | Sigmoid                  | Sigmoid                                         | Sigmoid                                         |
 | SigmoidGrad | ✅ | ✅ |  |  |  |  | |  |  |  |  |  |  |
-| Sin                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Sin                             |                          | Sin                                             | Sin                                             | Sin |
-| Size  | ✅ | ✅    |  |   |   |  |  |  |  |  |  | Size  |  |
-| Slice                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Slice                           | Slice                    | Slice                                           | Slice                                           |  |
-| SkipGram              |              | ✅    |              |               |              |              |           | | SKipGram                        |                          |                                                 |                                                 |  |
-| Softmax               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | Softmax                         | Softmax                  | Softmax                                         | Softmax                                         | Softmax |
+| Sin                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                            | Sin                             |                          | Sin                                             | Sin                                             |
+| Size  | ✅ | ✅    |  |   |   |  |  |  |  |  |  |  | Size  |
+| Slice                 | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | |                            | Slice                           | Slice                    | Slice                                           | Slice                                           |
+| SkipGram              |              | ✅    |              |               |              |              |           | |                         | SKipGram                        |                          |                                                 |                                                 |
+| Softmax               | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | ✅ | ✅                        | Softmax                         | Softmax                  | Softmax                                         | Softmax                                         |
 | SoftmaxGrad |  | ✅ |  |  |  |  | | |  |  |  |  |  |
-| Softplus | ✅ | ✅ |  |  |  |  |  | | |  |  | Softplus |  |
-| SpaceToBatch          | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |         |  | SpaceToBatch                    |                          |                                                 |                                                 |  |
-| SpaceToBatchND        | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |       |    | SpaceToBatchND                  |                          |                                                 | SpaceToBatchND                                  |  |
-| SpaceToDepth          |  ✅  | ✅    |              |               | ✅    | ✅    |         |  | SpaceToDepth                    |                          | SpaceToDepth                                    |                                                 |  |
-| SparseToDense         |  ✅  | ✅    |      |      | ✅    | ✅    |          | | SpareToDense                    |                          |                                                 |                                                 |  |
-| Splice | ✅ | ✅  |  |  |  |  |   | |  |  | Splice |  |  |
-| Split                 | ✅    | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ | | Split, SplitV                   |                          | Split                                           | Split, SplitV                                   |  |
-| SplitWithOverlap      | ✅    | ✅    |      |      |     |     |  |   |   |   |    |   |  |
-| Sqrt                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Sqrt                            |                          | Sqrt                                            | Sqrt                                            | Sqrt |
-| Square                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | Square                          |                          |                                                 | Square                                          | Square |
-| SquaredDifference     | ✅    | ✅    |              |               | ✅    | ✅    |   | | SquaredDifference               |                          |                                                 | SquaredDifference                               |  |
-| Squeeze               |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ | Squeeze                         |                          | Squeeze                                         | Squeeze                                         |  |
-| StridedSlice          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | StridedSlice                    |                          | Slice,<br/>DynamicSlice                              | StridedSlice                                    | StridedSlice |
-| StridedSliceGrad      |  ✅  | ✅    |       |       |       |      |    |   |         |        |        |         |
-| Stack                 | ✅    | ✅    |              |               | ✅    | ✅    |           | | Stack                           |                          |                                                 | Pack                                            | Stack |
-| Sub                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | Sub                             |                          | Sub                                             | Sub                                             | Sub |
-| SubGrad |  | ✅ |  |  |  |  |  | | | |  |  | |
+| Softplus | ✅ | ✅ |  |  |  |  |  | | | |  |  | Softplus |
+| SpaceToBatch          | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |         |  |                     | SpaceToBatch                    |                          |                                                 |                                                 |
+| SpaceToBatchND        | ✅ | ✅    | ✅    | ✅     | ✅    | ✅    |       |    |                   | SpaceToBatchND                  |                          |                                                 | SpaceToBatchND                                  |
+| SpaceToDepth          |  ✅  | ✅    |              |               | ✅    | ✅    |         |  |                     | SpaceToDepth                    |                          | SpaceToDepth                                    |                                                 |
+| SparseToDense         |  ✅  | ✅    |      |      | ✅    | ✅    |          | |                     | SpareToDense                    |                          |                                                 |                                                 |
+| Splice | ✅ | ✅  |  |  |  |  |   | |  |  |  | Splice |  |
+| Split                 | ✅    | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ | |                    | Split, SplitV                   |                          | Split                                           | Split, SplitV                                   |
+| SplitWithOverlap      | ✅    | ✅    |      |      |     |     |  |   |   |   |   |    |   |
+| Sqrt                  | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                           | Sqrt                            |                          | Sqrt                                            | Sqrt                                            |
+| Square                | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ | | ✅                         | Square                          |                          |                                                 | Square                                          |
+| SquaredDifference     | ✅    | ✅    |              |               | ✅    | ✅    |   | |                | SquaredDifference               |                          |                                                 | SquaredDifference                               |
+| Squeeze               |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    |           |  ✅ |                          | Squeeze                         |                          | Squeeze                                         | Squeeze                                         |
+| StridedSlice          |  ✅  | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                   | StridedSlice                    |                          | Slice,<br/>DynamicSlice                              | StridedSlice                                    |
+| StridedSliceGrad      |  ✅  | ✅    |       |       |       |      |    |   |         |         |        |        |         |
+| Stack                 | ✅    | ✅    |              |               | ✅    | ✅    |           | | ✅                          | Stack                           |                          |                                                 | Pack                                            |
+| Sub                   | ✅    | ✅    | ✅    | ✅     | ✅    | ✅    | ✅ |  ✅ | ✅                            | Sub                             |                          | Sub                                             | Sub                                             |
+| SubGrad |  | ✅ |  |  |  |  |  | | | | |  |  |
 | Swish | ✅ | ✅ | | | | | | | | | | | |
-| Switch                | ✅ | ✅    |              |               |              |              |           |                                 |                          |                     |                            | Switch                                          |  |
-| Tanh                  | ✅    | ✅    |              |               | ✅    | ✅    | ✅ |  ✅ | Tanh                            | TanH                     | Tanh, Sign                                      | Tanh                                            | Tanh |
-| TanhGrad |  | ✅ | | |  |  | |  | | |  |  |  |
-| TensorListFromTensor  | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListFromTensor                            |  |
-| TensorListGetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListGetItem                               |  |
-| TensorListReserve     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListReserve                               |  |
-| TensorListSetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListSetItem                               |  |
-| TensorListStack       | ✅ | ✅    |              |               |              |              |           |                                 |                          |                                          |       | TensorListStack                                 |  |
-| Tile                  | ✅ | ✅    |              |               |              |              | ✅       |  | Tile                            | Tile                     | Tile                                    | Tile                                            |  |
-| TopK                  | ✅ | ✅    | ✅    | ✅     |              |              |       |    | TopKV2                          |                          | TopK                                            | TopKV2                                          |  |
-| Transpose             | ✅    | ✅    |   ✅    |               | ✅    | ✅    | ✅ | ✅ |  Transpose                       | Permute                  | Transpose, Int8Transpose                                       | Transpose                                       | Transpose |
-| UniformReal           |              | ✅    |              |               |              |              |           |                                 |                          |                   |                              |                                                 |  |
-| Unique | ✅ | ✅  |  |   |   |  |   |     | Unique |   |   |  |  |
-| UnsortedSegmentSum    |  ✅  | ✅    |       |       |              |              |    |                          |               |                      |                                        |  UnsortedSegmentSum |  |
-| Unsqueeze             |  ✅  | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ |          ✅               |               |                      | Unsqueeze                                       |                                                 |  |
-| Unstack |  ✅  | ✅    |  |  |  |  |  | | Unstack |  |  |  |  |
-| Where | ✅  | ✅   |  |  |  |  |  | | Where |  | NonZero, Where | Where |  |
-| ZerosLike | ✅ | ✅ |  |  |  |  |  |  | ZerosLike  |  | | ZerosLike |  |
-| 转换工具支持的其他算子<sup>[4]</sup> |     |              |              |               |              |              |         |           |       |     | Constant,<br/>Atan, Asin, Tan, Erf,<br/>Loop, Dropout, If, Identity,<br/>Int8GivenIntTensorFill,<br/>Int8GivenTensorFill,<br/>Int8Quantize,<br/>Int8Dequantize,<br/>LpNormalization      | Dropout, Enter,<br/>Exit, If, <br/>IsFinite,<br/>LinSpace,<br/>LoopCond,<br/>NextIteration,<br/>StatelessIf,<br/>StatelessWhile,<br/>TensorArrayGatherV3,<br/>TensorArrayReadV3,<br/>TensorArrayScatterV3,<br/>TensorArraySizeV3,<br/>TensorArrayV3,<br/>TensorArrayWriteV3,<br/>While |  |
+| Switch                | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                     |                            | Switch                                          |
+| Tanh                  | ✅    | ✅    |              |               | ✅    | ✅    | ✅ |  ✅ | ✅                           | Tanh                            | TanH                     | Tanh, Sign                                      | Tanh                                            |
+| TanhGrad |  | ✅ | | |  |  | |  | | | |  |  |
+| TensorListFromTensor  | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListFromTensor                            |
+| TensorListGetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListGetItem                               |
+| TensorListReserve     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListReserve                               |
+| TensorListSetItem     | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListSetItem                               |
+| TensorListStack       | ✅ | ✅    |              |               |              |              |           |                                 |                          |                          |                                          |       | TensorListStack                                 |
+| Tile                  | ✅ | ✅    |              |               |              |              | ✅       |  |                             | Tile                            | Tile                     | Tile                                    | Tile                                            |
+| TopK                  | ✅ | ✅    | ✅    | ✅     |              |              |       |    |                           | TopKV2                          |                          | TopK                                            | TopKV2                                          |
+| Transpose             | ✅    | ✅    |   ✅    |               | ✅    | ✅    | ✅ | ✅ |  ✅                      |  Transpose                       | Permute                  | Transpose, Int8Transpose                                       | Transpose                                       |
+| UniformReal           |              | ✅    |              |               |              |              |           |                                 |                          |                          |                   |                              |                                                 |
+| Unique | ✅ | ✅  |  |   |   |  |   |     |  | Unique |   |   |  |
+| UnsortedSegmentSum    |  ✅  | ✅    |       |       |              |              |    |                          |               |               |                      |                                        |  UnsortedSegmentSum |
+| Unsqueeze             |  ✅  | ✅    | ✅    | ✅     | ✅ | ✅ | ✅ |          ✅               |               |               |                      | Unsqueeze                                       |                                                 |
+| Unstack |  ✅  | ✅    |  |  |  |  |  | |  | Unstack |  |  |  |
+| Upsample |  |  | | | | | | | ✅ |  | | | |
+| Where | ✅  | ✅   |  |  |  |  |  | |  | Where |  | NonZero, Where | Where |
+| ZerosLike | ✅ | ✅ |  |  |  |  |  |  |   | ZerosLike  |  | | ZerosLike |
+| 转换工具支持的其他算子<sup>[4]</sup> |     |              |              |               |              |              |         |           |       |       |     | Constant,<br/>Atan, Asin, Tan, Erf,<br/>Loop, Dropout, If, Identity,<br/>Int8GivenIntTensorFill,<br/>Int8GivenTensorFill,<br/>Int8Quantize,<br/>Int8Dequantize,<br/>LpNormalization      | Dropout, Enter,<br/>Exit, If, <br/>IsFinite,<br/>LinSpace,<br/>LoopCond,<br/>NextIteration,<br/>StatelessIf,<br/>StatelessWhile,<br/>TensorArrayGatherV3,<br/>TensorArrayReadV3,<br/>TensorArrayScatterV3,<br/>TensorArraySizeV3,<br/>TensorArrayV3,<br/>TensorArrayWriteV3,<br/>While |
 
 [1] Clip：仅支持将clip(0, 6)转换为Relu6。
 
diff --git a/docs/lite/docs/source_zh_cn/quick_start/image_segmentation.md b/docs/lite/docs/source_zh_cn/quick_start/image_segmentation.md
index 23463e1e3ae48a2219c47be95f1fd7eb8892e84a..85833a3d5a5e9c6ba0773f15ea6bf814a47ec139 100644
--- a/docs/lite/docs/source_zh_cn/quick_start/image_segmentation.md
+++ b/docs/lite/docs/source_zh_cn/quick_start/image_segmentation.md
@@ -80,11 +80,7 @@
 app
 ├── src/main
 │   ├── assets # 资源文件
-|   |   └── model # 模型文件
-|   |       └── segment_model.ms # 存放的模型文件
-│   |
-│   ├── libs # Android库项目的二进制归档文件
-|   |   └── mindspore-lite-version.aar #  MindSpore Lite针对Android版本的归档文件
+|   |   └── segment_model.ms # 存放的模型文件
 │   |
 │   ├── java # java层应用代码
 │   │   └── com.mindspore.imagesegmentation
@@ -95,7 +91,9 @@ app
 │   ├── res # 存放Android相关的资源文件
 │   └── AndroidManifest.xml # Android配置文件
 │
-│
+├── libs # Android库项目的二进制归档文件
+|     └── mindspore-lite-version.aar #  MindSpore Lite针对Android版本的归档文件
+|
 ├── build.gradle # 其他Android配置文件
 ├── download.gradle # 工程依赖文件下载
 └── ...
@@ -121,30 +119,35 @@ Android调用MindSpore Android AAR时，需要相关库文件支持。可通过M
 
 推理代码流程如下，完整代码请参见 [src/java/com/mindspore/imagesegmentation/TrackingMobile](https://gitee.com/mindspore/models/blob/master/official/lite/image_segmentation/app/src/main/java/com/mindspore/imagesegmentation/help/TrackingMobile.java)。
 
-1. 加载MindSpore Lite模型文件，构建上下文、会话以及用于推理的计算图。  
+1. 加载MindSpore Lite模型，构建上下文、会话以及用于推理的计算图。  
 
     - 创建会话。
 
       ```java
       // Create and init config.
       MSContext context = new MSContext();
-      context.init(2, CpuBindMode.HIGHER_CPU, false);
-      boolean ret = context.addDeviceInfo(com.mindspore.config.DeviceType.DT_CPU, false, 0);
-      if (!ret) {
-          Log.e(TAG, "Create CPU Config failed.");
-          return null;
+      if (!context.init(2, CpuBindMode.MID_CPU, false)) {
+          Log.e(TAG, "Init context failed");
+          return;
+      }
+      if (!context.addDeviceInfo(DeviceType.DT_CPU, false, 0)) {
+          Log.e(TAG, "Add device info failed");
+          return;
       }
       ```
 
-    - 加载模型文件并构建用于推理的计算图。
+    - 加载模型并构建用于推理的计算图。
 
       ```java
+      MappedByteBuffer modelBuffer = loadModel(mContext, IMAGESEGMENTATIONMODEL);
+      if(modelBuffer == null) {
+          Log.e(TAG, "Load model failed");
+          return;
+      }
       // build model.
-      boolean ret = model.build(filePath, ModelType.MT_MINDIR, msContext);
-      if (!ret) {
-          model.free();
-          Log.e(TAG, "Compile graph failed");
-          return null;
+      boolean ret = model.build(modelBuffer, ModelType.MT_MINDIR,context);
+      if(!ret) {
+          Log.e(TAG, "Build model failed");
       }
       ```
 
@@ -172,12 +175,11 @@ Android调用MindSpore Android AAR时，需要相关库文件支持。可通过M
 
     ```java
     // Run graph to infer results.
-    boolean ret = model.predict();
-    if (!ret) {
-        Log.e(TAG, "MindSpore Lite run failed.");
-        return false;
+    if (!model.predict()) {
+        Log.e(TAG, "Run graph failed");
+        return null;
     }
-     ```
+    ```
 
 4. 对输出数据进行处理。
 
diff --git a/docs/lite/docs/source_zh_cn/quick_start/one_hour_introduction.md b/docs/lite/docs/source_zh_cn/quick_start/one_hour_introduction.md
index bd4f8aa57a48d520be5cf8c64d80bdc3f2ee9df3..23b35770ca5b52c0b6f1d10a12149bdd17313f5e 100644
--- a/docs/lite/docs/source_zh_cn/quick_start/one_hour_introduction.md
+++ b/docs/lite/docs/source_zh_cn/quick_start/one_hour_introduction.md
@@ -1,6 +1,6 @@
 # 一小时入门
 
-`Android` `C++` `全流程` `模型转换` `模型加载` `推理应用` `数据准备` `初级` `中级` `高级`
+`Windows` `Linux` `Android` `C++` `全流程` `模型转换` `模型加载` `推理应用` `数据准备` `初级` `中级` `高级`
 
 <a href="https://gitee.com/mindspore/docs/blob/master/docs/lite/docs/source_zh_cn/quick_start/one_hour_introduction.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
 
@@ -513,7 +513,7 @@ mindspore-lite-{version}-linux-x64
 
 5. 编写代码
 
-    打开刚才创建的`mian.cc`，粘贴如下内容：
+    打开刚才创建的`main.cc`，粘贴如下内容：
 
     ```cpp
     #include <iostream>
@@ -1221,7 +1221,7 @@ mindspore-lite-{version}-win-x64
 
 5. 编写代码
 
-    打开刚才创建的`mian.cc`，粘贴如下内容：
+    打开刚才创建的`main.cc`，粘贴如下内容：
 
     ```cpp
     #include <iostream>
diff --git a/docs/lite/docs/source_zh_cn/quick_start/quick_start.md b/docs/lite/docs/source_zh_cn/quick_start/quick_start.md
index 01e8379cbe960b738b7cd33b8af3efae16c835ec..7c96cff5257eed0b07815dee2ed07d258c0ffedc 100644
--- a/docs/lite/docs/source_zh_cn/quick_start/quick_start.md
+++ b/docs/lite/docs/source_zh_cn/quick_start/quick_start.md
@@ -243,139 +243,119 @@ target_link_libraries( # Specifies the target library.
         MSNetWork *labelNet = new MSNetWork;
         *labelEnv = labelNet;
 
-        mindspore::lite::Context *context = new mindspore::lite::Context;
-        context->thread_num_ = num_thread;
-        context->device_list_[0].device_info_.cpu_device_info_.cpu_bind_mode_ = mindspore::lite::NO_BIND;
-        context->device_list_[0].device_info_.cpu_device_info_.enable_float16_ = false;
-        context->device_list_[0].device_type_ = mindspore::lite::DT_CPU;
-
-        labelNet->CreateSessionMS(modelBuffer, bufferLen, context);
-        delete context;
+        auto context = std::make_shared<mindspore::Context>();
+        if (context == nullptr) {
+            MS_PRINT("context create failed!");
+            delete labelNet;
+            delete labelEnv;
+            return (jlong) nullptr;
+        }
+
+        context->SetThreadNum(num_thread);
+        context->SetThreadAffinity(0);
+        auto &device_list = context->MutableDeviceInfo();
+        auto cpuDeviceInfo = std::make_shared<mindspore::CPUDeviceInfo>();
+        cpuDeviceInfo->SetEnableFP16(false);
+        device_list.push_back(cpuDeviceInfo);
         ```
 
         基于模型文件`modelBuffer`构建用于推理的计算图。
 
-         ```cpp
-         void MSNetWork::CreateSessionMS(char *modelBuffer, size_t bufferLen, mindspore::lite::Context *ctx) {
-           session_ = mindspore::session::LiteSession::CreateSession(ctx);
-           if (session_ == nullptr) {
-               MS_PRINT("Create Session failed.");
-               return;
-           }
-
-           // Compile model.
-           model_ = mindspore::lite::Model::Import(modelBuffer, bufferLen);
-           if (model_ == nullptr) {
-               ReleaseNets();
-               MS_PRINT("Import model failed.");
-               return;
-           }
-
-           int ret = session_->CompileGraph(model_);
-           if (ret != mindspore::lite::RET_OK) {
-                ReleaseNets();
-                MS_PRINT("CompileGraph failed.");
-                return;
-           }
-         }
+        ```cpp
+        bool MSNetWork::BuildModel(char *modelBuffer, size_t bufferLen,
+                                   std::shared_ptr<mindspore::Context> ctx) {
+        model_ = std::make_shared<mindspore::Model>();
+        if (model_ == nullptr) {
+            MS_PRINT("MindSpore build model failed!.");
+            return false;
+        }
+        auto ret = model_->Build(modelBuffer, bufferLen, mindspore::ModelType::kMindIR, ctx);
+        return ret.IsOk();
+        }
         ```
 
 2. 将输入图片转换为传入MindSpore模型的Tensor格式。
 
     - 将待检测图片`srcBitmap`进行尺寸裁剪并转换为LiteMat格式`lite_norm_mat_cut`。对其宽高以及通道数信息转换成float格式数据`dataHWC`。最终把`dataHWC`拷贝到MindSpore模型的Tensor输入`inTensor`中。
 
-      ```cpp
-      if (!BitmapToLiteMat(env, srcBitmap, &lite_mat_bgr)) {
-          MS_PRINT("BitmapToLiteMat error");
-          return NULL;
-       }
-      if (!PreProcessImageData(lite_mat_bgr, &lite_norm_mat_cut)) {
-          MS_PRINT("PreProcessImageData error");
-          return NULL;
-       }
-
-      ImgDims inputDims;
-      inputDims.channel = lite_norm_mat_cut.channel_;
-      inputDims.width = lite_norm_mat_cut.width_;
-      inputDims.height = lite_norm_mat_cut.height_;
-
-      // Get the MindSpore inference environment which created in loadModel().
-      void **labelEnv = reinterpret_cast<void **>(netEnv);
-      if (labelEnv == nullptr) {
-          MS_PRINT("MindSpore error, labelEnv is a nullptr.");
-          return NULL;
-       }
-      MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
-
-      auto mSession = labelNet->session();
-      if (mSession == nullptr) {
-          MS_PRINT("MindSpore error, Session is a nullptr.");
-          return NULL;
+        ```cpp
+        void **labelEnv = reinterpret_cast<void **>(netEnv);
+        if (labelEnv == nullptr) {
+            MS_PRINT("MindSpore error, labelEnv is a nullptr.");
+            return NULL;
+        }
+        MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv);
+
+        auto mModel = labelNet->model();
+        if (mModel == nullptr) {
+            MS_PRINT("MindSpore error, Model is a nullptr.");
+            return NULL;
         }
-       MS_PRINT("MindSpore get session.");
+        MS_PRINT("MindSpore get Model.");
 
-       auto msInputs = mSession->GetInputs();
-       if (msInputs.size() == 0) {
-          MS_PRINT("MindSpore error, msInputs.size() equals 0.");
-          return NULL;
+        auto msInputs = mModel->GetInputs();
+        if (msInputs.empty()) {
+            MS_PRINT("MindSpore error, msInputs.size() equals 0.");
+            return NULL;
         }
         auto inTensor = msInputs.front();
 
         float *dataHWC = reinterpret_cast<float *>(lite_norm_mat_cut.data_ptr_);
         // Copy dataHWC to the model input tensor.
-        memcpy(inTensor->MutableData(), dataHWC,
-           inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
-      ```
+        memcpy(inTensor.MutableData(), dataHWC,
+                inputDims.channel * inputDims.width * inputDims.height * sizeof(float));
+        ```
 
        调整输入图片的尺寸，以及数据处理详细算法。
 
-       ```cpp
-       bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) {
-         bool ret = false;
-         LiteMat lite_mat_resize;
-         LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr;
-         ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 256, 256);
-         if (!ret) {
+        ```cpp
+        bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) {
+            bool ret = false;
+            LiteMat lite_mat_resize;
+            LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr;
+            ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 256, 256);
+            if (!ret) {
             MS_PRINT("ResizeBilinear error");
             return false;
-         }
-         LiteMat lite_mat_convert_float;
-         ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0);
-         if (!ret) {
-             MS_PRINT("ConvertTo error");
-             return false;
-         }
-         LiteMat lite_mat_cut;
-         ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224);
-         if (!ret) {
-             MS_PRINT("Crop error");
-             return false;
-         }
-         std::vector<float> means = {0.485, 0.456, 0.406};
-         std::vector<float> stds = {0.229, 0.224, 0.225};
-         SubStractMeanNormalize(lite_mat_cut, lite_norm_mat_cut, means, stds);
-         return true;
+            }
+            LiteMat lite_mat_convert_float;
+            ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0);
+            if (!ret) {
+                MS_PRINT("ConvertTo error");
+                return false;
+            }
+            LiteMat lite_mat_cut;
+            ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224);
+            if (!ret) {
+                MS_PRINT("Crop error");
+                return false;
+            }
+            std::vector<float> means = {0.485, 0.456, 0.406};
+            std::vector<float> stds = {0.229, 0.224, 0.225};
+            SubStractMeanNormalize(lite_mat_cut, lite_norm_mat_cut, means, stds);
+            return true;
         }
-      ```
+        ```
 
 3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。
 
    - 图和模型加载完成，执行端侧推理。
 
         ```cpp
+        std::vector<mindspore::MSTensor> outputs;
         // After the model and image tensor data is loaded, run inference.
-        auto status = mSession->RunGraph();
+        auto status = mModel->Predict(msInputs, &outputs);
         ```
 
    - 获取对MindSpore模型的Tensor输出`msOutputs`。通过`msOutputs`以及分类数组信息，计算得到在APP中显示的文本信息`resultCharData`。
 
         ```cpp
-        auto names = mSession->GetOutputTensorNames();
-        std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs;
+        auto names = mModel->GetOutputTensorNames();
+        std::unordered_map<std::string, mindspore::MSTensor> msOutputs;
         for (const auto &name : names) {
-            auto temp_dat =mSession->GetOutputByTensorName(name);
-            msOutputs.insert(std::pair<std::string, mindspore::tensor::MSTensor *> {name, temp_dat});
-          }
+            auto temp_dat = mModel->GetOutputByTensorName(name);
+            msOutputs.insert(std::pair<std::string, mindspore::MSTensor>{name, temp_dat});
+        }
         std::string resultStr = ProcessRunnetResult(::RET_CATEGORY_SUM,::labels_name_map, msOutputs);
 
         const char *resultCharData = resultStr.c_str();
@@ -385,48 +365,55 @@ target_link_libraries( # Specifies the target library.
      输出数据的后续处理。通过`msOutputs`获取输出对象`outputTensor`，并和事物类别数组`labels_name_map`解析得到每个元素的训练的得分数组`scores[]`。 设置可信度阀值为`unifiedThre`，根据训练数据统计可信度阀值。高于阀值，归属于这个类型。反之，则不是。最终返回一个对应事物类别名称和对应得分的数据`categoryScore`。
 
         ```cpp
-        std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[], std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs) {
+        std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[],
+                                        std::unordered_map<std::string, mindspore::MSTensor> msOutputs) {
         // Get the branch of the model output.
         // Use iterators to get map elements.
-        std::unordered_map<std::string, mindspore::tensor::MSTensor *>::iterator iter;
+        std::unordered_map<std::string, mindspore::MSTensor>::iterator iter;
         iter = msOutputs.begin();
 
         // The mobilenetv2.ms model output just one branch.
         auto outputTensor = iter->second;
 
-        int tensorNum = outputTensor->ElementsNum();
+        int tensorNum = outputTensor.ElementNum();
         MS_PRINT("Number of tensor elements:%d", tensorNum);
 
         // Get a pointer to the first score.
-        float *temp_scores = static_cast<float *>(outputTensor->MutableData());
+        float *temp_scores = static_cast<float *>(outputTensor.MutableData());
         float scores[RET_CATEGORY_SUM];
         for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
-             scores[i] = temp_scores[i];
+            scores[i] = temp_scores[i];
         }
 
         const float unifiedThre = 0.5;
         const float probMax = 1.0;
         for (size_t i = 0; i < RET_CATEGORY_SUM; ++i) {
-             float threshold = g_thres_map[i];
-             float tmpProb = scores[i];
-        if (tmpProb < threshold) {
+            float threshold = g_thres_map[i];
+            float tmpProb = scores[i];
+            if (tmpProb < threshold) {
             tmpProb = tmpProb / threshold * unifiedThre;
-        } else {
+            } else {
             tmpProb = (tmpProb - threshold) / (probMax - threshold) * unifiedThre + unifiedThre;
+            }
+            scores[i] = tmpProb;
         }
-        scores[i] = tmpProb;
+
+        for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
+            if (scores[i] > 0.5) {
+            MS_PRINT("MindSpore scores[%d] : [%f]", i, scores[i]);
+            }
         }
 
-         // Score for each category.
-         // Converted to text information that needs to be displayed in the APP.
-         std::string categoryScore = "";
-         for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
-             categoryScore += labels_name_map[i];
-             categoryScore += ":";
-             std::string score_str = std::to_string(scores[i]);
-             categoryScore += score_str;
-             categoryScore += ";";
-          }
-         return categoryScore;
-      }
-     ```
+        // Score for each category.
+        // Converted to text information that needs to be displayed in the APP.
+        std::string categoryScore = "";
+        for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
+            categoryScore += labels_name_map[i];
+            categoryScore += ":";
+            std::string score_str = std::to_string(scores[i]);
+            categoryScore += score_str;
+            categoryScore += ";";
+        }
+        return categoryScore;
+        }
+        ```
diff --git a/docs/lite/docs/source_zh_cn/troubleshooting_guide.md b/docs/lite/docs/source_zh_cn/troubleshooting_guide.md
index 2ff6b0f6bca12a009e4829e93bf1276de0e9c8de..715ff061010373e509cd039e7d4314ab22a949b1 100644
--- a/docs/lite/docs/source_zh_cn/troubleshooting_guide.md
+++ b/docs/lite/docs/source_zh_cn/troubleshooting_guide.md
@@ -4,7 +4,7 @@
 
 ## 概述
 
-在MindSpore Lite使用中遇到问题时，可首先查看日志，多数场景下的问题可以通过日志报错信息直接定位（通过设置环境变量[GLOG_v](https://mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11) 调整日志等级可以打印更多调试日志），这里简单介绍几种常见报错场景的问题定位与解决方法。
+在MindSpore Lite使用中遇到问题时，可首先查看日志，多数场景下的问题可以通过日志报错信息直接定位（通过设置环境变量[GLOG_v](https://mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置) 调整日志等级可以打印更多调试日志），这里简单介绍几种常见报错场景的问题定位与解决方法。
 
 > 1. 因不同版本中日志行号可能存在差异，下述示例日志报错信息中的行号信息均用”**”表示；
 > 2. 示例日志中只列出了通用信息，其他涉及具体场景的信息均用“****”表示。
@@ -56,7 +56,7 @@
 
 ### 全量化转换失败
 
-1. 针对动态Shape的模型，需要在[转换命令](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#id5)上设置`--inputShape=<INPUTSHAPE>`，例如
+1. 针对动态Shape的模型，需要在[转换命令](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#参数说明)上设置`--inputShape=<INPUTSHAPE>`，例如
 
     ```
     ./converter_lite --fmk=ModelType --modelFile=ModelFilePath --outputFile=ConvertedModelPath --configFile=/mindspore/lite/tools/converter/quantizer/config/full_quant.cfg --inputShape=intput_1:1,224,224,3;intput_2:1,48;
@@ -116,7 +116,7 @@
     ```
 
     - 问题分析：ms模型的输入shape包含-1，即模型输入为动态shape，直接推理时由于shape无效导致推理失败。
-    - 解决方法：MindSpore Lite在对包含动态shape输入的模型推理时要求指定合理的shape，使用benchmark工具时可通过设置[inputShapes](https://mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#id3) 参数指定，使用MindSpore Lite集成开发时可通过调用[Resize](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#resize) 方法设置。
+    - 解决方法：MindSpore Lite在对包含动态shape输入的模型推理时要求指定合理的shape，使用benchmark工具时可通过设置[inputShapes](https://mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#参数说明) 参数指定，使用MindSpore Lite集成开发时可通过调用[Resize](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#resize) 方法设置。
 
 ### OpenCL GPU 推理问题
 
@@ -185,7 +185,7 @@
     ```
 
     - 问题分析：TensorRT GPU 构图暂不支持有动态 shape 的模型，具体情况为模型的输入 shape 包含-1，或者模型中包含 shape 算子。
-    - 解决方法：在使用 converter 将模型转换成ms时，需要在[转换命令](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#id5)上设置`--inputShape=<INPUTSHAPE>`，指定输入 tensor 的 shape 信息。如需在推理时改变输入 shape，使用 benchmark 工具时可通过设置[inputShapes](https://mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#id3) 参数指定，使用 MindSpore Lite 集成开发时可通过调用[Resize](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#resize) 方法设置。注意： [Resize](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#resize)输入的 shape 维度必须要小于等于 [Build](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#build)模型的维度。
+    - 解决方法：在使用 converter 将模型转换成ms时，需要在[转换命令](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#参数说明)上设置`--inputShape=<INPUTSHAPE>`，指定输入 tensor 的 shape 信息。如需在推理时改变输入 shape，使用 benchmark 工具时可通过设置[inputShapes](https://mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#参数说明) 参数指定，使用 MindSpore Lite 集成开发时可通过调用[Resize](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#resize) 方法设置。注意： [Resize](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#resize)输入的 shape 维度必须要小于等于 [Build](https://mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#build)模型的维度。
 
 #### 图执行失败
 
@@ -280,7 +280,7 @@
 
 2. MindSpore Lite使用fp32推理结果正确，但是fp16推理结果出现NaN或者Inf值怎么办？
     - 结果出现NaN或者Inf值一般为推理过程中出现数值溢出，可以查看模型结构，筛选可能出数值溢出的算子层，然后通过benchmark工具的[Dump功能](https://mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#dump) 保存算子层输出确认出现数值溢出的算子。
-    - MindSpore Lite 1.5.0之后版本提供混合精度推理能力，在整网推理优先使用fp16时支持设置某一层算子进行fp32推理，具体使用方法可参考官网文档[混合精度运行](https://mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html#id13) ，通过将溢出层设置为fp32避免在fp16推理时出现的整网推理精度问题。
+    - MindSpore Lite 1.5.0之后版本提供混合精度推理能力，在整网推理优先使用fp16时支持设置某一层算子进行fp32推理，具体使用方法可参考官网文档[混合精度运行](https://mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html#混合精度运行) ，通过将溢出层设置为fp32避免在fp16推理时出现的整网推理精度问题。
 
 3. MindSpore Lite使用fp32和fp16推理结果同时出现NaN或者Inf值怎么办？
     - 问题分析：检查整个网络存在做除法操作的算子。在做推理时如果执行了除法操作，且除数是0时容易出现NaN值。比如下面的网络结构，如果该网络用于输入数据不做归一化的场景，输入数据在0-255范围，则会出现NaN值，原因在于不做归一化时，输入数据比较大，导致matmul的输出值会很大，导致Tanh激活函数输出等于1，最终导致Div算子除0。但如果网络输入数据做了归一化，Tanh激活函数不等于1，网络推理数据不存在NaN值。
diff --git a/docs/lite/docs/source_zh_cn/use/ascend_info.md b/docs/lite/docs/source_zh_cn/use/ascend_info.md
index 2f189dc872a13d5e6e390da27ce578f35aee5348..3f102dc6dc7dd2ea16f6f6de40bf7c3f1b6e18a7 100644
--- a/docs/lite/docs/source_zh_cn/use/ascend_info.md
+++ b/docs/lite/docs/source_zh_cn/use/ascend_info.md
@@ -100,7 +100,7 @@ MindSpore Lite提供离线转换模型功能的工具，将多种类型的模型
     CONVERTER RESULT SUCCESS:0
     ```
 
-    用户若想了解converter_lite转换工具的相关参数，可参考[参数说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#id4)。
+    用户若想了解converter_lite转换工具的相关参数，可参考[参数说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#参数说明)。
 
     说明：当原始模型输入shape不确定时，converter工具转换模型时要指定inputShape，同时configFile配置acl_option_cfg_param中input_shape_vector参数，取值相同，命令如下：
 
@@ -112,7 +112,7 @@ MindSpore Lite提供离线转换模型功能的工具，将多种类型的模型
 
     ```cpp
     [acl_option_cfg_param]
-    input_shape_vector="1,64,64,1"
+    input_shape_vector="[1,64,64,1]"
     ```
 
 表1：配置[acl_option_cfg_param]参数
@@ -126,7 +126,6 @@ MindSpore Lite提供离线转换模型功能的工具，将多种类型的模型
 | `dynamic_batch_size`       | 可选 | 指定[动态BatchSize](#动态Batch size)参数。 | String | `"2,4"`|
 | `dynamic_image_size`       | 可选 | 指定[动态分辨率](#动态分辨率)参数。  | String | `"96,96;32,32"` |
 | `fusion_switch_config_file_path` | 可选 | 配置[融合规则开关配置](https://support.huaweicloud.com/atctool-cann504alpha2infer/atlasatc_16_0077.html)文件路径及文件名。 | String   | -      |
-| `op_select_implmode`       | 可选 | 配置算子选择模式。 | String   | 可选有`"high_performance"`和`"high_precision"`，默认为`"high_performance"` |
 | `insert_op_config_file_path` | 可选 | 模型插入[AIPP](https://support.huaweicloud.com/adevg-ms-atlas200dkappc32/atlasadm_01_0023.html)算子 | String  | [AIPP](https://support.huaweicloud.com/adevg-ms-atlas200dkappc32/atlasadm_01_0023.html)配置文件路径 |
 
 ## 推理工具runtime
diff --git a/docs/lite/docs/source_zh_cn/use/benchmark_tool.md b/docs/lite/docs/source_zh_cn/use/benchmark_tool.md
index 1fa3771ebe2b56689f8f25a648ee91bb943afefa..c21a8982aaab039a4db104c6bde5f3a9b6e995b1 100644
--- a/docs/lite/docs/source_zh_cn/use/benchmark_tool.md
+++ b/docs/lite/docs/source_zh_cn/use/benchmark_tool.md
@@ -14,9 +14,9 @@
 
 使用Benchmark工具，需要进行如下环境准备工作。
 
-- 编译：Benchmark工具代码在MindSpore源码的`mindspore/lite/tools/benchmark`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id1)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id3)执行编译。
+- 编译：Benchmark工具代码在MindSpore源码的`mindspore/lite/tools/benchmark`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#编译示例)执行编译。
 
-- 运行：参考构建文档中的[编译输出](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id4)，获得`benchmark`工具。
+- 运行：参考构建文档中的[编译输出](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#编译结构)，获得`benchmark`工具。
 
 - 将推理需要的动态链接库加入环境变量LD_LIBRARY_PATH。
 
@@ -268,7 +268,7 @@ np.fromfile("/path/to/dump.bin", np.float32)
 
 使用Benchmark工具，需要进行如下环境准备工作。
 
-- 编译：Benchmark工具代码在MindSpore源码的`mindspore/lite/tools/benchmark`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id9)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id11)执行编译。
+- 编译：Benchmark工具代码在MindSpore源码的`mindspore/lite/tools/benchmark`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求-1)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#编译示例-1)执行编译。
 - 将推理需要的动态链接库加入环境变量PATH。
 
     ```bash
diff --git a/docs/lite/docs/source_zh_cn/use/benchmark_train_tool.md b/docs/lite/docs/source_zh_cn/use/benchmark_train_tool.md
index 4736d74f0d5df24145b8ab26b47f3662e9ca75bd..bba1a9f8a3d493da576c7e779b4bf6603ffd5470 100644
--- a/docs/lite/docs/source_zh_cn/use/benchmark_train_tool.md
+++ b/docs/lite/docs/source_zh_cn/use/benchmark_train_tool.md
@@ -14,9 +14,9 @@
 
 使用`benchmark_train`工具，需要进行如下环境准备工作。
 
-- 编译：`benchmark_train`工具代码在MindSpore源码的`mindspore/lite/tools/benchmark_train`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id1)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id3)编译端侧训练框架。
+- 编译：`benchmark_train`工具代码在MindSpore源码的`mindspore/lite/tools/benchmark_train`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#模块构建编译选项)编译端侧训练框架。
 
-- 配置环境变量：参考构建文档中的[编译输出](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id5)，获得`benchmark_train`工具，并配置环境变量。假设您编译出的端侧训练框架压缩包所在完整路径为`/path/mindspore-lite-{version}-{os}-{arch}.tar.gz`，解压并配置环境变量的命令如下：
+- 配置环境变量：参考构建文档中的[编译输出](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#目录结构)，获得`benchmark_train`工具，并配置环境变量。假设您编译出的端侧训练框架压缩包所在完整路径为`/path/mindspore-lite-{version}-{os}-{arch}.tar.gz`，解压并配置环境变量的命令如下：
 
     ```bash
     cd /path
diff --git a/docs/lite/docs/source_zh_cn/use/build.md b/docs/lite/docs/source_zh_cn/use/build.md
index 5ab7916ac3e4ed95b7214a1787f580cee9632fa1..2094da66e4f678e78951f1f4463612f0f2a35d64 100644
--- a/docs/lite/docs/source_zh_cn/use/build.md
+++ b/docs/lite/docs/source_zh_cn/use/build.md
@@ -82,12 +82,15 @@ MindSpore根目录下的`build.sh`脚本可用于MindSpore Lite的编译。
 | MSLITE_ENABLE_TRAIN | 是否编译训练版本 | on、off | on |
 | MSLITE_ENABLE_SSE | 是否启用SSE指令集，仅在`-I x86_64`时有效 | on、off | off |
 | MSLITE_ENABLE_AVX | 是否启用AVX指令集，仅在`-I x86_64`时有效 | on、off | off |
+| MSLITE_ENABLE_AVX512 | 是否启用AVX512指令集，仅在`-I x86_64`时有效 | on、off | off |
 | MSLITE_ENABLE_CONVERTER | 是否编译模型转换工具，仅在`-I x86_64`时有效 | on、off | on |
 | MSLITE_ENABLE_TOOLS | 是否编译配套工具 | on、off | on |
 | MSLITE_ENABLE_TESTCASES | 是否编译测试用例 | on、off | off |
+| MSLITE_ENABLE_SERVER_INFERENCE | 是否启动服务端推理接口 | on、off | off |
 
 > - TensorRT 和 NPU 的编译环境配置，参考[专用芯片集成说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/asic.html)。
 > - 启用AVX指令集时，需要运行环境的CPU同时支持avx特性和fma特性。
+> - 模型转换工具的编译时间较长，若非必要，建议通过`MSLITE_ENABLE_CONVERTER`关闭转换工具编译，以加快编译速度。
 
 - runtime功能裁减编译选项
 
@@ -246,11 +249,13 @@ MindSpore根目录下的`build.bat`脚本可用于MindSpore Lite的编译。
 | -------- | ----- | ---- | ---- |
 | MSLITE_ENABLE_SSE | 是否启用SSE指令集 | on、off | off |
 | MSLITE_ENABLE_AVX | 是否启用AVX指令集（该选项暂不支持Visual Studio编译器） | on、off | off |
+| MSLITE_ENABLE_AVX512 | 是否启用AVX512指令集（该选项暂不支持Visual Studio编译器） | on、off | off |
 | MSLITE_ENABLE_CONVERTER | 是否编译模型转换工具（该选项暂不支持Visual Studio编译器） | on、off | on |
 | MSLITE_ENABLE_TOOLS | 是否编译配套工具 | on、off | on |
 | MSLITE_ENABLE_TESTCASES | 是否编译测试用例 | on、off | off |
 
 > - 以上选项可通过设置同名环境变量或者`mindspore/lite/CMakeLists.txt`文件修改。
+> - 模型转换工具的编译时间较长，若非必要，建议通过`MSLITE_ENABLE_CONVERTER`关闭转换工具编译，以加快编译速度。
 
 ### 编译示例
 
diff --git a/docs/lite/docs/source_zh_cn/use/converter_register.md b/docs/lite/docs/source_zh_cn/use/converter_register.md
index 50062a073ee10c1b304895bd1aaa14247723f44e..12092fcac3b501abe5a743e3ac861dd989c8889f 100644
--- a/docs/lite/docs/source_zh_cn/use/converter_register.md
+++ b/docs/lite/docs/source_zh_cn/use/converter_register.md
@@ -79,7 +79,7 @@ REG_SCHEDULED_PASS(POSITION_BEGIN, {"PassTutorial"})  // 注册调度逻辑
 
 示例代码可参考[pass](https://gitee.com/mindspore/mindspore/tree/master/mindspore/lite/examples/converter_extend/pass)。
 
-> 在离线转换阶段，我们会对模型的每一个节点的输出张量进行推断，包括输出张量的Format、DataType以及Shape，因此，离线转换阶段，用户需提供自己实现的算子的推断过程，这里用户可以参考[算子Infershape扩展](https://www.mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html#id19)说明，示例代码可参考[infer](https://gitee.com/mindspore/mindspore/tree/master/mindspore/lite/examples/converter_extend/infer)。
+> 在离线转换阶段，我们会对模型的每一个节点的输出张量进行推断，包括输出张量的Format、DataType以及Shape，因此，离线转换阶段，用户需提供自己实现的算子的推断过程，这里用户可以参考[算子Infershape扩展](https://www.mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html#扩展使用)说明，示例代码可参考[infer](https://gitee.com/mindspore/mindspore/tree/master/mindspore/lite/examples/converter_extend/infer)。
 
 ## 示例演示
 
@@ -94,7 +94,7 @@ REG_SCHEDULED_PASS(POSITION_BEGIN, {"PassTutorial"})  // 注册调度逻辑
 
 - 编译准备
 
-  MindSpore Lite的发布件不会提供其他框架下的序列化文件，因此，用户需自行编译获得，请参考[概述](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_register.html#id1)。
+  MindSpore Lite的发布件不会提供其他框架下的序列化文件，因此，用户需自行编译获得，请参考[概述](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_register.html#概述)。
 
   本示例采用的是tflite模型，用户需编译[flatbuffers](https://gitee.com/mindspore/mindspore/blob/master/cmake/external_libs/flatbuffers.cmake)，从[MindSpore仓](https://gitee.com/mindspore/mindspore/tree/master)中获取[TFLITE原型文件](https://gitee.com/mindspore/mindspore/blob/master/mindspore/lite/tools/converter/parser/tflite/schema.fbs)，最终生成tflite的序列化文件。
 
diff --git a/docs/lite/docs/source_zh_cn/use/converter_tool.md b/docs/lite/docs/source_zh_cn/use/converter_tool.md
index c1cdcda3167def6d123c8778b11a0c3b36b32bbf..4911b10a0b21c10ce711eb41d650c338d4c7ff2a 100644
--- a/docs/lite/docs/source_zh_cn/use/converter_tool.md
+++ b/docs/lite/docs/source_zh_cn/use/converter_tool.md
@@ -71,7 +71,7 @@ MindSpore Lite模型转换工具提供了多种参数设置，用户可根据需
 > - Caffe模型一般分为两个文件：`*.prototxt`模型结构，对应`--modelFile`参数；`*.caffemodel`模型权值，对应`--weightFile`参数。
 > - `--fp16`的优先级很低，比如如果开启了量化，那么对于已经量化的权重，`--fp16`不会再次生效。总而言之，该选项只会在序列化时对模型中的Float32的权重生效。
 > - `inputDataFormat`：一般在集成NCHW规格的三方硬件场景下(例如[集成NNIE使用说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/nnie.html#nnie))，设为NCHW比NHWC会有较明显的性能提升。在其他场景下，用户也可按需设置。
-> - `configFile`配置文件采用`key=value`的方式定义相关参数，量化相关的配置参数详见[训练后量化](https://www.mindspore.cn/lite/docs/zh-CN/master/use/post_training_quantization.html)，扩展功能相关的配置参数详见[扩展配置](https://www.mindspore.cn/lite/docs/zh-CN/master/use/nnie.html#id6)。
+> - `configFile`配置文件采用`key=value`的方式定义相关参数，量化相关的配置参数详见[训练后量化](https://www.mindspore.cn/lite/docs/zh-CN/master/use/post_training_quantization.html)，扩展功能相关的配置参数详见[扩展配置](https://www.mindspore.cn/lite/docs/zh-CN/master/use/nnie.html#扩展配置)。
 
 ### 使用示例
 
@@ -164,7 +164,7 @@ mindspore-lite-{version}-win-x64
 
 ### 参数说明
 
-参考Linux环境模型转换工具的[参数说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#id3)。
+参考Linux环境模型转换工具的[参数说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#参数说明)。
 
 ### 使用示例
 
diff --git a/docs/lite/docs/source_zh_cn/use/cropper_tool.md b/docs/lite/docs/source_zh_cn/use/cropper_tool.md
index e8c439c07a3618ac964ca611c96731fb196d583b..f9ba2e8da2720a3801681aa782f37f2ccf578c7e 100644
--- a/docs/lite/docs/source_zh_cn/use/cropper_tool.md
+++ b/docs/lite/docs/source_zh_cn/use/cropper_tool.md
@@ -14,9 +14,9 @@ MindSpore Lite提供对Runtime的`libmindspore-lite.a`静态库裁剪工具，
 
 使用MindSpore Lite裁剪工具，需要进行如下环境准备工作。
 
-- 编译：裁剪工具代码在MindSpore源码的`mindspore/lite/tools/cropper`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id1)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id3)编译x86_64版本。
+- 编译：裁剪工具代码在MindSpore源码的`mindspore/lite/tools/cropper`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#编译示例)编译x86_64版本。
 
-- 运行：参考构建文档中的[编译输出](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id4)，获得`cropper`工具。
+- 运行：参考构建文档中的[编译输出](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#目录结构)，获得`cropper`工具。
 
 ## 参数说明
 
diff --git a/docs/lite/docs/source_zh_cn/use/delegate.md b/docs/lite/docs/source_zh_cn/use/delegate.md
index 263ddcf89d67284822298bcff585a9fe14a46af0..4ec5e64d7b6936897fd1378e5d3acd038f537646 100644
--- a/docs/lite/docs/source_zh_cn/use/delegate.md
+++ b/docs/lite/docs/source_zh_cn/use/delegate.md
@@ -14,7 +14,7 @@ MindSpore Lite的Delegate接口用于支持第三方AI框架（例如：NPU、Te
 
 1. 新增自定义Delegate类：继承[Delegate](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#delegate)类实现自定义的Delegate。
 2. 实现初始化接口：[Init](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#init)接口实现判断运行设备是否支持Delegate框架，初始化Delegate资源等功能。
-3. 实现构图接口：[Build](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#id24)接口要实现算子支持判断、子图构建、在线构图功能。
+3. 实现构图接口：[Build](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#build)接口要实现算子支持判断、子图构建、在线构图功能。
 4. 实现子图Kernel：继承[Kernel](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore_kernel.html#kernel)实现Delegate的子图Kernel。
 
 ### 新增自定义Delegate类
@@ -47,7 +47,7 @@ Status XXXDelegate::Init() {
 
 ### 实现构图接口
 
-构图接口[Build(DelegateModel *model)](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#id24)接口的入参是[DelegateModel](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#delegatemodel)的实例。
+构图接口[Build(DelegateModel *model)](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#build)接口的入参是[DelegateModel](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#delegatemodel)的实例。
 
 > `DelegateModel`中，[std::vector<kernel::Kernel *> *kernels_](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#kernel)是已经完成MindSpore Lite内置算子注册、经过拓扑排序的算子列表。
 >
diff --git a/docs/lite/docs/source_zh_cn/use/downloads.md b/docs/lite/docs/source_zh_cn/use/downloads.md
index d11ceca5b3bb880365611bce086a281932a6ee16..38ed8cb9397adbd08644e2bdc1836d170db22d68 100644
--- a/docs/lite/docs/source_zh_cn/use/downloads.md
+++ b/docs/lite/docs/source_zh_cn/use/downloads.md
@@ -6,6 +6,22 @@
 
 欢迎使用MindSpore Lite，我们提供了支持多种操作系统和硬件平台的模型转换、模型推理、图像处理等功能，你可以下载适用于本地环境的版本包直接使用。
 
+其中Linux-x86_64目标运行系统为Linux，底层架构为x86-64，已在Linux发行版Euleros2.0、Centos7.8、Ubuntu18.04版本上经过测试验证。
+
+## 1.6.0
+
+|   组件   | 硬件平台 |   操作系统   |   链接   |   SHA-256   |
+|   ---   |   ---   |     ---     |   ---   |     ---     |
+| 推理/训练runtime、推理/训练aar包、以及benchmark工具 | CPU     | Android-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/android/cpu/mindspore-lite-1.6.0-android-aarch32.tar.gz> | d043803cffc8a0b75409aab3e4039f1e86756cf618af1538a76865e9fa4fd481 |
+| 推理/训练runtime、推理/训练aar包、以及benchmark工具 | CPU/GPU | Android-aarch64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/android/gpu/mindspore-lite-1.6.0-android-aarch64.tar.gz> | 25188266621f4cfedb24970a9a98ef6190fe02c9d034b7285f360da425ffe9d6 |
+| 推理/训练runtime、推理/训练jar包、以及benchmark/codegen/converter/cropper工具 | CPU     | Linux-x86_64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/linux/x86_64/mindspore-lite-1.6.0-linux-x64.tar.gz> | 90472996359f64509f38036ed8100605c76dcdc42453c2fc7156048eb981708c |
+| 推理runtime以及benchmark/codegen/converter工具 | CPU     | Windows-x86_64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/windows/mindspore-lite-1.6.0-win-x64.zip> | 4460b8f1bf321eca005074dccffb54d6d3164ba3f78ce34530ec20db4dbc9980 |
+| iOS 推理runtime | CPU | iOS-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/ios/mindspore-lite-1.6.0-ios-aarch32.tar.gz> | 72fe007660abe9c51d0a1852b094fb52d8bbd1610c989e79c9858937102aa59f |
+| iOS 推理runtime | CPU | iOS-aarch64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/ios/mindspore-lite-1.6.0-ios-aarch64.tar.gz> | 51bd5f7c21477d7856bea33d31e059f578b6b964a7c43e440e97c44b186db4a4 |
+| NNIE converter工具 | CPU     | Linux-x86_64 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/linux/nnie/mindspore-lite-1.6.0-linux-x64.tar.gz> | 81c2a5dadf51978d1c80f75c63fde4edefa2897792ac571fd33ffd35e338736b |
+| NNIE 推理runtime及benchmark工具 | Hi3516D | Linux-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/release/linux/nnie/Hi3516D/mindspore-lite-1.6.0-linux-aarch32.tar.gz> | 8133c2326e2defa3614f86592d5691fdb410a4296898e254a33cd33a7e519b16 |
+| 轻鸿蒙推理runtime | Hi3516D | OpenHarmony-aarch32 | <https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/lite/ohos_aarch32/mindspore-lite-1.6.0-ohos-aarch32.tar.gz> | d5daafac4bdcd0d03158e2a7cd3f881869b49cfb77d9654a24ddd967edbe5e91 |
+
 ## 1.5.0
 
 |   组件   | 硬件平台 |   操作系统   |   链接   |   SHA-256   |
diff --git a/docs/lite/docs/source_zh_cn/use/nnie.md b/docs/lite/docs/source_zh_cn/use/nnie.md
index ed3698ac24105e352e3da6ff49f21eb0c4b29900..93764a8761fee35471dd02b051a510a22b271ed1 100644
--- a/docs/lite/docs/source_zh_cn/use/nnie.md
+++ b/docs/lite/docs/source_zh_cn/use/nnie.md
@@ -37,7 +37,7 @@ mindspore-lite-{version}-linux-aarch32
         └── libmslite_proposal.so  # 集成proposal的样例动态库
 ```
 
-上述是NNIE的集成目录结构，推理工具runtime的其余目录结构详情，见[目录结构](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id4)。
+上述是NNIE的集成目录结构，推理工具runtime的其余目录结构详情，见[目录结构](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#目录结构)。
 
 ## 工具使用
 
@@ -142,7 +142,7 @@ nnie.cfg文件的示例参考如下：
      CONVERTER RESULT SUCCESS:0
      ```
 
-     用户若想了解converter_lite转换工具的相关参数，可参考[参数说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#id4)。
+     用户若想了解converter_lite转换工具的相关参数，可参考[参数说明](https://www.mindspore.cn/lite/docs/zh-CN/master/use/converter_tool.html#参数说明)。
 
 ### 推理工具runtime
 
@@ -337,7 +337,7 @@ ${model_path}为转换后ms模型文件路径
 
   MindSpore Lite在转换NNIE模型时，会将大部分的算子融合为NNIE运行的二进制文件，用户无法观察到中间算子的输出，通过在top域上添加”_report“后缀，转换构图时会将中间算子的输出添加到融合后的层输出中，若原先该算子便有输出（未被融合），则维持不变。
 
-  在推理运行时，用户可通过[回调运行](https://www.mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html#id15)得到中间算子输出。
+  在推理运行时，用户可通过[回调运行](https://www.mindspore.cn/lite/docs/zh-CN/master/use/runtime_cpp.html#回调运行)得到中间算子输出。
 
   MindSpore Lite解析_report的相应规则，及与[inplace机制](#inplace机制)的冲突解决，参照《HiSVP 开发指南》中的定义说明。
 
diff --git a/docs/lite/docs/source_zh_cn/use/obfuscator_tool.md b/docs/lite/docs/source_zh_cn/use/obfuscator_tool.md
index 0457c23b49fbc6580eabbce24f073a76695accf3..49e12f80403a1333c1143691156ce33d4d650e25 100644
--- a/docs/lite/docs/source_zh_cn/use/obfuscator_tool.md
+++ b/docs/lite/docs/source_zh_cn/use/obfuscator_tool.md
@@ -14,7 +14,7 @@ MindSpore Lite提供一个轻量级的离线模型混淆工具，可用于保护
 
 使用MindSpore Lite模型混淆工具，需要进行如下环境准备工作。
 
-- 参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id1)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#id3)编译x86_64版本。
+- 参考构建文档中的[环境要求](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求)和[编译示例](https://www.mindspore.cn/lite/docs/zh-CN/master/use/build.html#编译示例)编译x86_64版本。
 
 ### 目录结构
 
diff --git a/docs/lite/docs/source_zh_cn/use/post_training_quantization.md b/docs/lite/docs/source_zh_cn/use/post_training_quantization.md
index 56d1ca1712c43216cf72300551df2d0c822335f7..d127ebf126f7ea159b42ff495da0606ec5ebf705 100644
--- a/docs/lite/docs/source_zh_cn/use/post_training_quantization.md
+++ b/docs/lite/docs/source_zh_cn/use/post_training_quantization.md
@@ -217,7 +217,7 @@ min_quant_weight_channel=16
 
 全量化计算激活值的量化参数，用户需要提供校准数据集。校准数据集最好来自真实推理场景，能表征模型的实际输入情况，数量在100个左右。
 
-针对图片数据，目前支持通道调整、归一化、缩放、裁剪等预处理的功能。用户可以根据推理时所需的预处理操作，设置相应的[参数](https://www.mindspore.cn/lite/docs/zh-CN/master/use/post_training_quantization.html#id7)。
+针对图片数据，目前支持通道调整、归一化、缩放、裁剪等预处理的功能。用户可以根据推理时所需的预处理操作，设置相应的[参数](https://www.mindspore.cn/lite/docs/zh-CN/master/use/post_training_quantization.html#数据预处理)。
 
 全量化转换命令的一般形式为：
 
diff --git a/docs/lite/docs/source_zh_cn/use/runtime_cpp.md b/docs/lite/docs/source_zh_cn/use/runtime_cpp.md
index 86da93f360301e3ee02b1fa1573c87837949b469..f9ba15275a4c52ecd3471eaf2065fbccb41350ca 100644
--- a/docs/lite/docs/source_zh_cn/use/runtime_cpp.md
+++ b/docs/lite/docs/source_zh_cn/use/runtime_cpp.md
@@ -288,7 +288,7 @@ MindSpore Lite提供两种方法来获取模型的输入Tensor。
    // Users need to free input_buf.
    ```
 
-> MindSpore Lite的模型输入Tensor中的数据排布必须是`NHWC`。如果需要了解更多数据前处理过程，可参考基于JNI接口的Android应用开发中[编写端侧推理代码](https://www.mindspore.cn/lite/docs/zh-CN/master/quick_start/quick_start.html#id10)的第2步，将输入图片转换为传入MindSpore模型的Tensor格式。
+> MindSpore Lite的模型输入Tensor中的数据排布必须是`NHWC`。如果需要了解更多数据前处理过程，可参考基于JNI接口的Android应用开发中[编写端侧推理代码](https://www.mindspore.cn/lite/docs/zh-CN/master/quick_start/quick_start.html#编写端侧推理代码)的第2步，将输入图片转换为传入MindSpore模型的Tensor格式。
 >
 > [GetInputs](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#getinputs)和[GetInputByTensorName](https://www.mindspore.cn/lite/api/zh-CN/master/api_cpp/mindspore.html#getinputbytensorname)方法返回的数据不需要用户释放。
 
diff --git a/docs/lite/faq/requirements.txt b/docs/lite/faq/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/lite/faq/requirements.txt
+++ b/docs/lite/faq/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/mindarmour/api/requirements.txt b/docs/mindarmour/api/requirements.txt
index f2424ce6f08f8ce69b2f1bb181bc09da2641d35d..1e7189447f37b0e2e1c98a2ac5e82da36756a493 100644
--- a/docs/mindarmour/api/requirements.txt
+++ b/docs/mindarmour/api/requirements.txt
@@ -1,4 +1,5 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
 opencv-python
\ No newline at end of file
diff --git a/docs/mindarmour/docs/requirements.txt b/docs/mindarmour/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/mindarmour/docs/requirements.txt
+++ b/docs/mindarmour/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/mindarmour/docs/source_en/conf.py b/docs/mindarmour/docs/source_en/conf.py
index b9f143de66b8503ab664e5c9b2098701a52c4ed4..e8091add2480a75778c807c34eb5188aa66eb74e 100644
--- a/docs/mindarmour/docs/source_en/conf.py
+++ b/docs/mindarmour/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindarmour
@@ -114,20 +113,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindarmour/docs/source_en/faq.md b/docs/mindarmour/docs/source_en/faq.md
index de406f38321ef6be077ca854323488e5fdebc0ce..4c064845bcafd0c4645e5ef0277997c99f11d6ff 100644
--- a/docs/mindarmour/docs/source_en/faq.md
+++ b/docs/mindarmour/docs/source_en/faq.md
@@ -1,6 +1,6 @@
 # FAQ
 
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/mindarmour/faq/source_en/faq.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/mindarmour/docs/source_en/faq.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
 
 <font size=3>**Q: What should I do when FastGradientSignMethod does not specify loss_fn, it reports an error: `Function construct_wrapper, the number of parameters of this function is 9, but the number of provided arguments is 10.`**</font>
 
diff --git a/docs/mindarmour/docs/source_en/index.rst b/docs/mindarmour/docs/source_en/index.rst
index 4f7fe8c35905b3d8c804c48adfa2e17e588fafb1..f2567cec82407b1bfac6508e1e77cd243052a3d2 100644
--- a/docs/mindarmour/docs/source_en/index.rst
+++ b/docs/mindarmour/docs/source_en/index.rst
@@ -3,8 +3,9 @@ MindArmour Documents
 
 As a general technology, AI brings great opportunities and benefits, but also faces new security and privacy protection challenges. MindArmour is a subsystem of MindSpore. It provides security and privacy protection for MindSpore, including adversarial robustness, model security test, differential privacy training, privacy risk assessment, and data drift detection.
 
-.. image:: ./images/mindarmour.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindarmour/docs/source_en/images/mindarmour.png" width="700px" alt="" >
 
 Typical MindArmour Application Scenarios
 -----------------------------------------
diff --git a/docs/mindarmour/docs/source_zh_cn/conf.py b/docs/mindarmour/docs/source_zh_cn/conf.py
index ef02fc30cacbc16899c6e612edbb8eb7376590d9..065330c5731faa17f52b967ab9c5120e82c24c92 100644
--- a/docs/mindarmour/docs/source_zh_cn/conf.py
+++ b/docs/mindarmour/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindarmour
@@ -114,20 +113,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindarmour/docs/source_zh_cn/faq.md b/docs/mindarmour/docs/source_zh_cn/faq.md
index ae562faab408b8a0fba1b95290e9bea4a3074cae..a6952d7ad19d6f583f95e221d3536660b2980da5 100644
--- a/docs/mindarmour/docs/source_zh_cn/faq.md
+++ b/docs/mindarmour/docs/source_zh_cn/faq.md
@@ -1,6 +1,6 @@
 # FAQ
 
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/mindarmour/faq/source_zh_cn/faq.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/mindarmour/docs/source_zh_cn/faq.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
 
 <font size=3>**Q: FastGradientSignMethod未指定loss_fn时报错`Function construct_wrapper, The number of parameters of this function is 9, but the number of provided arguments is 10.`怎么办？**</font>
 
diff --git a/docs/mindarmour/docs/source_zh_cn/index.rst b/docs/mindarmour/docs/source_zh_cn/index.rst
index 8335f92f9bd2ad350b21df36f00c700984912427..33a25ed1627cca6d6bedfd8f9ed2b895032f6094 100644
--- a/docs/mindarmour/docs/source_zh_cn/index.rst
+++ b/docs/mindarmour/docs/source_zh_cn/index.rst
@@ -3,8 +3,9 @@ MindArmour 文档
 
 AI作为一种通用技术，在带来巨大机遇和效益的同时也面临着新的安全与隐私保护的挑战。MindArmour是昇思MindSpore的一个子项目，为昇思MindSpore提供安全与隐私保护能力，主要包括对抗鲁棒性、模型安全测试、差分隐私训练、隐私泄露风险评估、数据漂移检测等技术。
 
-.. image:: ./images/mindarmour_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindarmour/docs/source_zh_cn/images/mindarmour_cn.png" width="700px" alt="" >
 
 使用MindArmour的典型场景
 ------------------------------
diff --git a/docs/mindarmour/faq/requirements.txt b/docs/mindarmour/faq/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/mindarmour/faq/requirements.txt
+++ b/docs/mindarmour/faq/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/mindinsight/api/requirements.txt b/docs/mindinsight/api/requirements.txt
index c47c7c8ff90e45f1e82e4c77c21c0e404f8b5b71..82711b2877bab025b77468be8612e79fed32fd30 100644
--- a/docs/mindinsight/api/requirements.txt
+++ b/docs/mindinsight/api/requirements.txt
@@ -1,4 +1,5 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
 torch>=1.8.2
diff --git a/docs/mindinsight/docs/requirements.txt b/docs/mindinsight/docs/requirements.txt
index 412ae8c2254c231eaa625b4e3f09c8ff3c88203d..91b4fcd07762187b4ec0653a3d0fe7e4c68c6e34 100644
--- a/docs/mindinsight/docs/requirements.txt
+++ b/docs/mindinsight/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/mindinsight/docs/source_en/conf.py b/docs/mindinsight/docs/source_en/conf.py
index 1fd161bd11c76aee01e4d50cf0af3165ec40f204..16c3884daddcfc3ad5bda13919adad2c352c6ac2 100644
--- a/docs/mindinsight/docs/source_en/conf.py
+++ b/docs/mindinsight/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 from sphinx.ext import autodoc as sphinx_autodoc
 
 import mindinsight
@@ -111,20 +110,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindinsight/docs/source_en/dashboard.md b/docs/mindinsight/docs/source_en/dashboard.md
index ad7ffbb5e336130deaad8646971ea35176f175dd..2fd8705f9809affe3ce19412fcb4be9e7b1b9c77 100644
--- a/docs/mindinsight/docs/source_en/dashboard.md
+++ b/docs/mindinsight/docs/source_en/dashboard.md
@@ -4,7 +4,7 @@
 
 ## Overview
 
-Training dashboard is an important part of mindinsight's visualization component, and its tags include scalar visualization, parameter distribution visualization, computational graph visualization, data graph visualization, image visualization and tensor visualization.
+Training dashboard is an important part of mindinsight's visualization component, and its tags include scalar visualization, parameter distribution visualization, computational graph visualization, data graph visualization, image visualization, tensor visualization and training optimization process visualization.
 
 Access the Training Dashboard by selecting a specific training from the training list.
 
@@ -97,7 +97,12 @@ Figure 7 shows the function area of the computational graph, including:
 
 Figure 8 shows the readability optimization feature, which optimizes the readability of the graph and reduces the complexity of the graph, and removes most of the gradient and optimizer operators.
 
-Note: to get the clearest visualization of computational graph, please avoid to use public methods which cross cell and set `jit_level` to `o0` when collecting the computational graph, please refer to the API [mindspore.Model.build](https://www.mindspore.cn/docs/api/en/master/api_python/mindspore/mindspore.Model.html#mindspore.Model.build).
+Note:
+
+- To get the clearest visualization of computational graph, please avoid to use public methods which cross Cell.
+- Set `jit_level` to `o0` when collecting the computational graph, please refer to the API [mindspore.Model.build](https://www.mindspore.cn/docs/api/en/master/api_python/mindspore/mindspore.Model.html#mindspore.Model.build).
+- In the computation graph optimization process, some operators in different namespaces may be merged with each other due to the same function, which will cause a cycle between namespaces and affect the readability.
+- The complete control flow is not supported at present. If you need, please specify the control branch in the script.
 
 ## Dataset Graph Visualization
 
@@ -164,6 +169,10 @@ Figure 13 shows tensors recorded by a user in a form of a table which includes t
 
 Figure 14 shows tensors recorded by a user in a form of a histogram. Click the upper right corner to zoom in the histogram.
 
+## Training Optimization Process Visualization
+
+The training optimization process visualization can show the optimization space around the neural network training path. For more information, please refer to  [Training Optimization Process Visualization](https://www.mindspore.cn/mindinsight/docs/en/master/landscape.html).
+
 ## Notices
 
 1. Currently MindSpore supports recording computational graph after operator fusion for Ascend 910 AI processor only.
diff --git a/docs/mindinsight/docs/source_en/debugger_offline.md b/docs/mindinsight/docs/source_en/debugger_offline.md
index 799443feffbc7ac246acf53ba17f1616d4f2d101..40282e695157eb5b00c1a63e7d0f85c6c6bf0dad 100644
--- a/docs/mindinsight/docs/source_en/debugger_offline.md
+++ b/docs/mindinsight/docs/source_en/debugger_offline.md
@@ -101,22 +101,26 @@ import mindinsight.debugger as debugger
 from mindinsight.debugger import DumpAnalyzer as DumpAnalyzer
 from mindinsight.debugger import Watchpoint as Watchpoint
 
-# Init DumpAnalyzer with the dump_dir
-analyzer = DumpAnalyzer("/path/to/dump_dir")
-# Select the tensors generated by the code in 'lenet.py', line 49
-tensors = analyzer.select_tensors(query_string="/path/to/src/of/lenet.py:49", select_by="code_stack")
-# Create a watchpoint for tensors with condition TensorTooLarge, set the parameter abs_mean_gt=0.001
-watchpoint1 = Watchpoint(tensors, debugger.TensorTooLargeCondition(abs_mean_gt=0.001))
-# Create another watchpoint for tensors with condition TensorAllZero, set the parameter zero_percentage_ge=99.9
-watchpoint2 = Watchpoint(tensors, debugger.TensorAllZeroCondition(zero_percentage_ge=99.9))
-# Check the given watchpoints
-hits = analyzer.check_watchpoints([watchpoint1, watchpoint2])
-# Show the result
-for hit in hits:
-    print("The hit detail is: {}".format(hit.get_hit_detail()))
-    tensor = hit.tensor
-    print("The hit tensor info is: iteration: {}, graph_name: {}, node_name: {}, rank: {}, slot: {}"
-          .format(tensor.iteration, tensor.node.graph_name, tensor.node.name, tensor.node.name, tensor.rank, tensor.slot))
+def test_debugger_offline():
+    # Init DumpAnalyzer with the dump_dir
+    analyzer = DumpAnalyzer("/path/to/dump_dir")
+    # Select the tensors generated by the code in 'lenet.py', line 49
+    tensors = analyzer.select_tensors(query_string="/path/to/src/of/lenet.py:49", select_by="code_stack")
+    # Create a watchpoint for tensors with condition TensorTooLarge, set the parameter abs_mean_gt=0.001
+    watchpoint1 = Watchpoint(tensors, debugger.TensorTooLargeCondition(abs_mean_gt=0.001))
+    # Create another watchpoint for tensors with condition TensorAllZero, set the parameter zero_percentage_ge=99.9
+    watchpoint2 = Watchpoint(tensors, debugger.TensorAllZeroCondition(zero_percentage_ge=99.9))
+    # Check the given watchpoints, the check_watchpoints function start a new process needs to be called through the main entry
+    hits = analyzer.check_watchpoints([watchpoint1, watchpoint2])
+    # Show the result
+    for hit in hits:
+        print("The hit detail is: {}".format(hit.get_hit_detail()))
+        tensor = hit.tensor
+        print("The hit tensor info is: iteration: {}, graph_name: {}, node_name: {}, rank: {}, slot: {}"
+            .format(tensor.iteration, tensor.node.graph_name, tensor.node.name, tensor.node.name, tensor.rank, tensor.slot))
+
+if __name__ == "__main__":
+    test_debugger_offline()
 ```
 
 ## Precautions
@@ -124,7 +128,7 @@ for hit in hits:
 - Scenarios:
     - The offline debugger does not support the CPU scenario currently.
     - The offline debugger supports the single-node multi-device scenario. To analyze the multi-node multi-device scenario, you need to summarize the data of multiple nodes.
-    - The offline debugger does not support checking the initial weight and operator overflow currently.
+    - The offline debugger does not support checking the initial weight.
     - The offline debugger does not support PyNative mode.
 
 - GPU scenario:
diff --git a/docs/mindinsight/docs/source_en/debugger_online.md b/docs/mindinsight/docs/source_en/debugger_online.md
index 8310d15cda85820f01c8ab556d608da1696c8c7b..214798a9513a886e19cbd2f4f1d46dc2e0978f6b 100644
--- a/docs/mindinsight/docs/source_en/debugger_online.md
+++ b/docs/mindinsight/docs/source_en/debugger_online.md
@@ -106,7 +106,6 @@ After a watchpoint is created, manually select the node to be checked and click
 The following conditions are supported (abbreviations in parentheses):
 
 - Tensor check
-    - Operator overflow (OO): Check whether overflow occurs during operator computation. Only the Ascend AI Processor is supported.
     - Whether tensor values are all 0 (TZ): Set the threshold to `Percentage of 0 values ≥` to check the percentage of 0 tensor values.
     - Tensor overflow (TO): Check whether a tensor value overflow occurs.
     - Tensor value range (TR): Set a threshold to check the tensor value range. The options are `Percentage of the value in the range >`, `Percentage of the value in the range <`, `MAX-MIN>` and `MAX-MIN<`. If setting the threshold to `Percentage of the value in the range >` or `Percentage of the value in the range <`, you need to set the `Upper limit of the range (inclusive)` or `Lower limit of the range (inclusive)` at the same time.
@@ -256,7 +255,6 @@ Tensors can be downloaded in tensor check view. Users can download the desired t
 
 - When using the debugger, make sure that the version numbers of MindInsight and MindSpore are the same.
 - Recheck only watchpoints that have tensor values.
-- To check overflow during computation, you need to enable the overflow detection function of the asynchronous dump. For details about how to enable the function, see [Asynchronous Dump](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#asynchronous-dump).
 - The graph displayed by the debugger is the finally optimized execution graph. The called operator may have been integrated with other operators, or the name of the called operator is changed after optimization.
 - Enabling the debugger will turn off memory reuse mode, which may lead to an 'out of memory' error when the training network is too large.
 
diff --git a/docs/mindinsight/docs/source_en/index.rst b/docs/mindinsight/docs/source_en/index.rst
index c747cf5f8d1f46b8bf6e5b3b12db625f640447be..333e2e3ef7dd4aaad5200a0ddee032a1eb042489 100644
--- a/docs/mindinsight/docs/source_en/index.rst
+++ b/docs/mindinsight/docs/source_en/index.rst
@@ -12,8 +12,9 @@ MindInsight provides the following functions:
 - `Hyperparameter optimization <https://www.mindspore.cn/mindinsight/docs/en/master/hyper_parameters_auto_tuning.html>`_
 - `Model migration <https://www.mindspore.cn/mindinsight/docs/en/master/migrate_3rd_scripts_mindconverter.html>`_
 
-.. image:: ./images/mindinsight_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindinsight/docs/source_en/images/mindinsight_en.png" width="700px" alt="" >
 
 Using MindInsight to Visualize the Training Process
 ----------------------------------------------------
diff --git a/docs/mindinsight/docs/source_en/landscape.md b/docs/mindinsight/docs/source_en/landscape.md
index 4e2dd9e12b41c3c8a289831dd20762429e67180b..a8bfbc61852cd21fde629ac5ceb8bd8b4dc47688 100644
--- a/docs/mindinsight/docs/source_en/landscape.md
+++ b/docs/mindinsight/docs/source_en/landscape.md
@@ -1,4 +1,4 @@
-# Visualization of Training Optimization Process
+# Training Optimization Process Visualization
 
 <a href="https://gitee.com/mindspore/docs/blob/master/docs/mindinsight/docs/source_en/landscape.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>&nbsp;&nbsp;
 
@@ -158,9 +158,96 @@ The specific use steps are divided into two steps. Taking the classification tas
 2. Landscape drawing: using the model parameters saved in the training process, the model and dataset are consistent with the training, start a new script, and generate landscape information through forward calculation without re-training. (applicable to drawing landscape by single device or multi devices Parallel Computing)
 
    ```python
+   import mindspore.dataset as ds
+   import mindspore.dataset.vision.c_transforms as CV
+   import mindspore.dataset.transforms.c_transforms as C
+   from mindspore.dataset.vision import Inter
+   from mindspore import dtype as mstype
+   import mindspore.nn as nn
+
+   from mindspore import Model
+   from mindspore.common.initializer import Normal
    from mindspore.nn import Loss
    from mindspore.train.callback import SummaryLandscape
 
+   def create_dataset(data_path, batch_size=32, repeat_size=1,
+                      num_parallel_workers=1):
+       """
+       create dataset for train or test
+       """
+       # define dataset
+       mnist_ds = ds.MnistDataset(data_path, shuffle=False)
+
+       resize_height, resize_width = 32, 32
+       rescale = 1.0 / 255.0
+       shift = 0.0
+       rescale_nml = 1 / 0.3081
+       shift_nml = -1 * 0.1307 / 0.3081
+
+       # define map operations
+       resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+       rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+       rescale_op = CV.Rescale(rescale, shift)
+       hwc2chw_op = CV.HWC2CHW()
+       type_cast_op = C.TypeCast(mstype.int32)
+
+       # apply map operations on images
+       mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+
+       # apply DatasetOps
+       buffer_size = 10000
+       mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+       mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+       mnist_ds = mnist_ds.repeat(repeat_size)
+
+       return mnist_ds
+
+   class LeNet5(nn.Cell):
+       """
+       Lenet network
+
+       Args:
+           num_class (int): Number of classes. Default: 10.
+           num_channel (int): Number of channels. Default: 1.
+
+       Returns:
+           Tensor, output tensor
+       Examples:
+           >>> LeNet(num_class=10)
+
+       """
+       def __init__(self, num_class=10, num_channel=1, include_top=True):
+           super(LeNet5, self).__init__()
+           self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid', weight_init=Normal(0.02))
+           self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid', weight_init=Normal(0.02))
+           self.relu = nn.ReLU()
+           self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+           self.include_top = include_top
+           if self.include_top:
+               self.flatten = nn.Flatten()
+               self.fc1 = nn.Dense(16 * 5 * 5, 120)
+               self.fc2 = nn.Dense(120, 84)
+               self.fc3 = nn.Dense(84, num_class)
+
+       def construct(self, x):
+           x = self.conv1(x)
+           x = self.relu(x)
+           x = self.max_pool2d(x)
+           x = self.conv2(x)
+           x = self.relu(x)
+           x = self.max_pool2d(x)
+           if not self.include_top:
+               return x
+           x = self.flatten(x)
+           x = self.relu(self.fc1(x))
+           x = self.relu(self.fc2(x))
+           x = self.fc3(x)
+           return x
+
    def callback_fn():
        network = LeNet5(10)
        net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
diff --git a/docs/mindinsight/docs/source_en/lineage_and_scalars_comparison.md b/docs/mindinsight/docs/source_en/lineage_and_scalars_comparison.md
index 5fe54d5358582029ad0f1da80e2118e99e154136..81bfe4fa3b92b7db4bf98eb1a2eaea5fbe0b1ecc 100644
--- a/docs/mindinsight/docs/source_en/lineage_and_scalars_comparison.md
+++ b/docs/mindinsight/docs/source_en/lineage_and_scalars_comparison.md
@@ -64,7 +64,7 @@ Figure 7 shows the data processing and augmentation information of all model tra
 
 ## Scalars Comparison
 
-Scalars Comparison can be used to compare scalar curves between multiple trainings
+Scalars Comparison can be used to compare scalar curves and loss graph between multiple trainings. For the detailed information of loss graph comparison, please refer to [Training Optimization Process Visualization](https://www.mindspore.cn/mindinsight/docs/en/master/landscape.html).
 
 ![multi_scalars.png](./images/multi_scalars.png)
 
diff --git a/docs/mindinsight/docs/source_en/performance_profiling_ascend.md b/docs/mindinsight/docs/source_en/performance_profiling_ascend.md
index 2e78d0ba334181c79f1e9e551abb1e16fb773f78..0733d4c6dd1f44a11d8eeb3abd2d4e3b1d0a0d55 100644
--- a/docs/mindinsight/docs/source_en/performance_profiling_ascend.md
+++ b/docs/mindinsight/docs/source_en/performance_profiling_ascend.md
@@ -232,6 +232,8 @@ The red box in Figure 5 includes calculation quantity data on operator granulari
 
 Figure 6 is a sankey diagram that presents data in the structure of a tree where the cursor selects a scope to see the specific FLOPs value.
 
+> This figure only draws the Scope hierarchy structure of the operator (the specific name of the operator at the last layer is not shown). Since the depth of each operator's hierarchy is not equal in the training process, it may occur that the sum of time of adjacent levels is not equal.
+
 ### Data Preparation Performance Analysis
 
 The Data preparation performance analysis component is used to analyse the execution of data input pipeline for the training. The data input pipeline can be divided into three stages:  
diff --git a/docs/mindinsight/docs/source_en/performance_profiling_ascend_of_cluster.md b/docs/mindinsight/docs/source_en/performance_profiling_ascend_of_cluster.md
index ff04a7b9a5ef6489df75ddbd93f53bf79f9c0f5e..102f2d942ebdb0a4cc79da742afc32f0150f29ab 100644
--- a/docs/mindinsight/docs/source_en/performance_profiling_ascend_of_cluster.md
+++ b/docs/mindinsight/docs/source_en/performance_profiling_ascend_of_cluster.md
@@ -284,6 +284,10 @@ To use MindInsight to visualize communication performance data, you need to inst
 pip install /usr/local/Ascend/tools/hccl_parser-{version}-py3-none-any.whl
 ```
 
+### specifications
+
+For data parsing performance, the number of files generated when cluster communication is enabled is currently limited. Currently, the maximum number of original communication performance files (named with the suffix.trace) generated by MindSpore is 500. When the original communication data exceeds the upper limit, the step number of Cluster Communication may be inconsistent with the step number of cluster Step Trace.
+
 ## Resource Utilization
 
 ### Cluster Memory Analysis
diff --git a/docs/mindinsight/docs/source_en/summary_record.md b/docs/mindinsight/docs/source_en/summary_record.md
index 73a08c5cc157a87b14c7387e6e6608d655f1c29a..af451e1ea6fc58daaebb530a6ec386ac4d473135 100644
--- a/docs/mindinsight/docs/source_en/summary_record.md
+++ b/docs/mindinsight/docs/source_en/summary_record.md
@@ -4,17 +4,17 @@
 
 ## Overview
 
-Scalars, images, computational graphs, and model hyperparameters during training are recorded in files and can be viewed on the web page.
+Scalars, images, computational graphs, training optimization process, and model hyperparameters during training are recorded in files and can be viewed on the web page.
 
 ## Operation Process
 
-- Prepare a training script, specify scalars, images, computational graphs, and model hyperparameters in the training script, record them in the summary log file, and run the training script.
+- Prepare a training script, specify scalars, images, computational graphs, training optimization process, and model hyperparameters in the training script, record them in the summary log file, and run the training script.
 - Start MindInsight and specify the summary log file directory using startup parameters. After MindInsight is started, access the visualization page based on the IP address and port number. The default access IP address is `http://127.0.0.1:8080`.
 - During the training, when data is written into the summary log file, you can view the data on the web page.
 
 ## Preparing The Training Script
 
-Currently, MindSpore supports to save scalars, images, computational graph, and model hyperparameters to summary log file and display them on the web page. The computational graph can only be recorded in the graph mode.
+Currently, MindSpore supports to save scalars, images, computational graph, training optimization process, and model hyperparameters to summary log file and display them on the web page. The computational graph can only be recorded in the graph mode. The detailed process of data collection and landscape drawing in the training optimization process can be referred to  [Training Optimization Process Visualization](https://www.mindspore.cn/mindinsight/docs/en/master/landscape.html).
 
 MindSpore currently supports multiple ways to record data into summary log files.
 
@@ -120,7 +120,8 @@ if __name__ == '__main__':
 ```
 
 > 1. When using summary, it is recommended that you set `dataset_sink_mode` argument of `model.train` to `False`. Please see notices for more information.
-> 2. When using summary, you need to run the code in `if __name__ == "__main__"`. For more detail, refer to [Python tutorial](https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing-programming)
+> 2. When using summary, you need to run the code in `if __name__ == "__main__"`. For more detail, refer to [Python tutorial](https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing-programming).
+> 3. dataset_path is the path to the user's local training dataset.
 
 ### Method two: Custom collection of network data with summary operators and SummaryCollector
 
diff --git a/docs/mindinsight/docs/source_zh_cn/accuracy_optimization.md b/docs/mindinsight/docs/source_zh_cn/accuracy_optimization.md
index 1f4e1f50ef679abd4ec4882683dd0aada0c09a91..e7f7f216da8b099af33d437ac104fd9ba930c6d8 100644
--- a/docs/mindinsight/docs/source_zh_cn/accuracy_optimization.md
+++ b/docs/mindinsight/docs/source_zh_cn/accuracy_optimization.md
@@ -212,6 +212,8 @@
     model.eval(ds_eval, callbacks=[summary_collector])
     ```
 
+    > dataset_path为用户本地的训练数据集路径。
+
     使用训练看板[可视化功能](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html)查看训练过程数据：
 
     ![mindinsight_dashboard](./images/mindinsight_dashboard.png)
@@ -305,14 +307,13 @@ MindInsight可以辅助用户对输入数据、数据处理流水线进行检查
 6. 激活值饱和或过弱（例如Sigmoid的输出接近1，Relu的输出全为0）；
 7. 梯度爆炸、消失；
 8. 训练epoch不足；
-9. 算子计算结果存在NAN、INF；
-10. 算子计算过程溢出（计算过程中的溢出不一定都是有害的）等。
+9. 算子计算结果存在NAN、INF等。
 
 上述这些问题或现象，有的可以通过loss表现出来，有的则难以观察。MindInsight提供了针对性的功能，可以观察上述现象、自动检查问题，帮助您更快定位问题根因。例如：
 
 - MindInsight的参数分布图模块可以展示模型权重随训练过程的变化趋势；
 - MindInsight的张量可视模块可以展示张量的具体取值，对不同张量进行对比；
-- [MindInsight调试器](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger.html)内置了种类丰富，功能强大的检查能力，可以检查权重问题（例如权重不更新、权重更新过大、权重值过大/过小）、梯度问题（例如梯度消失、梯度爆炸）、激活值问题（例如激活值饱和或过弱）、张量全为0、NAN/INF、算子计算过程溢出等问题。
+- [MindInsight调试器](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger.html)内置了种类丰富，功能强大的检查能力，可以检查权重问题（例如权重不更新、权重更新过大、权重值过大/过小）、梯度问题（例如梯度消失、梯度爆炸）、激活值问题（例如激活值饱和或过弱）、张量全为0、NAN/INF等问题。
 
 ![loss](./images/loss.png)
 
diff --git a/docs/mindinsight/docs/source_zh_cn/accuracy_problem_preliminary_location.md b/docs/mindinsight/docs/source_zh_cn/accuracy_problem_preliminary_location.md
index 93aa18eb61d0ff18a2e9ca47740a29830bb37be1..89e3055b0fc9224944b910819d689a15d6f6a9ea 100644
--- a/docs/mindinsight/docs/source_zh_cn/accuracy_problem_preliminary_location.md
+++ b/docs/mindinsight/docs/source_zh_cn/accuracy_problem_preliminary_location.md
@@ -303,7 +303,7 @@ MindSpore API同其它框架的API存在一定差异。有标杆脚本的情况
 
 ### 常见计算图结构问题
 
-为了检查计算图结构问题，请读者首先参考[收集Summary数据](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html)，将计算图保存到summary文件中，然后使用MindInsight[可视化查看计算图](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id5)。
+为了检查计算图结构问题，请读者首先参考[收集Summary数据](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html)，将计算图保存到summary文件中，然后使用MindInsight[可视化查看计算图](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#计算图可视化)。
 
 检查结论：
 
@@ -384,9 +384,7 @@ MindSpore API同其它框架的API存在一定差异。有标杆脚本的情况
 检查方法：
 当使用[混合精度](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/enable_mixed_precision.html)训练，或者是使用Ascend AI处理器训练时，建议检查是否存在溢出问题。
 
-使用GPU时，通过[调试器](https://mindspore.cn/mindinsight/docs/zh-CN/master/debugger_online.html#id10)中的“检查张量溢出”监测点可以进行溢出检查。
-
-使用Ascend AI处理器时，使能溢出检查的详细方法请见[异步Dump文档](https://mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id11)。使能溢出检查时，注意设置op_debug_mode为3，开启全部溢出检测功能。若在指定的目录存在算子溢出信息文件，则说明存在溢出问题，反之，则说明不存在溢出问题。
+使用GPU时，通过[调试器](https://mindspore.cn/mindinsight/docs/zh-CN/master/debugger_online.html#异常现象检查列表)中的“检查张量溢出”监测点可以进行溢出检查。
 
 发现溢出问题后，应首先找到并分析第一个出现溢出的节点（对于Ascend的溢出数据，可以按文件名中的时间戳，找时间戳最小的一个；对于GPU上的溢出，只要找执行序中最靠前的一个），结合算子的输入输出数据确定溢出原因。
 
@@ -477,7 +475,7 @@ MindSpore API同其它框架的API存在一定差异。有标杆脚本的情况
 
 ## 求助方式
 
-参考上面两种初步定位方法的任意一种进行操作。若未发现可疑点，一般说明脚本不存在明显的问题，此时请参考[精度调优建议](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/accuracy_optimization.html#id12)进行调优。若使用基于现象对比的定位方法发现了疑点，请依据定位方法中的提示判断是需要自行定位的问题还是向MindSpore求助。若使用checklist发现了疑点或问题，请参考[精度问题详细定位和调优指南](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/accuracy_optimization.html)进行详细定位。
+参考上面两种初步定位方法的任意一种进行操作。若未发现可疑点，一般说明脚本不存在明显的问题，此时请参考[精度调优建议](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/accuracy_optimization.html#常用调优建议)进行调优。若使用基于现象对比的定位方法发现了疑点，请依据定位方法中的提示判断是需要自行定位的问题还是向MindSpore求助。若使用checklist发现了疑点或问题，请参考[精度问题详细定位和调优指南](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/accuracy_optimization.html)进行详细定位。
 
 当您遇到精度问题，要向MindSpore求助时，提供相关材料将有助于我们更好地判断和解决您的问题。建议您提供的材料包括但不限于：
 
diff --git a/docs/mindinsight/docs/source_zh_cn/conf.py b/docs/mindinsight/docs/source_zh_cn/conf.py
index 44cfeddef5e70f03a601123b7bb560a80a97ac00..3803ec2c356d017a2481ef45cf9201954f9f0381 100644
--- a/docs/mindinsight/docs/source_zh_cn/conf.py
+++ b/docs/mindinsight/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -85,20 +84,9 @@ intersphinx_mapping = {
     'numpy': ('https://docs.scipy.org/doc/numpy/', '../../../../resource/numpy_objects.inv'),
 }
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 # Modify default signatures for autodoc.
 autodoc_source_path = os.path.abspath(sphinx_autodoc.__file__)
diff --git a/docs/mindinsight/docs/source_zh_cn/dashboard.md b/docs/mindinsight/docs/source_zh_cn/dashboard.md
index d59aac09e15db6d0f0e5fe357e584006162fd00b..c87de61821df950a7aa75b7b5ac67c204e212978 100644
--- a/docs/mindinsight/docs/source_zh_cn/dashboard.md
+++ b/docs/mindinsight/docs/source_zh_cn/dashboard.md
@@ -4,7 +4,7 @@
 
 ## 概述
 
-训练看板是MindInsight的可视化组件的重要组成部分，而训练看板的标签包含：标量可视化、参数分布图可视化、计算图可视化、数据图可视化、图像可视化和张量可视化等。
+训练看板是MindInsight的可视化组件的重要组成部分，而训练看板的标签包含：标量可视化、参数分布图可视化、计算图可视化、数据图可视化、图像可视化、张量可视化和优化过程可视化等。
 
 用户从训练列表中选择指定的训练，进入训练看板。
 
@@ -96,7 +96,12 @@
 
 图8展示了优化可读性功能，该功能优化了计算图的可读性，降低计算图的复杂度，图中大部分的梯度计算逻辑和优化器计算逻辑将会被移除。
 
-注意：为达到最清晰的计算图可视化效果，请勿使用跨cell的公共函数，并在收集计算图时设置`jit_level`为`o0`，请参考[mindspore.Model.build接口定义](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore/mindspore.Model.html?highlight=jit_config#mindspore.Model.build) 。
+注意：
+
+- 为达到最清晰的计算图可视化效果，请勿使用跨Cell的公共函数。
+- 收集计算图时设置`jit_level`为`o0`，详细请参考[mindspore.Model.build接口定义](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore/mindspore.Model.html#mindspore.Model.build) 。
+- 在计算图优化时，不同命名空间中的多个算子可能会因为功能一致而融合，这种情况会导致命名空间之间的连线成环，影响可读性。
+- 暂不支持展示完整的控制流，如需展示请在脚本中指定控制分支。
 
 ## 数据图可视化
 
@@ -163,6 +168,10 @@
 
 图14将用户所记录的张量以直方图的形式进行展示。点击图中右上角，可以将图放大。
 
+## 优化过程可视化
+
+优化过程可视化可以将将神经网络训练路径周围的优化空间展示出来，更多信息请查阅[训练优化过程可视化](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/landscape.html)。
+
 ## 注意事项
 
 1. 目前MindSpore仅支持在Ascend 910 AI处理器上导出算子融合后的计算图。
diff --git a/docs/mindinsight/docs/source_zh_cn/debugger_offline.md b/docs/mindinsight/docs/source_zh_cn/debugger_offline.md
index d19c8da9be1759cb8b460889433d844a533b729b..b7a2317fe302cbbf9f96bf0f6808cbab34b0ec39 100644
--- a/docs/mindinsight/docs/source_zh_cn/debugger_offline.md
+++ b/docs/mindinsight/docs/source_zh_cn/debugger_offline.md
@@ -58,7 +58,7 @@ mindinsight start --port {PORT} --summary-base-dir {SUMMARY_BASE_DIR} --offline-
 
 ## 离线调试器页面介绍
 
-离线调试器界面与在线调试器大致相同。在线调试器的页面介绍详见[在线调试器页面介绍](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_online.html#id6) 。不同的是，离线调试器会在计算图的上方显示图执行历史，并且可以重置训练轮次。
+离线调试器界面与在线调试器大致相同。在线调试器的页面介绍详见[在线调试器页面介绍](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_online.html#调试器页面介绍) 。不同的是，离线调试器会在计算图的上方显示图执行历史，并且可以重置训练轮次。
 
 ### 图执行历史
 
@@ -90,9 +90,9 @@ mindinsight start --port {PORT} --summary-base-dir {SUMMARY_BASE_DIR} --offline-
 
     此时，调试器处于加载离线数据的状态。
 
-2. 稍等片刻，在MindInsight UI上可以看到弹窗，提示选择是否使用推荐监测点，接下来的使用步骤与在线调试相同。[使用调试器进行调试](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_online.html#id17) 。
+2. 稍等片刻，在MindInsight UI上可以看到弹窗，提示选择是否使用推荐监测点，接下来的使用步骤与在线调试相同。[使用调试器进行调试](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_online.html#使用调试器进行调试) 。
 
-3. 如果需要重置训练轮次，可以参考[训练轮次重置](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_offline.html#id7) 来重置训练轮次。每个轮次的数据保存情况可以参考[图执行历史](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_offline.html#id6) 来查看。
+3. 如果需要重置训练轮次，可以参考[训练轮次重置](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_offline.html#训练轮次重置) 来重置训练轮次。每个轮次的数据保存情况可以参考[图执行历史](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/debugger_offline.html#图执行历史) 来查看。
 
 ## 离线调试器API使用样例
 
@@ -101,22 +101,26 @@ import mindinsight.debugger as debugger
 from mindinsight.debugger import DumpAnalyzer as DumpAnalyzer
 from mindinsight.debugger import Watchpoint as Watchpoint
 
-# Init DumpAnalyzer with the dump_dir
-analyzer = DumpAnalyzer("/path/to/dump_dir")
-# Select the tensors generated by the code in 'lenet.py', line 49
-tensors = analyzer.select_tensors(query_string="/path/to/src/of/lenet.py:49", select_by="code_stack")
-# Create a watchpoint for tensors with condition TensorTooLarge, set the parameter abs_mean_gt=0.001
-watchpoint1 = Watchpoint(tensors, debugger.TensorTooLargeCondition(abs_mean_gt=0.001))
-# Create another watchpoint for tensors with condition TensorAllZero, set the parameter zero_percentage_ge=99.9
-watchpoint2 = Watchpoint(tensors, debugger.TensorAllZeroCondition(zero_percentage_ge=99.9))
-# Check the given watchpoints
-hits = analyzer.check_watchpoints([watchpoint1, watchpoint2])
-# Show the result
-for hit in hits:
-    print("The hit detail is: {}".format(hit.get_hit_detail()))
-    tensor = hit.tensor
-    print("The hit tensor info is: iteration: {}, graph_name: {}, node_name: {}, rank: {}, slot: {}"
-          .format(tensor.iteration, tensor.node.graph_name, tensor.node.name, tensor.node.name, tensor.rank, tensor.slot))
+def test_debugger_offline():
+    # Init DumpAnalyzer with the dump_dir
+    analyzer = DumpAnalyzer("/path/to/dump_dir")
+    # Select the tensors generated by the code in 'lenet.py', line 49
+    tensors = analyzer.select_tensors(query_string="/path/to/src/of/lenet.py:49", select_by="code_stack")
+    # Create a watchpoint for tensors with condition TensorTooLarge, set the parameter abs_mean_gt=0.001
+    watchpoint1 = Watchpoint(tensors, debugger.TensorTooLargeCondition(abs_mean_gt=0.001))
+    # Create another watchpoint for tensors with condition TensorAllZero, set the parameter zero_percentage_ge=99.9
+    watchpoint2 = Watchpoint(tensors, debugger.TensorAllZeroCondition(zero_percentage_ge=99.9))
+    # Check the given watchpoints, the check_watchpoints function start a new process needs to be called through the main entry
+    hits = analyzer.check_watchpoints([watchpoint1, watchpoint2])
+    # Show the result
+    for hit in hits:
+        print("The hit detail is: {}".format(hit.get_hit_detail()))
+        tensor = hit.tensor
+        print("The hit tensor info is: iteration: {}, graph_name: {}, node_name: {}, rank: {}, slot: {}"
+            .format(tensor.iteration, tensor.node.graph_name, tensor.node.name, tensor.node.name, tensor.rank, tensor.slot))
+
+if __name__ == "__main__":
+    test_debugger_offline()
 ```
 
 ## 注意事项
@@ -124,7 +128,7 @@ for hit in hits:
 - 场景支持：
     - 离线调试器暂不支持CPU场景。
     - 离线调试器支持单机多卡场景。若要分析多机多卡的场景。需要自行把多机数据汇总到一起。
-    - 离线调试器暂不支持初始权重和计算过程溢出的检查。
+    - 离线调试器暂不支持初始权重的检查。
     - 离线调试器暂不支持PyNative模式。
 
 - GPU场景：
diff --git a/docs/mindinsight/docs/source_zh_cn/debugger_online.md b/docs/mindinsight/docs/source_zh_cn/debugger_online.md
index 6e4a714f4e8c1d341c174691498faf1e69bb3185..58d2623180ddd8e5027553522ec6a233b0944128 100644
--- a/docs/mindinsight/docs/source_zh_cn/debugger_online.md
+++ b/docs/mindinsight/docs/source_zh_cn/debugger_online.md
@@ -101,7 +101,6 @@ mindinsight start --port {PORT} --enable-debugger True --debugger-port {DEBUGGER
 支持的条件包括（括号中为缩写）：
 
 - 检查张量
-    - 检查计算过程溢出（OO）：检查算子计算过程中是否存在溢出现象，仅支持昇腾AI处理器。
     - 检查张量是否全为0（TZ）：通过对条件参数设置阈值来检查张量的0值比例，可选参数为`0值比例>=`。
     - 检查张量溢出（TO）：检查张量值是否存在溢出现象。
     - 检查张量值范围（TR）：通过对条件参数设置阈值来检查张量值的范围，可选参数为`在范围中的值所占百分比>`、`在范围中的值所占百分比<`、`MAX-MIN>`和`MAX-MIN<`。其中在设置`在范围中的值所占百分比>`和`在范围中的值所占百分比<`时需要同时设置支持参数`范围上界（含）`和`范围下界（含）`。
@@ -249,6 +248,5 @@ mindinsight start --port {PORT} --enable-debugger True --debugger-port {DEBUGGER
 
 - 使用调试器时要保证MindInsight和MindSpore的版本号相同。
 - 重新检查只检查当前有张量值的监测点。
-- 检查计算过程溢出需要用户开启异步Dump的全部溢出检测功能，开启方式请参照[异步Dump功能介绍](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id5)
 - 调试器展示的图是优化后的最终执行图。调用的算子可能已经与其它算子融合，或者在优化后改变了名称。
 - 开启调试器会关闭内存复用，在训练网络过大时有可能导致'out of memory'错误。
diff --git a/docs/mindinsight/docs/source_zh_cn/hyper_parameters_auto_tuning.md b/docs/mindinsight/docs/source_zh_cn/hyper_parameters_auto_tuning.md
index 4c1c2e6e05ee0bbdc08f9846357e91e89936ab52..aa15cffb895ae2b11046cc4fdc283eab445205c2 100644
--- a/docs/mindinsight/docs/source_zh_cn/hyper_parameters_auto_tuning.md
+++ b/docs/mindinsight/docs/source_zh_cn/hyper_parameters_auto_tuning.md
@@ -189,7 +189,7 @@ optional arguments:
 
 4. 可视化
 
-    基于config.yaml里面配置的summary_base_dir来启动MindInsight，启动方法可以查看[MindInsight启动命令](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/mindinsight_commands.html#id3)。
+    基于config.yaml里面配置的summary_base_dir来启动MindInsight，启动方法可以查看[MindInsight启动命令](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/mindinsight_commands.html#启动服务)。
 
 ## 注意事项
 
diff --git a/docs/mindinsight/docs/source_zh_cn/index.rst b/docs/mindinsight/docs/source_zh_cn/index.rst
index f9e77aae375653514de5883f2144bd9ef4f09c63..ca7338c71f27928fe806fa1e3c1d883078c35300 100644
--- a/docs/mindinsight/docs/source_zh_cn/index.rst
+++ b/docs/mindinsight/docs/source_zh_cn/index.rst
@@ -12,8 +12,9 @@ MindInsight包括以下内容：
 - `超参调优 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/hyper_parameters_auto_tuning.html>`_
 - `模型迁移 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/migrate_3rd_scripts_mindconverter.html>`_
 
-.. image:: ./images/mindinsight.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindinsight/docs/source_zh_cn/images/mindinsight.png" width="700px" alt="" >
 
 使用MindInsight可视化训练过程
 ------------------------------
@@ -22,18 +23,18 @@ MindInsight包括以下内容：
 
    在训练脚本中使用SummaryCollector记录训练信息，再执行训练。
 
-2. `启动MindInsight可视化训练 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/mindinsight_commands.html#id3>`_
+2. `启动MindInsight可视化训练 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/mindinsight_commands.html#启动服务>`_
 
    启动MindInsight，并通过 ``--summary-base-dir`` 参数指定summary日志文件目录。
 
-3. `查看训练看板 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/mindinsight_commands.html#id3>`_
+3. `查看训练看板 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html>`_
 
    在浏览器中打开MindInsight访问地址，点击“训练看板”按钮查看详细信息。
 
 使用MindInsight分析模型性能
 ---------------------------
 
-1. `收集模型分析数据 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id3>`_
+1. `收集模型分析数据 <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#准备训练脚本>`_
 
    在训练脚本中调用MindSpore Profiler相关接口，再执行训练。
 
diff --git a/docs/mindinsight/docs/source_zh_cn/landscape.md b/docs/mindinsight/docs/source_zh_cn/landscape.md
index 05636f5dc168872ec3c11897b2623c7cae8d5dcc..9b6f216b55e54114a3aecc47c57f622adb64487c 100644
--- a/docs/mindinsight/docs/source_zh_cn/landscape.md
+++ b/docs/mindinsight/docs/source_zh_cn/landscape.md
@@ -162,9 +162,96 @@
 2. 地形图绘制：利用训练过程中保存的模型参数，模型与数据集与训练一致，启动新的脚本，正向计算生成地形图信息，不用再次进行训练。（适用于单卡或多卡并行计算绘制地形图）
 
    ```python
+   import mindspore.dataset as ds
+   import mindspore.dataset.vision.c_transforms as CV
+   import mindspore.dataset.transforms.c_transforms as C
+   from mindspore.dataset.vision import Inter
+   from mindspore import dtype as mstype
+   import mindspore.nn as nn
+
+   from mindspore.common.initializer import Normal
+   from mindspore import Model
    from mindspore.nn import Loss
    from mindspore.train.callback import SummaryLandscape
 
+   def create_dataset(data_path, batch_size=32, repeat_size=1,
+                      num_parallel_workers=1):
+       """
+       create dataset for train or test
+       """
+       # define dataset
+       mnist_ds = ds.MnistDataset(data_path, shuffle=False)
+
+       resize_height, resize_width = 32, 32
+       rescale = 1.0 / 255.0
+       shift = 0.0
+       rescale_nml = 1 / 0.3081
+       shift_nml = -1 * 0.1307 / 0.3081
+
+       # define map operations
+       resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
+       rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
+       rescale_op = CV.Rescale(rescale, shift)
+       hwc2chw_op = CV.HWC2CHW()
+       type_cast_op = C.TypeCast(mstype.int32)
+
+       # apply map operations on images
+       mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+       mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+
+       # apply DatasetOps
+       buffer_size = 10000
+       mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
+       mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
+       mnist_ds = mnist_ds.repeat(repeat_size)
+
+       return mnist_ds
+
+   class LeNet5(nn.Cell):
+       """
+       Lenet network
+
+       Args:
+           num_class (int): Number of classes. Default: 10.
+           num_channel (int): Number of channels. Default: 1.
+
+       Returns:
+           Tensor, output tensor
+       Examples:
+           >>> LeNet(num_class=10)
+
+       """
+       def __init__(self, num_class=10, num_channel=1, include_top=True):
+           super(LeNet5, self).__init__()
+           self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid', weight_init=Normal(0.02))
+           self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid', weight_init=Normal(0.02))
+           self.relu = nn.ReLU()
+           self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+           self.include_top = include_top
+           if self.include_top:
+               self.flatten = nn.Flatten()
+               self.fc1 = nn.Dense(16 * 5 * 5, 120)
+               self.fc2 = nn.Dense(120, 84)
+               self.fc3 = nn.Dense(84, num_class)
+
+       def construct(self, x):
+           x = self.conv1(x)
+           x = self.relu(x)
+           x = self.max_pool2d(x)
+           x = self.conv2(x)
+           x = self.relu(x)
+           x = self.max_pool2d(x)
+           if not self.include_top:
+               return x
+           x = self.flatten(x)
+           x = self.relu(self.fc1(x))
+           x = self.relu(self.fc2(x))
+           x = self.fc3(x)
+           return x
+
    def callback_fn():
        network = LeNet5(10)
        net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
diff --git a/docs/mindinsight/docs/source_zh_cn/lineage_and_scalars_comparison.md b/docs/mindinsight/docs/source_zh_cn/lineage_and_scalars_comparison.md
index 85fd3d0ff2ab44da2d3902c7dcd6ad97ababafa3..4604fb118547ed9a0d8f0ed5170a3010a047adf4 100644
--- a/docs/mindinsight/docs/source_zh_cn/lineage_and_scalars_comparison.md
+++ b/docs/mindinsight/docs/source_zh_cn/lineage_and_scalars_comparison.md
@@ -64,7 +64,7 @@ MindInsight中的模型溯源、数据溯源和对比看板同训练看板一样
 
 ## 对比看板
 
-对比看板可视用于多个训练之间的标量曲线对比。
+对比看板可视用于多个训练之间的标量曲线对比以及损失函数图形对比，其中损失函数图形对比的详细信息可查阅[训练优化过程可视化](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/landscape.html)。
 
 ![multi_scalars.png](./images/multi_scalars.png)
 
diff --git a/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend.md b/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend.md
index 2af5b5e4f41976ce0bc1332f6c33bc786dea4b17..529788d2c8e93a28504d996d6f1499d1c5bb57cf 100644
--- a/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend.md
+++ b/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend.md
@@ -233,6 +233,8 @@ profiler.analyse()
 
 图6是一个桑基图，以一种树的结构展示数据，其中光标选中某个scope能看到具体的FLOPs值。
 
+> 该图仅绘制算子的Scope层级结构（不展示最后一层算子的具体名字），由于训练过程中各算子的层级深度不相等，可能出现相邻层级时间总和不相等的情况。
+
 ### 数据准备性能分析
 
 使用数据准备性能分析组件可以对训练数据准备过程进行性能分析。数据准备过程可以分为三个阶段：数据处理pipeline、数据发送至Device以及Device侧读取训练数据。数据准备性能分析组件会对每个阶段的处理性能进行详细分析，并将分析结果进行展示。
diff --git a/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend_of_cluster.md b/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend_of_cluster.md
index 741d5bb8f1c7a45774bc8b1ec36f89e7d8c384de..e16716075fcfd34d0d5ea0e7f8965a03595791b0 100644
--- a/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend_of_cluster.md
+++ b/docs/mindinsight/docs/source_zh_cn/performance_profiling_ascend_of_cluster.md
@@ -284,6 +284,10 @@ done
 pip install /usr/local/Ascend/tools/hccl_parser-{version}-py3-none-any.whl
 ```
 
+### 规格
+
+出于对数据解析性能的考虑，当前对开启集群通信生成的文件数量进行了限制，目前MindSpore侧生成的原始通信性能文件（.trace后缀命名）数量上限为500。当通信原始数据超出上限，可能出现集群通信step数与集群迭代轨迹step数不一致的情况。
+
 ## 资源利用
 
 ### 集群内存使用情况分析
diff --git a/docs/mindinsight/docs/source_zh_cn/performance_profiling_gpu.md b/docs/mindinsight/docs/source_zh_cn/performance_profiling_gpu.md
index 5e79e7b0e2c27095687ccf05029c5add84bf6e16..9e8f749abc4a72f0047fba27671b4466e81b19a9 100644
--- a/docs/mindinsight/docs/source_zh_cn/performance_profiling_gpu.md
+++ b/docs/mindinsight/docs/source_zh_cn/performance_profiling_gpu.md
@@ -30,7 +30,7 @@
 
 - 在训练结束后，调用`Profiler.analyse`停止性能数据收集并生成性能分析结果。
 
-样例代码与Ascend使用方式一致，可以参考：<https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id3>
+样例代码与Ascend使用方式一致，可以参考：<https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#准备训练脚本>
 
 GPU场景可自定义callback方式收集性能，但数据准备阶段、数据下沉模式不支持该方式收集性能数据。
 
@@ -124,13 +124,13 @@ GPU场景下，Timeline分析的使用方法和Ascend场景相同，不同之处
 
 GPU场景下，迭代轨迹分析的使用方法和Ascend场景相同。（注意：**迭代轨迹暂不支持异构训练场景**）
 
-使用方法可参考：<https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id5>
+使用方法可参考：<https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#迭代轨迹分析>
 
 ### 数据准备性能分析
 
 GPU场景下，数据准备性能分析的使用方法和Ascend场景相同。
 
-使用方法可参考：<https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id7>
+使用方法可参考：<https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#数据准备性能分析>
 
 ## 资源利用
 
diff --git a/docs/mindinsight/docs/source_zh_cn/performance_tuning_guide.md b/docs/mindinsight/docs/source_zh_cn/performance_tuning_guide.md
index 92acdcb5f88383ea319ac5728aae02f73a047075..b5cef24f3a666e34251d774a68b63b8558282809 100644
--- a/docs/mindinsight/docs/source_zh_cn/performance_tuning_guide.md
+++ b/docs/mindinsight/docs/source_zh_cn/performance_tuning_guide.md
@@ -60,7 +60,7 @@ MindInsight在性能调优的单卡页面为用户提供了`迭代轨迹`标签
 
 - 若用户脚本中不存在耗时的自定义逻辑，说明框架将数据从Host侧发送到Device侧耗时较长，请到[MindSpore社区](https://gitee.com/mindspore/mindspore/issues) 进行反馈。
 
-步骤2：跳转到`数据准备详情`页的`数据处理`标签页，观察算子间队列，确定数据处理具体哪个算子存在性能瓶颈。判断原则请见[性能调试](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id8) 页面的`数据处理pipeline分析`部分。找到存在性能问题的算子后，可参考[优化数据处理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/optimize_data_processing.html) 页面尝试提高数据处理算子的性能。
+步骤2：跳转到`数据准备详情`页的`数据处理`标签页，观察算子间队列，确定数据处理具体哪个算子存在性能瓶颈。判断原则请见[性能调试](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#数据准备性能分析) 页面的`数据处理pipeline分析`部分。找到存在性能问题的算子后，可参考[优化数据处理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/optimize_data_processing.html) 页面尝试提高数据处理算子的性能。
 
 #### 数据下沉模式
 
@@ -71,7 +71,7 @@ MindInsight在性能调优的单卡页面为用户提供了`迭代轨迹`标签
 
 步骤2：查看主机队列Size曲线的变化情况。若该队列Size都不是0，说明训练数据从Host发往Device的流程为性能瓶颈点，请到[MindSpore社区](https://gitee.com/mindspore/mindspore/issues) 反馈；否则说明数据处理流程是性能瓶颈点，请参照步骤3继续定位数据处理哪个算子存在性能问题。
 
-步骤3：跳转到`数据准备详情页的数据处理标签页`观察算子间队列，确定数据处理具体哪个算子存在性能瓶颈。判断原则请见[性能调试](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id8) 页面的`数据处理pipeline分析`部分。找到存在性能问题的算子后，可参考[优化数据处理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/optimize_data_processing.html) 页面尝试提高数据处理算子的性能。
+步骤3：跳转到`数据准备详情页的数据处理标签页`观察算子间队列，确定数据处理具体哪个算子存在性能瓶颈。判断原则请见[性能调试](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#数据准备性能分析) 页面的`数据处理pipeline分析`部分。找到存在性能问题的算子后，可参考[优化数据处理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/optimize_data_processing.html) 页面尝试提高数据处理算子的性能。
 
 ### 前反向耗时长
 
diff --git a/docs/mindinsight/docs/source_zh_cn/summary_record.md b/docs/mindinsight/docs/source_zh_cn/summary_record.md
index 1b5651a80d9dce0ee2ae02ebd0e605fa5381c41d..1a2be4143cade79a8b42698c7e720eef6070baa7 100644
--- a/docs/mindinsight/docs/source_zh_cn/summary_record.md
+++ b/docs/mindinsight/docs/source_zh_cn/summary_record.md
@@ -4,11 +4,11 @@
 
 ## 概述
 
-训练过程中的标量、图像、计算图以及模型超参等信息记录到文件中，通过可视化界面供用户查看。
+训练过程中的标量、图像、计算图、训练优化过程以及模型超参等信息记录到文件中，通过可视化界面供用户查看。
 
 ## 操作流程
 
-- 准备训练脚本，并在训练脚本中指定标量、图像、计算图、模型超参等信息记录到summary日志文件，接着运行训练脚本。
+- 准备训练脚本，并在训练脚本中指定标量、图像、计算图、训练优化过程、模型超参等信息记录到summary日志文件，接着运行训练脚本。
 - 启动MindInsight，并通过启动参数指定summary日志文件目录，启动成功后，根据IP和端口访问可视化界面，默认访问地址为 `http://127.0.0.1:8080`。
 - 在训练过程中，有数据写入summary日志文件时，即可在页面中[查看训练看板中可视的数据](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html)。
 
@@ -16,7 +16,7 @@
 
 ## 准备训练脚本
 
-当前MindSpore支持将标量、图像、计算图、模型超参等信息保存到summary日志文件中，并通过可视化界面进行展示。计算图数据仅能在图模式下记录。
+当前MindSpore支持将标量、图像、计算图、训练优化过程、模型超参等信息保存到summary日志文件中，并通过可视化界面进行展示。计算图数据仅能在图模式下记录，训练优化过程数据收集及地形图绘制的详细流程可参考[训练优化过程可视化](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/landscape.html)。
 
 MindSpore目前支持多种方式将数据记录到summary日志文件中。
 
@@ -125,6 +125,7 @@ if __name__ == '__main__':
 
 > 1. 使用summary功能时，建议将`model.train`的`dataset_sink_mode`参数设置为`False`。请参考文末的注意事项。
 > 2. 使用summary功能时，需要将代码放置到`if __name__ == "__main__"`中运行。详情请[参考Python官网介绍](https://docs.python.org/zh-cn/3.7/library/multiprocessing.html#multiprocessing-programming)。
+> 3. dataset_path为用户本地的训练数据集路径。
 
 ### 方式二：结合Summary算子和SummaryCollector，自定义收集网络中的数据
 
diff --git a/docs/mindinsight/faq/requirements.txt b/docs/mindinsight/faq/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/mindinsight/faq/requirements.txt
+++ b/docs/mindinsight/faq/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/mindquantum/api/requirements.txt b/docs/mindquantum/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/mindquantum/api/requirements.txt
+++ b/docs/mindquantum/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/mindquantum/docs/requirements.txt b/docs/mindquantum/docs/requirements.txt
index e96c888345b8dd6ef8151075764a7847c5982b0c..5e132d85aecbc652067257757273ac8bc8cca5ee 100644
--- a/docs/mindquantum/docs/requirements.txt
+++ b/docs/mindquantum/docs/requirements.txt
@@ -1,6 +1,7 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
 numpy
diff --git a/docs/mindquantum/docs/source_en/conf.py b/docs/mindquantum/docs/source_en/conf.py
index a1cf5fa242c184ef65e79fcf34f86445de3e766d..7da1fabc96e847e57ecd3636882de42b8b91683f 100644
--- a/docs/mindquantum/docs/source_en/conf.py
+++ b/docs/mindquantum/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 import sphinx.ext.autosummary.generate as g
 from sphinx.ext import autodoc as sphinx_autodoc
@@ -128,21 +127,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    f.seek(0)
-    f.writelines(contents)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 
diff --git a/docs/mindquantum/docs/source_en/index.rst b/docs/mindquantum/docs/source_en/index.rst
index 3ba3bb48fd784ad30885c92ec3d7a0d61b3610af..e7abba12e293c223df2782c3a7fcfc299c8eb336 100644
--- a/docs/mindquantum/docs/source_en/index.rst
+++ b/docs/mindquantum/docs/source_en/index.rst
@@ -1,10 +1,11 @@
 MindQuantum Documents
 ======================
 
-MindQuantum is a general quantum computing library developed by MindSpore and HiQ, that can be used to build and train different quantum neural networks. Thanks to the powerful algorithm of quantum software group of Huawei and High-performance automatic differentiation ability of MindSpore, MindQuantum can efficiently handle problems such as quantum machine learning, quantum chemistry simulation, and quantum optimization, which provides an efficient platform for researchers, teachers and students to quickly design and verify quantum machine learning algorithms.
+MindQuantum is a general quantum computing framework developed by MindSpore and HiQ, that can be used to build and train different quantum neural networks. Thanks to the powerful algorithm of quantum software group of Huawei and High-performance automatic differentiation ability of MindSpore, MindQuantum can efficiently handle problems such as quantum machine learning, quantum chemistry simulation, and quantum optimization, which provides an efficient platform for researchers, teachers and students to quickly design and verify quantum machine learning algorithms.
 
-.. image:: ./images/mindquantum_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindquantum/docs/source_en/images/mindquantum_en.png" width="700px" alt="" >
 
 Typical MindQuantum Application Scenarios
 ------------------------------------------
diff --git a/docs/mindquantum/docs/source_zh_cn/conf.py b/docs/mindquantum/docs/source_zh_cn/conf.py
index 42234ddac9a4816d4182c0e16aac945e261fa0d2..ce058b41157b74b6c7a3dd2cc12278c99d696ecb 100644
--- a/docs/mindquantum/docs/source_zh_cn/conf.py
+++ b/docs/mindquantum/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 import sphinx.ext.autosummary.generate as g
 from sphinx.ext import autodoc as sphinx_autodoc
@@ -92,21 +91,9 @@ intersphinx_mapping = {
     'numpy': ('https://docs.scipy.org/doc/numpy/', '../../../../resource/numpy_objects.inv'),
 }
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    f.seek(0)
-    f.writelines(contents)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 # Modify regex for sphinx.ext.autosummary.generate.find_autosummary_in_lines.
 gfile_abs_path = os.path.abspath(g.__file__)
diff --git a/docs/mindquantum/docs/source_zh_cn/images/quantum_phase_estimation.png b/docs/mindquantum/docs/source_zh_cn/images/quantum_phase_estimation.png
index 44d90c301ae3005c7930b0e47053be0dad3f6cd6..778c4d03379dd6b70182e7a84f6e948d60ea25ad 100644
Binary files a/docs/mindquantum/docs/source_zh_cn/images/quantum_phase_estimation.png and b/docs/mindquantum/docs/source_zh_cn/images/quantum_phase_estimation.png differ
diff --git a/docs/mindquantum/docs/source_zh_cn/index.rst b/docs/mindquantum/docs/source_zh_cn/index.rst
index 6c2717e28512813ddf0bcf683f920734304f1f6c..c58757c927a60bbc11d5862d04c03bade2813d0b 100644
--- a/docs/mindquantum/docs/source_zh_cn/index.rst
+++ b/docs/mindquantum/docs/source_zh_cn/index.rst
@@ -1,10 +1,11 @@
 MindQuantum文档
 =========================
 
-MindQuantum是基于昇思MindSpore开源深度学习框架和HiQ量子计算云平台开发的通用量子计算库，支持多种量子神经网络的训练和推理。得益于华为HiQ团队的量子计算模拟器和昇思MindSpore高性能自动微分能力，MindQuantum能够高效处理量子机器学习、量子化学模拟和量子优化等问题，为广大的科研人员、老师和学生提供快速设计和验证量子机器学习算法的高效平台。
+MindQuantum是基于昇思MindSpore开源深度学习框架和HiQ量子计算云平台开发的通用量子计算框架，支持多种量子神经网络的训练和推理。得益于华为HiQ团队的量子计算模拟器和昇思MindSpore高性能自动微分能力，MindQuantum能够高效处理量子机器学习、量子化学模拟和量子优化等问题，为广大的科研人员、老师和学生提供快速设计和验证量子机器学习算法的高效平台。
 
-.. image:: ./images/mindquantum_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/blob/master/docs/mindquantum/docs/source_zh_cn/images/mindquantum_cn.png" width="700px" alt="" >
 
 使用MindQuantum的典型场景
 ------------------------------
diff --git a/docs/mindquantum/docs/source_zh_cn/quantum_phase_estimation.ipynb b/docs/mindquantum/docs/source_zh_cn/quantum_phase_estimation.ipynb
index 7d606bdf7879d154c54dfc8a637ebd5e07708237..09a074ebec2a4b75e263dcccaf8e22e77e50af39 100644
--- a/docs/mindquantum/docs/source_zh_cn/quantum_phase_estimation.ipynb
+++ b/docs/mindquantum/docs/source_zh_cn/quantum_phase_estimation.ipynb
@@ -26,15 +26,15 @@
    "source": [
     "## 算法解析\n",
     "\n",
-    "相位估计算法的实现需要两个寄存器(register)，第一个寄存器包含$t$个初始在 $|0\\rangle$ 的量子比特，比特数和最后相位估计的结果的精度和算法的成功概率相关；第二个寄存器初始化在幺正算符 $U$ 的本征态 $|u\\rangle$ 上。相位估计算法主要分为三步：\n",
+    "量子相位估计算法的实现需要两个寄存器(register)，第一寄存器包含$t$个初始在 $|0\\rangle$ 的量子比特，比特数和最后相位估计的结果的精度和算法的成功概率相关；第二个寄存器初始化在幺正算符 $U$ 的本征态 $|u\\rangle$ 上。相位估计算法主要分为三步：\n",
     "\n",
-    "1. 对第一个寄存器的所有量子比特进行``Hadamard``门操作，对第二寄存器连续进行``控制U``门操作，其中U门的幂次依次为 $2^0, 2^1,...,2^{t-1}$，控制比特依次为 $q_{t-1}, q_{t-2},..., q_{1}, q_{0}$。这时第一寄存器中的态就会变为\n",
+    "1. 对第一寄存器的所有量子比特进行 `Hadamard` 门操作，对第二寄存器连续进行 `控制U` 门操作，其中 $U$ 门的幂次依次为 $2^0, 2^1,...,2^{t-1}$，控制比特依次为 $q_{t-1}, q_{t-2},..., q_{1}, q_{0}$。这时第一寄存器中的态就会变为\n",
     "\n",
     "$$\n",
     "|\\psi_1\\rangle=\\frac{1}{2^{t/2}}\\left(|0\\rangle+e^{i2\\pi 2^{t-1}\\varphi}|1\\rangle\\right)\\left(|0\\rangle+e^{i2\\pi2^{t-2}\\varphi}|1\\rangle\\right)...\\left(|0\\rangle+e^{i2\\pi 2^{0}\\varphi}|1\\rangle\\right) = \\frac{1}{2^{t/2}}\\sum_{k=0}^{2^t-1}e^{i2\\pi\\varphi k}|k\\rangle\n",
     "$$\n",
     "\n",
-    "其中k为直积态的十进制表示，比如 $k=0$ 表示第一寄存器中t个比特全部在基态 $|00...00\\rangle$,  $k=2$ 表示 $|00...10\\rangle$，以此类推。\n",
+    "其中$k$为直积态的十进制表示，比如 $k=0$ 表示第一寄存器中t个比特全部在基态 $|00...00\\rangle$,  $k=2$ 表示 $|00...10\\rangle$，以此类推。\n",
     "\n",
     "2. 对第一寄存器的进行量子傅里叶变换的逆变换(Inverse Quantum Fourier Transform)，在线路中表示成 $QFT^\\dagger$, 对 $|\\psi_1\\rangle$ 进行逆量子傅里叶变换可得 $|\\psi_2\\rangle$\n",
     "\n",
@@ -50,11 +50,11 @@
     "\n",
     "为本征基矢 $|x\\rangle$ ($x=0.1,...,2^t$) 对应的概率幅 。由上式可得，当 $2^t\\varphi$ 为整数，且满足 $x=2^t\\varphi$ 时，概率幅取最大值1，此时第一寄存器的末态可以精确反映 $\\varphi$；当 $2^t\\varphi$ 不是整数时，$x$ 为 $\\varphi$ 的估计，且$t$越大，估计精度越高。\n",
     "\n",
-    "3. 对第一寄存器的量子比特进行测量，得到第一寄存器的末态 $f=\\sum_{x}^{2^t-1}a_x|x\\rangle$, $x=0,1,...,2^t$；从中找到最大的概率幅 $a_{max}$，其对应的本征基矢 $|x\\rangle$ 中的 $x$ 在除以 $2^t$ 即为相位的估计值。\n",
+    "3. 对第一寄存器的量子比特进行测量，得到第一寄存器的末态 $f=\\sum_{x}^{2^t-1}a_x|x\\rangle$, $x=0,1,...,2^t$，从中找到最大的振幅 $a_{max}$，其对应的本征基矢 $|x\\rangle$ 中的 $x$ 再除以 $2^t$ 即为相位的估计值。\n",
     "\n",
     "## QPE代码实现\n",
     "\n",
-    "下面用一个实例来演示如何在MindQuantum实现相位估计算法，选择 ``T``门作为进行估计的幺正算符，由定义\n",
+    "下面用一个实例来演示如何在MindQuantum实现量子相位估计算法，选择 `T` 门作为进行估计的幺正算符，由定义\n",
     "\n",
     "$$\n",
     "T|1\\rangle=e^{i\\pi/4}|1\\rangle\n",
@@ -62,6 +62,8 @@
     "\n",
     "可知需要估计的相位角为 $\\varphi=\\frac{1}{8}$。\n",
     "\n",
+    "现在假设我们不知道 `T` 门的相位信息，只知道幺正算符 $U$ 是 `T` 门且本征态为 $|1\\rangle$ ，接下来我们需要用量子相位估计算法求出其对应的本征值，即需要估计本征值指数上的相位角。\n",
+    "\n",
     "首先导入相关依赖。"
    ]
   },
@@ -71,9 +73,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindquantum import Circuit\n",
-    "from mindquantum import Simulator\n",
-    "from mindquantum import UN, PhaseShift, qft, H, X, BARRIER\n",
+    "from mindquantum.core import Circuit, UN, T, H, X, Power, BARRIER\n",
+    "from mindquantum.simulator import Simulator\n",
+    "from mindquantum.algorithm import qft\n",
     "import numpy as np"
    ]
   },
@@ -81,9 +83,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Mindquantum.UN`` 可以指定量子门，目标比特和控制比特，从而在线路中搭建门操作。因为我们已知 $\\varphi=1/8$，当 $t=3$ 时可令 $2^t\\varphi$ 为整数，所以第一寄存器只需要3个比特即可准确估计；又已知 ``T`` 门的本征态为 $|1\\rangle$，所以第二寄存器选择一个比特，即：我们需要搭建4比特线路，前 $q_0, q_1, q_2$ 比特用于估计，属于第一寄存器；$q_3$ 属于第二寄存器用于传入 $T$ 算符的本征态。\n",
+    "`UN` 可以指定量子门，目标比特和控制比特，从而在线路中搭建门操作； `Power` 可以得到指定量子门的指数形式。因为我们已知 `T` 门的本征态为 $|1\\rangle$，所以第二寄存器只需1个比特，而在第一寄存器中的比特数越多，得到的结果就越准确，在这里我们使用4个比特。\n",
+    "\n",
+    "因此我们需要搭建5比特线路， $q_0, q_1, q_2, q_3$ 比特用于估计，属于第一寄存器， $q_4$ 属于第二寄存器用于传入 $T$ 算符的本征态。\n",
     "\n",
-    "利用 ``UN`` 对 $q_0, q_1, q_2$ 进行 ``Hadamard`` 门操作， 用 ``X`` 门对 $q_3$ 进行翻转，得到 ``T`` 门的本征态 $|1\\rangle$。"
+    "利用 `UN` 对 $q_0, q_1, q_2, q_3$ 进行 `Hadamard` 门操作， 用 `X` 门对 $q_4$ 进行翻转，得到 `T` 门的本征态 $|1\\rangle$。"
    ]
   },
   {
@@ -92,56 +96,37 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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-      "text/plain": []
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+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/html": [
-       "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H──</span>\n",
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-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──X──</span>\n",
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-       "         \n",
-       "q2: ──H──\n",
-       "         \n",
-       "q3: ──X──"
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+      "text/html": "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H──</span>\n\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──H──</span>\n\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H──</span>\n\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──H──</span>\n\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q4: ──X──</span>\n</pre>\n"
      },
-     "execution_count": 2,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 2
     }
    ],
    "source": [
     "# pylint: disable=W0104\n",
-    "n = 3\n",
-    "c = Circuit()\n",
-    "c += UN(H, n)\n",
-    "c += X.on(n)\n",
-    "c"
+    "n = 4\n",
+    "circ = Circuit()\n",
+    "circ += UN(H, n) # 对前4个比特作用力H门\n",
+    "circ += X.on(n)  # 对q4作用X门\n",
+    "circ"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "以 $q_3$ 为目标比特，添加 ``控制PhaseShift``门。"
+    "以 $q_4$ 为目标比特，添加控制$T^{2^i}$门。"
    ]
   },
   {
@@ -150,54 +135,35 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "text/html": [
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-     "output_type": "display_data"
+     "metadata": {}
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+     "output_type": "execute_result",
      "data": {
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-       "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H────────────────────────────────●──────</span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                       │      </span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──H───────────────────●────────────┼──────</span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                          │            │      </span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H───────●───────────┼────────────┼──────</span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">              │           │            │      </span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──X────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(phi)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">*phi)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">*phi)──</span>\n",
-       "</pre>\n"
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-       "                                       │      \n",
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-       "                          │            │      \n",
-       "q2: ──H───────●───────────┼────────────┼──────\n",
-       "              │           │            │      \n",
-       "q3: ──X────PS(phi)────PS(2*phi)────PS(4*phi)──"
-      ]
+      "text/plain": "q0: ──H──────────────────────────●───\n                                 │\nq1: ──H───────────────────●──────┼───\n                          │      │\nq2: ──H────────────●──────┼──────┼───\n                   │      │      │\nq3: ──H─────●──────┼──────┼──────┼───\n            │      │      │      │\nq4: ──X────T^1────T^2────T^4────T^8──",
+      "text/html": "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H──────────────────────────●───</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                 │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──H───────────────────●──────┼───</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                          │      │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H────────────●──────┼──────┼───</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                   │      │      │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──H─────●──────┼──────┼──────┼───</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">            │      │      │      │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q4: ──X────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">──</span>\n</pre>\n"
      },
-     "execution_count": 3,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
    "source": [
     "# pylint: disable=W0104\n",
     "for i in range(n):\n",
-    "    c += PhaseShift({'phi': 2**i}).on(n, n-i-1)\n",
-    "c"
+    "    circ += Power(T, 2**i).on(n, n - i - 1) # 添加T^2^i门，其中q4为目标比特，n-i-1为控制比特\n",
+    "circ"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "对第一寄存器比特进行逆傅里叶变换。"
+    "对第一寄存器中的比特进行逆量子傅里叶变换。"
    ]
   },
   {
@@ -206,112 +172,69 @@
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    "outputs": [
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+     "output_type": "display_data",
      "data": {
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-     "metadata": {},
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+     "output_type": "execute_result",
      "data": {
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-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H───────●───────────┼────────────┼────────@────H───────●────────────────●───────────────────────</span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">              │           │            │                                                              </span>\n",
-       "<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──X────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(phi)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">*phi)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">*phi)──────────────────────────────────────────────────────────</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "q0: ──H────────────────────────────────●────────@──────────────────────────PS(-π/4)────PS(-π/2)────H──\n",
-       "                                       │        │                             │           │           \n",
-       "q1: ──H───────────────────●────────────┼────────┼─────────PS(-π/2)────H───────┼───────────●───────────\n",
-       "                          │            │        │            │                │                       \n",
-       "q2: ──H───────●───────────┼────────────┼────────@────H───────●────────────────●───────────────────────\n",
-       "              │           │            │                                                              \n",
-       "q3: ──X────PS(phi)────PS(2*phi)────PS(4*phi)──────────────────────────────────────────────────────────"
-      ]
+      "text/plain": "q0: ──H──────────────────────────●──────────@───────────────────────────────────────────────────────PS(-π/8)────PS(-π/4)────PS(-π/2)────H──\n                                 │          │                                                          │           │           │\nq1: ──H───────────────────●──────┼─────@────┼──────────────────────────PS(-π/4)────PS(-π/2)────H───────┼───────────┼───────────●───────────\n                          │      │     │    │                             │           │                │           │\nq2: ──H────────────●──────┼──────┼─────@────┼─────────PS(-π/2)────H───────┼───────────●────────────────┼───────────●───────────────────────\n                   │      │      │          │            │                │                            │\nq3: ──H─────●──────┼──────┼──────┼──────────@────H───────●────────────────●────────────────────────────●───────────────────────────────────\n            │      │      │      │\nq4: ──X────T^1────T^2────T^4────T^8────────────────────────────────────────────────────────────────────────────────────────────────────────",
+      "text/html": "<pre style=\"white-space: pre;\"><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q0: ──H──────────────────────────●──────────@───────────────────────────────────────────────────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(-π/</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(-π/</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(-π/</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">)────H──</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                                 │          │                                                          │           │           │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q1: ──H───────────────────●──────┼─────@────┼──────────────────────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(-π/</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">)────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(-π/</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">)────H───────┼───────────┼───────────●───────────</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                          │      │     │    │                             │           │                │           │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q2: ──H────────────●──────┼──────┼─────@────┼─────────</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">PS</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">(-π/</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">)────H───────┼───────────●────────────────┼───────────●───────────────────────</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">                   │      │      │          │            │                │                            │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q3: ──H─────●──────┼──────┼──────┼──────────@────H───────●────────────────●────────────────────────────●───────────────────────────────────</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">            │      │      │      │</span>\n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">q4: ──X────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────T^</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n</pre>\n"
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 4
     }
    ],
    "source": [
     "# pylint: disable=W0104\n",
-    "c += BARRIER\n",
-    "c += qft(range(n)).hermitian()\n",
-    "c"
+    "circ += BARRIER\n",
+    "circ += qft(range(n)).hermitian() # 对前4个比特作用量子傅立叶变换的逆变换\n",
+    "circ"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "选择后端、传入总比特数创建模拟器，将 $\\varphi$ 值传入并进行演化，得到末态。"
+    "选择后端、传入总比特数创建模拟器，对量子线路进行演化，得到末态。"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1¦1100⟩\n"
-     ]
-    },
-    {
+     "output_type": "display_data",
      "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
-      ],
-      "text/plain": []
+      "text/plain": "",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
+     "output_type": "execute_result",
      "data": {
-      "text/html": [
-       "<pre style=\"white-space: pre;\"><span style=\"color: #808000; text-decoration-color: #808000\">shots: </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>\n",
-       "<span style=\"color: #808000; text-decoration-color: #808000\">Keys: q3 q2 q1 q0│</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.00</span><span style=\"color: #808000; text-decoration-color: #808000\">     </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n",
-       "<span style=\"color: #808000; text-decoration-color: #808000\">─────────────────┼───────────┴───────────┴───────────┴───────────┴───────────┴</span>\n",
-       "<span style=\"color: #808000; text-decoration-color: #808000\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1100</span><span style=\"color: #808000; text-decoration-color: #808000\">│▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓</span>\n",
-       "<span style=\"color: #808000; text-decoration-color: #808000\">                 │                                                            </span>\n",
-       "<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'1100'</span><span style=\"color: #808000; text-decoration-color: #808000\">: </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">}</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "shots: 100\n",
-       "Keys: q3 q2 q1 q0│0.00     0.2         0.4         0.6         0.8         1.0\n",
-       "─────────────────┼───────────┴───────────┴───────────┴───────────┴───────────┴\n",
-       "             1100│▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓\n",
-       "                 │                                                            \n",
-       "{'1100': 100}"
-      ]
+      "text/plain": "shots: 100\nKeys: q3 q2 q1 q0│0.00     0.2         0.4         0.6         0.8         1.0\n─────────────────┼───────────┴───────────┴───────────┴───────────┴───────────┴\n             0100│▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓\n                 │\n{'0100': 100}",
+      "text/html": "<pre style=\"white-space: pre;\"><span style=\"color: #808000; text-decoration-color: #808000\">shots: </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>\n<span style=\"color: #808000; text-decoration-color: #808000\">Keys: q3 q2 q1 q0│</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.00</span><span style=\"color: #808000; text-decoration-color: #808000\">     </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.4</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8</span><span style=\"color: #808000; text-decoration-color: #808000\">         </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>\n<span style=\"color: #808000; text-decoration-color: #808000\">─────────────────┼───────────┴───────────┴───────────┴───────────┴───────────┴</span>\n<span style=\"color: #808000; text-decoration-color: #808000\">             </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0100</span><span style=\"color: #808000; text-decoration-color: #808000\">│▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓</span>\n<span style=\"color: #808000; text-decoration-color: #808000\">                 │</span>\n<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">&#x27;0100&#x27;</span><span style=\"color: #808000; text-decoration-color: #808000\">: </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">}</span>\n</pre>\n"
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 5
     }
    ],
    "source": [
     "# pylint: disable=W0104\n",
     "from mindquantum import Measure\n",
-    "sim = Simulator('projectq', c.n_qubits)\n",
-    "phi = 0.125\n",
-    "sim.apply_circuit(c, {'phi': 2*np.pi*phi})\n",
-    "qs = sim.get_qs()\n",
-    "print(sim.get_qs(ket=True))\n",
-    "res = sim.sampling(UN(Measure(), c.n_qubits), shots=100)\n",
+    "sim = Simulator('projectq', circ.n_qubits)                      # 创建模拟器\n",
+    "sim.apply_circuit(circ)                                         # 用模拟器演化线路\n",
+    "qs = sim.get_qs()                                               # 获得演化得到的量子态\n",
+    "res = sim.sampling(UN(Measure(), circ.n_qubits - 1), shots=100) # 在寄存器1中加入测量门并对线路进行100次采样，获得统计结果\n",
     "res"
    ]
   },
@@ -319,49 +242,61 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "找出概率幅最大值的位置。"
+    "需要注意的是，测量结果作为二进制串的读取顺序应为$|q_0q_1q_2q_3\\rangle$，因此我们得到寄存器1的测量结果为`0010`，概率幅为1，该末态可以精准地反映相位$\\varphi$。但`0010`是二进制结果，因此我们将它转回十进制后再除以$2^n$，就得到了我们最终的估计值：$\\varphi=\\frac{2}{2^4}=\\frac{1}{8}$。\n",
+    "\n",
+    "我们也可以通过线路演化得到的量子态 `qs` 找出第一寄存器中振幅最大值 $a_{max}$ 的位置，进而得到其对应的本征基矢 $|x\\rangle$ ，其中的 $x$ 再除以 $2^t$ 即为相位的估计值。"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "12\n"
-     ]
+     "name": "stdout",
+     "text": "10100\n"
     }
    ],
    "source": [
     "index = np.argmax(np.abs(qs))\n",
-    "print(index)"
-   ]
+    "print(bin(index)[2:])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "tags": []
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "注意此时的 ``index`` 对应的 $x$ 并不是真正的估计值，被 $2^t$ 除之后也不是，因为测量结果中包括第二寄存器中的辅助比特，需要将``index``转成二进制后将辅助位剔除。"
-   ]
+    "需要注意的是，`qs` 对应的是整个量子线路的末态，因此得到的 ``index`` 也包含第二寄存器中的比特，不能直接得到第一寄存器末态中 $a_{max}$ 对应的 $|x\\rangle$ ，需要将 ``index`` 转成二进制后将 $q4$ 对应的比特位剔除，然后得到的才是第一寄存器的 $|x\\rangle$ 。"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "100\n"
-     ]
+     "name": "stdout",
+     "text": "0010\n"
     }
    ],
    "source": [
-    "bit_string = bin(index)[2:].zfill(c.n_qubits)[1:]\n",
+    "bit_string = bin(index)[2:].zfill(circ.n_qubits)[1:]        # 将index转换成01串并剔除q4\n",
+    "bit_string = bit_string[::-1]                               # 将比特串顺序调整为q0q1q2q3\n",
     "print(bit_string)"
    ]
   },
@@ -369,7 +304,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "在将二进制转回十进制，得到我们最终的估计值。"
+    "再将二进制转回十进制，得到我们最终的估计值。"
    ]
   },
   {
@@ -378,19 +313,17 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "0.125"
-      ]
+      "text/plain": "0.125"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
     "# pylint: disable=W0104\n",
-    "theta_exp = int(bit_string[::-1], 2) / 2**n\n",
+    "theta_exp = int(bit_string, 2) / 2**n\n",
     "theta_exp"
    ]
   },
@@ -430,9 +363,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.7.5-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/mindscience/api/requirements.txt b/docs/mindscience/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/mindscience/api/requirements.txt
+++ b/docs/mindscience/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/mindscience/docs/requirements.txt b/docs/mindscience/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/mindscience/docs/requirements.txt
+++ b/docs/mindscience/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/mindscience/docs/source_en/conf.py b/docs/mindscience/docs/source_en/conf.py
index 55fccca51583a10a8efe916257ae9f28889a7b81..fcd8da2832e6a64de60e299c5c68c94fa8b88c9c 100644
--- a/docs/mindscience/docs/source_en/conf.py
+++ b/docs/mindscience/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 import sphinx
 sys.path.append(os.path.abspath('../_ext'))
 import sphinx.ext.autosummary.generate as g
@@ -147,20 +146,9 @@ import mindspore
 
 import mindelec
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/mindscience/docs/source_en/index.rst b/docs/mindscience/docs/source_en/index.rst
index ff9d95803f07a0c80eab44b34026ba0cde5d2069..d84d76980025d2f207a01986353d178b9ef6367c 100644
--- a/docs/mindscience/docs/source_en/index.rst
+++ b/docs/mindscience/docs/source_en/index.rst
@@ -3,8 +3,9 @@ MindScience Documents
 
 MindScience is scientific computing kits for various industries based on the converged MindSpore framework. It contains the industry-leading datasets, basic network structures, high-precision pre-trained models, and pre- and post-processing tools, accelerating the development of scientific computing applications. Currently, the MindElec kit for the electronic information industry and the MindSPONGE kit for the life science industry have been launched, improving the electromagnetic simulation performance by 10 times and the simulation efficiency of biopharmaceutical compounds by 50%.
 
-.. image:: ./mindelec/images/mindscience_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindscience/docs/source_en/mindelec/images/mindscience_en.png" width="700px" alt="" >
 
 Typical MindScience Application Scenarios
 ------------------------------------------
diff --git a/docs/mindscience/docs/source_zh_cn/conf.py b/docs/mindscience/docs/source_zh_cn/conf.py
index 34a1649d1988c5fb973e784125f1047e1ada5a47..c45cf08602ea8f2c4b2c89f52286c54a4b166516 100644
--- a/docs/mindscience/docs/source_zh_cn/conf.py
+++ b/docs/mindscience/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 import sphinx
 sys.path.append(os.path.abspath('../_ext'))
 import sphinx.ext.autosummary.generate as g
@@ -81,20 +80,9 @@ html_search_language = 'zh'
 
 html_search_options = {'dict': '../../../resource/jieba.txt'}
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
diff --git a/docs/mindscience/docs/source_zh_cn/index.rst b/docs/mindscience/docs/source_zh_cn/index.rst
index 2a9fe6f600cdd8de5ca4b354f9f240e6e396477f..1bcc3cfc059f892154ff2ab665ef9bca2cd4daf9 100644
--- a/docs/mindscience/docs/source_zh_cn/index.rst
+++ b/docs/mindscience/docs/source_zh_cn/index.rst
@@ -3,8 +3,9 @@ MindScience 文档
 
 MindScience是基于昇思MindSpore融合架构打造的科学计算行业套件，包含了业界领先的数据集、基础模型、预置高精度模型和前后处理工具，加速了科学行业应用开发。目前已推出面向电子信息行业的MindElec套件和面向生命科学行业的MindSPONGE套件，分别实现了电磁仿真性能提升10倍和生物制药化合物模拟效率提升50%。
 
-.. image:: ./mindelec/images/mindscience_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/mindscience/docs/source_zh_cn/mindelec/images/mindscience_cn.png" width="700px" alt="" >
 
 使用MindScience的典型场景
 ------------------------------
diff --git a/docs/mindspore/api/Makefile b/docs/mindspore/api/Makefile
index 954652e2b70c041797a5444f6b9e5f07aeccda64..cd8d6bfa74c2ebc5218220cc76910c55aa3abeec 100644
--- a/docs/mindspore/api/Makefile
+++ b/docs/mindspore/api/Makefile
@@ -24,4 +24,4 @@ EXTRADIR      = $(SOURCEDIR)/api_python
 .PHONY: clean
 
 clean:
-	-rm -rf $(BUILDDIR)/* $(EXTRADIR)/ops $(EXTRADIR)/nn $(EXTRADIR)/dataset $(EXTRADIR)/dataset_vision $(EXTRADIR)/dataset_transforms $(EXTRADIR)/dataset_text $(EXTRADIR)/text $(EXTRADIR)/numpy $(EXTRADIR)/nn_probability $(EXTRADIR)/mindspore $(EXTRADIR)/scipy
+	-rm -rf $(BUILDDIR)/* $(EXTRADIR)/ops $(EXTRADIR)/nn $(EXTRADIR)/dataset $(EXTRADIR)/dataset_vision $(EXTRADIR)/dataset_audio $(EXTRADIR)/dataset_transforms $(EXTRADIR)/dataset_text $(EXTRADIR)/text $(EXTRADIR)/numpy $(EXTRADIR)/nn_probability $(EXTRADIR)/mindspore $(EXTRADIR)/scipy
diff --git a/docs/mindspore/api/requirements.txt b/docs/mindspore/api/requirements.txt
index f2424ce6f08f8ce69b2f1bb181bc09da2641d35d..1e7189447f37b0e2e1c98a2ac5e82da36756a493 100644
--- a/docs/mindspore/api/requirements.txt
+++ b/docs/mindspore/api/requirements.txt
@@ -1,4 +1,5 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
 opencv-python
\ No newline at end of file
diff --git a/docs/mindspore/api/source_en/api_python/mindspore.scipy.rst b/docs/mindspore/api/source_en/api_python/mindspore.scipy.rst
index 20ccb2229974d71bd5f532094de4d42c89ceb5d9..c48b343c4c2efdc04a9dab53b289cbbebe729f43 100644
--- a/docs/mindspore/api/source_en/api_python/mindspore.scipy.rst
+++ b/docs/mindspore/api/source_en/api_python/mindspore.scipy.rst
@@ -14,10 +14,14 @@ mindspore.scipy.linalg
     :template: classtemplate_inherited.rst
 
     mindspore.scipy.linalg.block_diag
+    mindspore.scipy.linalg.cho_factor
+    mindspore.scipy.linalg.cholesky
+    mindspore.scipy.linalg.cho_solve
     mindspore.scipy.linalg.eigh
     mindspore.scipy.linalg.inv
     mindspore.scipy.linalg.lu
     mindspore.scipy.linalg.lu_factor
+    mindspore.scipy.linalg.solve_triangular
 
 mindspore.scipy.optimize
 ------------------------
diff --git a/docs/mindspore/api/source_en/conf.py b/docs/mindspore/api/source_en/conf.py
index 7d274628392abe4406838b903ef309a3c0de1f61..4b1778e12f97bf6d871fc067ff90e61bd466e512 100644
--- a/docs/mindspore/api/source_en/conf.py
+++ b/docs/mindspore/api/source_en/conf.py
@@ -150,29 +150,38 @@ import mindspore
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
 
-# Copy images from mindspore repository to sphinx workdir before running.
-import glob
+# Copy images from mindspore repo.
+import imghdr
 import shutil
 from sphinx.util import logging
-logger = logging.getLogger(__name__)
-
-image_specified = {"docs/api_img/*.png": "./api_python/ops/api_img",
-                   "docs/api_img/dataset/*.png": "./api_python/dataset/api_img"}
 
-for img in image_specified.keys():
-    des_dir = os.path.normpath(image_specified[img])
-    try:
-        if "*" in img:
-            imgs = glob.glob(os.path.join(os.getenv("MS_PATH"), os.path.normpath(img)))
-            if not imgs:
-                continue
-            if not os.path.exists(des_dir):
-                os.makedirs(des_dir)
-            for i in imgs:
-                shutil.copy(i, des_dir)
-        else:
-            img_fullpath = os.path.join(os.getenv("MS_PATH"), des_dir)
-            if os.path.exists(img_fullpath):
-                shutil.copy(img_fullpath, des_dir)
-    except:
-        logger.warning(f"{img} deal failed!")
+logger = logging.getLogger(__name__)
+src_dir = os.path.join(os.getenv("MS_PATH"), 'docs/api/api_python')
+des_dir = "./api_python"
+image_specified = {"train/": ""}
+
+if not os.path.exists(src_dir):
+    logger.warning(f"不存在目录：{src_dir}！")
+
+def copy_image(sourcedir, des_dir):
+    """
+    Copy all images from sourcedir to workdir.
+    """
+    for cur, _, files in os.walk(sourcedir, topdown=True):
+        for i in files:
+            if imghdr.what(os.path.join(cur, i)):
+                try:
+                    rel_path = os.path.relpath(cur, sourcedir)
+                    targetdir = os.path.join(des_dir, rel_path)
+                    for j in image_specified.keys():
+                        if rel_path.startswith(j):
+                            value = image_specified[j]
+                            targetdir = os.path.join(des_dir,  re.sub(rf'^{j}', rf'{value}', rel_path))
+                            break
+                    if not os.path.exists(targetdir):
+                        os.makedirs(targetdir, exist_ok=True)
+                    shutil.copy(os.path.join(cur, i), targetdir)
+                except:
+                    logger.warning(f'picture {os.path.join(os.path.relpath(cur, sourcedir), i)} copy failed.')
+
+copy_image(src_dir, des_dir)
diff --git a/docs/mindspore/api/source_zh_cn/api_python/mindspore.scipy.rst b/docs/mindspore/api/source_zh_cn/api_python/mindspore.scipy.rst
index 20ccb2229974d71bd5f532094de4d42c89ceb5d9..c48b343c4c2efdc04a9dab53b289cbbebe729f43 100644
--- a/docs/mindspore/api/source_zh_cn/api_python/mindspore.scipy.rst
+++ b/docs/mindspore/api/source_zh_cn/api_python/mindspore.scipy.rst
@@ -14,10 +14,14 @@ mindspore.scipy.linalg
     :template: classtemplate_inherited.rst
 
     mindspore.scipy.linalg.block_diag
+    mindspore.scipy.linalg.cho_factor
+    mindspore.scipy.linalg.cholesky
+    mindspore.scipy.linalg.cho_solve
     mindspore.scipy.linalg.eigh
     mindspore.scipy.linalg.inv
     mindspore.scipy.linalg.lu
     mindspore.scipy.linalg.lu_factor
+    mindspore.scipy.linalg.solve_triangular
 
 mindspore.scipy.optimize
 ------------------------
diff --git a/docs/mindspore/api/source_zh_cn/conf.py b/docs/mindspore/api/source_zh_cn/conf.py
index 300c377cda8c46cfbd2d4095f179f687c03b89fc..b4221fa9e64a2a4e3cbac312df0e7186686527eb 100644
--- a/docs/mindspore/api/source_zh_cn/conf.py
+++ b/docs/mindspore/api/source_zh_cn/conf.py
@@ -150,29 +150,38 @@ import mindspore
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
 
-# Copy images from mindspore repository to sphinx workdir before running.
-import glob
+# Copy images from mindspore repo.
+import imghdr
 import shutil
 from sphinx.util import logging
-logger = logging.getLogger(__name__)
-
-image_specified = {"docs/api_img/*.png": "./api_python/ops/api_img",
-                   "docs/api_img/dataset/*.png": "./api_python/dataset/api_img"}
 
-for img in image_specified.keys():
-    des_dir = os.path.normpath(image_specified[img])
-    try:
-        if "*" in img:
-            imgs = glob.glob(os.path.join(os.getenv("MS_PATH"), os.path.normpath(img)))
-            if not imgs:
-                continue
-            if not os.path.exists(des_dir):
-                os.makedirs(des_dir)
-            for i in imgs:
-                shutil.copy(i, des_dir)
-        else:
-            img_fullpath = os.path.join(os.getenv("MS_PATH"), des_dir)
-            if os.path.exists(img_fullpath):
-                shutil.copy(img_fullpath, des_dir)
-    except:
-        logger.warning(f"{img} deal failed!")
+logger = logging.getLogger(__name__)
+src_dir = os.path.join(os.getenv("MS_PATH"), 'docs/api/api_python')
+des_dir = "./api_python"
+image_specified = {"train/": ""}
+
+if not os.path.exists(src_dir):
+    logger.warning(f"不存在目录：{src_dir}！")
+
+def copy_image(sourcedir, des_dir):
+    """
+    Copy all images from sourcedir to workdir.
+    """
+    for cur, _, files in os.walk(sourcedir, topdown=True):
+        for i in files:
+            if imghdr.what(os.path.join(cur, i)):
+                try:
+                    rel_path = os.path.relpath(cur, sourcedir)
+                    targetdir = os.path.join(des_dir, rel_path)
+                    for j in image_specified.keys():
+                        if rel_path.startswith(j):
+                            value = image_specified[j]
+                            targetdir = os.path.join(des_dir,  re.sub(rf'^{j}', rf'{value}', rel_path))
+                            break
+                    if not os.path.exists(targetdir):
+                        os.makedirs(targetdir, exist_ok=True)
+                    shutil.copy(os.path.join(cur, i), targetdir)
+                except:
+                    logger.warning(f'picture {os.path.join(os.path.relpath(cur, sourcedir), i)} copy failed.')
+
+copy_image(src_dir, des_dir)
diff --git a/docs/mindspore/faq/requirements.txt b/docs/mindspore/faq/requirements.txt
index 3fcb7644a26ae0615bc4e71a7d333f6200e1a5bf..878affd19d9928e8955e4d5b44b45b823da7fb0c 100644
--- a/docs/mindspore/faq/requirements.txt
+++ b/docs/mindspore/faq/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
diff --git a/docs/mindspore/faq/source_en/conf.py b/docs/mindspore/faq/source_en/conf.py
index 5be540341a1805fe8241134120683dc3bae5c6f7..4669f40aa0c1071982318c307891cbed76cfb52b 100644
--- a/docs/mindspore/faq/source_en/conf.py
+++ b/docs/mindspore/faq/source_en/conf.py
@@ -52,6 +52,8 @@ pygments_style = 'sphinx'
 #
 html_theme = 'sphinx_rtd_theme'
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindspore/faq/source_en/installation.md b/docs/mindspore/faq/source_en/installation.md
index 43f58139d1b31040b8b5d3d8756565801adac1ff..a99c9942e9de0a3e983f358abd21a9fd6e95dedf 100644
--- a/docs/mindspore/faq/source_en/installation.md
+++ b/docs/mindspore/faq/source_en/installation.md
@@ -286,3 +286,9 @@ $make
 $make check
 $sudo make install
 ```
+
+<br/>
+
+<font size=3>**What should I do if an warning message `UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.` is displayed when running Mindspore?**</font>
+
+A: We observed such warnings on ARM environment, with python 3.9 and numpy >=1.22.0 installed. These wharnings come from numpy instead of MindSpore, if you wish to suppress these warnings, please consider manually switching to a lower version of numpy (<=1.21.2).
diff --git a/docs/mindspore/faq/source_zh_cn/conf.py b/docs/mindspore/faq/source_zh_cn/conf.py
index 34872471f7f6867fdb6e3e35ba8765345a1b4546..face6046bf5697ccade00ccff3fc1eb770f20698 100644
--- a/docs/mindspore/faq/source_zh_cn/conf.py
+++ b/docs/mindspore/faq/source_zh_cn/conf.py
@@ -56,6 +56,8 @@ html_search_language = 'zh'
 
 html_search_options = {'dict': '../../../resource/jieba.txt'}
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindspore/faq/source_zh_cn/feature_advice.md b/docs/mindspore/faq/source_zh_cn/feature_advice.md
index e4061f58b882c7c18034cbcbde0ea53dd475da49..93df8eb21a61ea2c571a2caeecb0a33a69b93405 100644
--- a/docs/mindspore/faq/source_zh_cn/feature_advice.md
+++ b/docs/mindspore/faq/source_zh_cn/feature_advice.md
@@ -144,7 +144,7 @@ A: TensorFlow的对象检测Pipeline接口属于TensorFlow Model模块。待Mind
 
 <font size=3>**Q: 使用PyNative模式能够进行迁移学习？**</font>
 
-A: PyNative模式是兼容迁移学习的，更多的教程信息，可以参考[预训练模型加载代码详解](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cv_mobilenetv2_fine_tune.html#id7)。
+A: PyNative模式是兼容迁移学习的，更多的教程信息，可以参考[预训练模型加载代码详解](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cv_mobilenetv2_fine_tune.html#预训练模型加载代码详解)。
 
 <br/>
 
diff --git a/docs/mindspore/faq/source_zh_cn/implement_problem.md b/docs/mindspore/faq/source_zh_cn/implement_problem.md
index 3f168f5fdf553728f7dcc9bb5cce3098013cf69f..758b2bbec9fe2c27751393b41a36b69a516c8fcf 100644
--- a/docs/mindspore/faq/source_zh_cn/implement_problem.md
+++ b/docs/mindspore/faq/source_zh_cn/implement_problem.md
@@ -519,7 +519,7 @@ ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Exp
 
 <br/>
 
-<font size=3>**运行文档示例代码的过程中，遇到`matplotlib.pyplot.show()`或`plt.show()`无法执行怎么处理？**</font>
+<font size=3>**Q：运行文档示例代码的过程中，遇到`matplotlib.pyplot.show()`或`plt.show()`无法执行怎么处理？**</font>
 
 A: 首先确认是否安装`matplotlib`，如果没有安装，可以在命令行中执行`pip install matplotlib`进行安装。
 
diff --git a/docs/mindspore/faq/source_zh_cn/inference.md b/docs/mindspore/faq/source_zh_cn/inference.md
index 0eba8eefdbe0dfbfcd1b2fefa50a652355daebce..33fe0734ca3624d089055d823744017b4b0b96e3 100644
--- a/docs/mindspore/faq/source_zh_cn/inference.md
+++ b/docs/mindspore/faq/source_zh_cn/inference.md
@@ -34,7 +34,7 @@ def export_net():
 
 <font size=3>**Q: 编译应用时报错`/usr/bin/ld: warning: libxxx.so, needed by libmindspore.so, not found`怎么办？**</font>
 
-A: 寻找缺少的动态库文件所在目录，添加该路径到环境变量`LD_LIBRARY_PATH`中，环境变量设置参考[Ascend 310 AI处理器上使用MindIR模型进行推理#编译推理代码](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_310_mindir.html#id6)。
+A: 寻找缺少的动态库文件所在目录，添加该路径到环境变量`LD_LIBRARY_PATH`中，环境变量设置参考[Ascend 310 AI处理器上使用MindIR模型进行推理#编译推理代码](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_310_mindir.html#编译推理代码)。
 
 <br/>
 
diff --git a/docs/mindspore/faq/source_zh_cn/installation.md b/docs/mindspore/faq/source_zh_cn/installation.md
index fc445b3d4e4a9d7a1290ef03edc3f049c04e8ca8..ca67551f542ef8e56e8f3242db9af4eb49a0abca 100644
--- a/docs/mindspore/faq/source_zh_cn/installation.md
+++ b/docs/mindspore/faq/source_zh_cn/installation.md
@@ -222,7 +222,7 @@ A: MindSpore GPU模式一般无需设置`DEVICE_ID`环境变量，MindSpore会
 
 <font size=3>**Q: 编译应用时报错`/usr/bin/ld: warning: libxxx.so, needed by libmindspore.so, not found`怎么办？**</font>
 
-A: 寻找缺少的动态库文件所在目录，添加该路径到环境变量`LD_LIBRARY_PATH`中，环境变量设置参考[Ascend 310 AI处理器上使用MindIR模型进行推理#编译推理代码](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_310_mindir.html#id6)。
+A: 寻找缺少的动态库文件所在目录，添加该路径到环境变量`LD_LIBRARY_PATH`中，环境变量设置参考[Ascend 310 AI处理器上使用MindIR模型进行推理#编译推理代码](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_310_mindir.html#编译推理代码)。
 
 <br/>
 
@@ -304,3 +304,9 @@ $make
 $make check
 $sudo make install
 ```
+
+<br/>
+
+<font size=3>**Q: 运行MindSpore时出现告警 `UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.` 应该怎么解决？**</font>
+
+A: 上述问题出现在安装了较新版本的numpy(>=1.22.0)版本的ARM python3.9环境上。告警来自numpy而非MindSpore。如果告警影响到了代码的正常调测，可以考虑手动安装较低版本的numpy(<=1.21.2)来规避。
diff --git a/docs/mindspore/migration_guide/requirements.txt b/docs/mindspore/migration_guide/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/mindspore/migration_guide/requirements.txt
+++ b/docs/mindspore/migration_guide/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md b/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md
index 4b223b2a0934ef87e5b3fcc618850803cd32f07b..eba739c4f88e473d1d6831da5ec650ea378c65be 100644
--- a/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md
+++ b/docs/mindspore/migration_guide/source_en/api_mapping/pytorch_api_mapping.md
@@ -138,7 +138,7 @@ More MindSpore developers are also welcome to participate in improving the mappi
 | [torch.tanh](https://pytorch.org/docs/1.5.0/torch.html#torch.tanh)                           | [mindspore.ops.Tanh](https://mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.Tanh.html#mindspore.ops.Tanh)                                              |                                                                                                                   |
 | [torch.tensor](https://pytorch.org/docs/1.5.0/torch.html#torch.tensor)                       | [mindspore.Tensor](https://mindspore.cn/docs/api/en/master/api_python/mindspore/mindspore.Tensor.html#mindspore.Tensor)                                              |                                                                                                                   |
 | [torch.Tensor](https://pytorch.org/docs/1.5.0/torch.html#torch.Tensor)                       | [mindspore.Tensor](https://mindspore.cn/docs/api/en/master/api_python/mindspore/mindspore.Tensor.html#mindspore.Tensor)                                              |                                                                                                                   |
-| [torch.tensordot](https://pytorch.org/docs/1.5.0/torch.html#torch.tensordot)                 | [mindspore.numpy.tensordot](https://mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.Reshape.html#mindspore.numpy.tensordot)                             |                                                                                                                   |
+| [torch.tensordot](https://pytorch.org/docs/1.5.0/torch.html#torch.tensordot)                 | [mindspore.numpy.tensordot](https://www.mindspore.cn/docs/api/en/master/api_python/numpy/mindspore.numpy.tensordot.html)                             |                                                                                                                   |
 | [torch.topk](https://pytorch.org/docs/1.5.0/torch.html#torch.topk)                           | [mindspore.ops.TopK](https://mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.TopK.html#mindspore.ops.TopK)                                              | [diff](https://www.mindspore.cn/docs/migration_guide/en/master/api_mapping/pytorch_diff/TopK.html)                |
 | [torch.trace](https://pytorch.org/docs/1.5.0/torch.html#torch.trace)                         | [mindspore.Tensor.trace](https://mindspore.cn/docs/api/en/master/api_python/mindspore/mindspore.Tensor.html#mindspore.Tensor.trace)                                  |                                                                                                                   |
 | [torch.transpose](https://pytorch.org/docs/1.5.0/torch.html#torch.transpose)                 | [mindspore.ops.Transpose](https://mindspore.cn/docs/api/en/master/api_python/ops/mindspore.ops.Transpose.html#mindspore.ops.Transpose)                               |                                                                                                                   |
diff --git a/docs/mindspore/migration_guide/source_en/conf.py b/docs/mindspore/migration_guide/source_en/conf.py
index d0451dfbce424c51499552fe13a6591fd9333621..f7122e1445044c4b270fc99bc08ce2c89e49f9d7 100644
--- a/docs/mindspore/migration_guide/source_en/conf.py
+++ b/docs/mindspore/migration_guide/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 
 # -- Project information -----------------------------------------------------
 
@@ -61,20 +60,9 @@ pygments_style = 'sphinx'
 #
 html_theme = 'sphinx_rtd_theme'
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/mindspore/migration_guide/source_zh_cn/api_mapping/pytorch_api_mapping.md b/docs/mindspore/migration_guide/source_zh_cn/api_mapping/pytorch_api_mapping.md
index 8880b199d3f9d868b9592a6bcbaa2443615788c0..f6f2c512e0430690a58c70afdf1ebcbafd603f52 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/api_mapping/pytorch_api_mapping.md
+++ b/docs/mindspore/migration_guide/source_zh_cn/api_mapping/pytorch_api_mapping.md
@@ -138,7 +138,7 @@
 | [torch.tanh](https://pytorch.org/docs/1.5.0/torch.html#torch.tanh)                           | [mindspore.ops.Tanh](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.Tanh.html#mindspore.ops.Tanh)                                              |                                                                                                                          |
 | [torch.tensor](https://pytorch.org/docs/1.5.0/torch.html#torch.tensor)                       | [mindspore.Tensor](https://mindspore.cn/docs/api/zh-CN/master/api_python/mindspore/mindspore.Tensor.html#mindspore.Tensor)                                              |                                                                                                                          |
 | [torch.Tensor](https://pytorch.org/docs/1.5.0/torch.html#torch.Tensor)                       | [mindspore.Tensor](https://mindspore.cn/docs/api/zh-CN/master/api_python/mindspore/mindspore.Tensor.html#mindspore.Tensor)                                              |                                                                                                                          |
-| [torch.tensordot](https://pytorch.org/docs/1.5.0/torch.html#torch.tensordot)                 | [mindspore.numpy.tensordot](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.Reshape.html#mindspore.numpy.tensordot)                             |                                                                                                                          |
+| [torch.tensordot](https://pytorch.org/docs/1.5.0/torch.html#torch.tensordot)                 | [mindspore.numpy.tensordot](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/numpy/mindspore.numpy.tensordot.html)                             |                                                                                                                          |
 | [torch.topk](https://pytorch.org/docs/1.5.0/torch.html#torch.topk)                           | [mindspore.ops.TopK](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.TopK.html#mindspore.ops.TopK)                                              | [差异对比](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/api_mapping/pytorch_diff/TopK.html)                |
 | [torch.trace](https://pytorch.org/docs/1.5.0/torch.html#torch.trace)                         | [mindspore.Tensor.trace](https://mindspore.cn/docs/api/zh-CN/master/api_python/mindspore/mindspore.Tensor.html#mindspore.Tensor.trace)                                  |                                                                                                                          |
 | [torch.transpose](https://pytorch.org/docs/1.5.0/torch.html#torch.transpose)                 | [mindspore.ops.Transpose](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.Transpose.html#mindspore.ops.Transpose)                               |                                                                                                                          |
diff --git a/docs/mindspore/migration_guide/source_zh_cn/api_mapping/tensorflow_api_mapping.md b/docs/mindspore/migration_guide/source_zh_cn/api_mapping/tensorflow_api_mapping.md
index 14091d1cd2ba1feba9590ab9f563cf9ab7e7a773..398b588fc7ce8e3c7598674a677d003a98b3ce08 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/api_mapping/tensorflow_api_mapping.md
+++ b/docs/mindspore/migration_guide/source_zh_cn/api_mapping/tensorflow_api_mapping.md
@@ -17,7 +17,7 @@
 | [tf.eye](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/eye)                     | [mindspore.ops.Eye](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.Eye.html)                                           |                                                                                                                      |
 | [tf.fill](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/fill)                   | [mindspore.ops.Fill](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.Fill.html)                                         |                                                                                                                      |
 | [tf.gather](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/gather)               | [mindspore.ops.Gather](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.Gather.html)                                     |                                                                                                                      |
-| [tf.gradients](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/gradients)         | [mindspore.ops.GradOperation](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.GradOperation.html)                       | [差异对比](http://www.mindspore.cn/docs/migration_guide/zh-CN/master/api_mapping/tensorflow_diff/GradOperation.html) |
+| [tf.gradients](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/gradients)         | [mindspore.ops.GradOperation](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.GradOperation.html)                       | [差异对比](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/api_mapping/tensorflow_diff/GradOperation.html) |
 | [tf.norm](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/norm)                   | [mindspore.nn.Norm](https://mindspore.cn/docs/api/zh-CN/master/api_python/nn/mindspore.nn.Norm.html)                                            |                                                                                                                      |
 | [tf.one_hot](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/one_hot)             | [mindspore.nn.OneHot](https://mindspore.cn/docs/api/zh-CN/master/api_python/nn/mindspore.nn.OneHot.html)                                        |                                                                                                                      |
 | [tf.ones_like](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/ones_like)         | [mindspore.ops.OnesLike](https://mindspore.cn/docs/api/zh-CN/master/api_python/ops/mindspore.ops.OnesLike.html)                                 |                                                                                                                      |
diff --git a/docs/mindspore/migration_guide/source_zh_cn/conf.py b/docs/mindspore/migration_guide/source_zh_cn/conf.py
index e937416a34f01c0c3fcdf46cb6139ef1a7f7bc8d..4645ca4e62c51da6e52e2a480187c0b3754666ef 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/conf.py
+++ b/docs/mindspore/migration_guide/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 
 # -- Project information -----------------------------------------------------
 
@@ -65,20 +64,9 @@ html_search_language = 'zh'
 
 html_search_options = {'dict': '../../../resource/jieba.txt'}
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 
diff --git a/docs/mindspore/migration_guide/source_zh_cn/faq.md b/docs/mindspore/migration_guide/source_zh_cn/faq.md
index d2df2bb5a6023db12ef28d475577eac8a7b475f7..d7f85fd323d576c9ec20a07ab362e496f1e4bb82 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/faq.md
+++ b/docs/mindspore/migration_guide/source_zh_cn/faq.md
@@ -10,9 +10,9 @@
 
 - 网络脚本分析
 
-    [算子映射及缺失算子处理策略](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/script_analysis.html#id3)
+    [算子映射及缺失算子处理策略](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/script_analysis.html#查询算子映射表)
 
-    [常见语法限制及处理策略](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/script_analysis.html#id6)
+    [常见语法限制及处理策略](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/script_analysis.html#常见限制原则)
 
 - 网络脚本开发
 
@@ -20,17 +20,17 @@
 
 - 网络调试
 
-    [流程调试常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/neural_network_debug.html#id6)
+    [流程调试常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/neural_network_debug.html#常见错误)
 
-    [loss值对比检查常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/neural_network_debug.html#id8)
+    [loss值对比检查常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/neural_network_debug.html#相关问题定位)
 
-    [loss值异常常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/neural_network_debug.html#id11)
+    [loss值异常常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/neural_network_debug.html#loss值异常定位)
 
 - 性能调试
 
-    [性能调试常见问题及优化方法](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/sample_code.html#id26)
+    [性能调试常见问题及优化方法](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/sample_code.html#性能调优)
 
-    [Profiler工具常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/performance_optimization.html#id6)
+    [Profiler工具常见问题处理](https://www.mindspore.cn/docs/migration_guide/zh-CN/master/performance_optimization.html#常见问题)
 
 - 执行推理
 
diff --git a/docs/mindspore/migration_guide/source_zh_cn/inference.md b/docs/mindspore/migration_guide/source_zh_cn/inference.md
index bde5c9645747479dc8e3ae220857eebee89e77a3..f574013bb5e272884793a421c5ccbe36e428f5ba 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/inference.md
+++ b/docs/mindspore/migration_guide/source_zh_cn/inference.md
@@ -8,11 +8,11 @@ MindSpore可以基于训练好的模型，在不同的硬件平台上执行推
 
 ### 总览
 
-MindSpore支持保存为CheckPoint格式的[训练参数文件](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#id2)和MindIR、AIR、ONNX格式的[网络模型文件](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#id2)。
+MindSpore支持保存为CheckPoint格式的[训练参数文件](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#模型文件)和MindIR、AIR、ONNX格式的[网络模型文件](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#模型文件)。
 
-参考[执行推理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#id3)，不仅可以直接通过`mindspore.model.predict`接口执行本机推理，还可以通过`mindspore.export`导出MindIR、AIR、ONNX格式的网络模型文件，以便于跨平台执行推理。
+参考[执行推理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#执行推理)，不仅可以直接通过`mindspore.model.predict`接口执行本机推理，还可以通过`mindspore.export`导出MindIR、AIR、ONNX格式的网络模型文件，以便于跨平台执行推理。
 
-使用[MindIR格式](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#id3)的模型文件消除了不同后端模型的差异，可以用于执行跨硬件平台推理，支持部署到云端Serving和端侧Lite平台。
+使用[MindIR格式](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference.html#mindir介绍)的模型文件消除了不同后端模型的差异，可以用于执行跨硬件平台推理，支持部署到云端Serving和端侧Lite平台。
 
 ### 不同硬件平台执行推理
 
diff --git a/docs/mindspore/migration_guide/source_zh_cn/neural_network_debug.md b/docs/mindspore/migration_guide/source_zh_cn/neural_network_debug.md
index 855e1cfe9b9f8de125847e4bc5df8ac603636bc6..9bfbbefb85867897b50f2cec8110430c8da4375c 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/neural_network_debug.md
+++ b/docs/mindspore/migration_guide/source_zh_cn/neural_network_debug.md
@@ -43,7 +43,7 @@
 
 - 在PyNative模式下可使用pdb进行调试，利用pdb打印相关堆栈和上下文信息帮助问题定位。
 - 使用Print算子打印更多上下文信息，具体示例可参考[Print算子功能介绍](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#print)。
-- 调整日志级别获取更多报错信息，MindSpore可通过环境变量方便地调整日志级别，具体可参考[日志相关的环境变量和配置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id6)。
+- 调整日志级别获取更多报错信息，MindSpore可通过环境变量方便地调整日志级别，具体可参考[日志相关的环境变量和配置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)。
 
 #### 常见错误
 
@@ -125,7 +125,7 @@
 
 - [Callback功能](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#callback)
 
-   MindSpore已提供ModelCheckpoint、LossMonitor、SummaryCollector等Callback类用于保存模型参数、监控loss值、保存训练过程信息等功能，用户也可自定义Callback函数用于实现在每个epoch和step的开始和结束运行相关功能，具体示例可参考[自定义Callback](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id3)。
+   MindSpore已提供ModelCheckpoint、LossMonitor、SummaryCollector等Callback类用于保存模型参数、监控loss值、保存训练过程信息等功能，用户也可自定义Callback函数用于实现在每个epoch和step的开始和结束运行相关功能，具体示例可参考[自定义Callback](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#自定义callback)。
 
 - [MindSpore metrics功能](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#mindspore-metrics)
 
diff --git a/docs/mindspore/migration_guide/source_zh_cn/sample_code.md b/docs/mindspore/migration_guide/source_zh_cn/sample_code.md
index 058d94804009897e0eab89460e150cebcdbf9db3..7052278e59876d4c9f9177ef09499e140d2f4cce 100644
--- a/docs/mindspore/migration_guide/source_zh_cn/sample_code.md
+++ b/docs/mindspore/migration_guide/source_zh_cn/sample_code.md
@@ -823,7 +823,7 @@ profiler.analyse()
 
 当数据处理速度较慢时，队列从最开始的满队列情况逐渐消耗为空队列，训练进程会开始等待空队列填入数据，一旦有新的数据填入，网络才会继续进行单Step训练。由于数据处理没有队列作为缓冲，数据处理的性能抖动直接体现在单Step的性能上，因此还会造成单Step性能抖动。
 
-关于MindData的性能问题，可以参考 MindInsight 组件的 [数据准备性能分析](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#id8)，其给出了MindData 性能的常见问题及解决方法。
+关于MindData的性能问题，可以参考 MindInsight 组件的 [数据准备性能分析](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html#数据准备性能分析)，其给出了MindData 性能的常见问题及解决方法。
 
 #### 多机同步性能问题
 
diff --git a/docs/mindspore/note/requirements.txt b/docs/mindspore/note/requirements.txt
index 3fcb7644a26ae0615bc4e71a7d333f6200e1a5bf..878affd19d9928e8955e4d5b44b45b823da7fb0c 100644
--- a/docs/mindspore/note/requirements.txt
+++ b/docs/mindspore/note/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
diff --git a/docs/mindspore/note/source_en/conf.py b/docs/mindspore/note/source_en/conf.py
index c4c6d846fa95a2a37a3f00665150b9bb3fe9ef57..37e45152eea2b3cac89b34d82c6928860f7d7109 100644
--- a/docs/mindspore/note/source_en/conf.py
+++ b/docs/mindspore/note/source_en/conf.py
@@ -55,6 +55,8 @@ pygments_style = 'sphinx'
 #
 html_theme = 'sphinx_rtd_theme'
 
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindspore/note/source_en/env_var_list.md b/docs/mindspore/note/source_en/env_var_list.md
index 9016f580db391e7befae19fc524aa0b8f282b5aa..b2c96ebc75088fef18b12e034d435bd6b6e8ca3a 100644
--- a/docs/mindspore/note/source_en/env_var_list.md
+++ b/docs/mindspore/note/source_en/env_var_list.md
@@ -18,9 +18,11 @@ MindSpore environment variables are as follows:
 |GLOG_v|MindSpore|For details about the function and usage, see [GLOG_v](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|0-DEBUG <br>1-INFO <br>2-WARNING <br>3-ERROR|None|Optional|2|
 |GLOG_logtostderr|MindSpore|For details about the function and usage, see [GLOG_logtostderr](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|1:logs are output to the screen <br> 0:logs are output to a file|This variable is used together with GLOG_log_dir|Optional|1|
 |GLOG_log_dir|MindSpore|For details about the function and usage, see [GLOG_log_dir](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|String|File path, which can be a relative path or an absolute path.|This variable is used together with GLOG_logtostderr|Optional|None|
-|GLOG_log_max|MindSpore|For details about the function and usage, see [GLOG_log_max](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|>0|None |Optional|50|
-|MS_SUBMODULE_LOG_v|MindSpore|For details about the function and usage, see [MS_SUBMODULE_LOG_v](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Dict{String:Integer...}|LogLevel: 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR<br>SubModual: COMMON, MD, DEBUG, DEVICE, COMMON, IR...|None | Optional|None|
 |GLOG_stderrthreshold|For details about the function and usage, see [GLOG_stderrthreshold](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|0-DEBUG <br>1-INFO <br>2-WARNING <br>3-ERROR|None|Optional|2
+|MS_SUBMODULE_LOG_v|MindSpore|For details about the function and usage, see [MS_SUBMODULE_LOG_v](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Dict{String:Integer...}|LogLevel: 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR<br>SubModual: COMMON, MD, DEBUG, DEVICE, COMMON, IR...|None | Optional|None|
+|GLOG_log_max|MindSpore|For details about the function and usage, see [GLOG_log_max](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|>0|None |Optional|50|
+|logger_maxBytes|MindSpore|For details about the function and usage, see [logger_maxBytes](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|None|None | Optional|52428800|
+|logger_backupCount|For details about the function and usage, see [logger_backupCount](https://www.mindspore.cn/docs/programming_guide/en/master/custom_debugging_info.html#log-related-environment-variables-and-configurations)|Integer|None|None|Optional|30
 |OPTION_PROTO_LIB_PATH|MindSpore|Specifies the RPOTO dependent library path. |String|File path, which can be a relative path or an absolute path.|None|Optional|None|
 |MS_RDR_ENABLE|MindSpore|Determines whether to enable running data recorder (RDR). If a running exception occurs in MindSpore, the pre-recorded data in MindSpore is automatically exported to assist in locating the cause of the running exception.|Integer|1：enables RDR <br>0：disables RDR|This variable is used together with `MS_RDR_MODE` and `MS_RDR_PATH`.|Optional|None|
 |MS_RDR_MODE|MindSpore|Determines the exporting mode of running data recorder（RDR）.|Integer|1：export data when training process terminates in exceptional scenario <br>2：export data when training process terminates in both exceptional scenario and normal scenario.|This variable is used together with `MS_RDR_ENABLE=1`.|Optional|1|
diff --git a/docs/mindspore/note/source_en/static_graph_syntax_support.md b/docs/mindspore/note/source_en/static_graph_syntax_support.md
index 22e8c603e60841dc68aeaea605ffd9280c6596a5..e928f74dbeeeeb3ead884c7df1c63609975baa6b 100644
--- a/docs/mindspore/note/source_en/static_graph_syntax_support.md
+++ b/docs/mindspore/note/source_en/static_graph_syntax_support.md
@@ -380,7 +380,7 @@ Currently, `Cell` and its subclass instances can be constructed on the network.
 
 However, during construction, the parameter can be specified only in position parameter mode, and cannot be specified in the key-value pair mode. That is, the syntax `cell = Cell(arg_name=value)` is not supported.
 
-Currently, the attributes and APIs related to `Cell` and its subclasses cannot be called on the network unless they are called through `self` in `contrcut` of `Cell`.
+Currently, the attributes and APIs related to `Cell` and its subclasses cannot be called on the network unless they are called through `self` in `construct` of `Cell`.
 
 For details about the definition of `Cell`, click <https://www.mindspore.cn/docs/programming_guide/en/master/cell.html>.
 
diff --git a/docs/mindspore/note/source_zh_cn/conf.py b/docs/mindspore/note/source_zh_cn/conf.py
index 6a5ffa7f0544879eb64dbf581b1e89369730cc5f..8875a22e5490a152db3a521acabe37f404c5859e 100644
--- a/docs/mindspore/note/source_zh_cn/conf.py
+++ b/docs/mindspore/note/source_zh_cn/conf.py
@@ -59,8 +59,8 @@ html_search_language = 'zh'
 
 html_search_options = {'dict': '../../../resource/jieba.txt'}
 
-
-
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindspore/note/source_zh_cn/env_var_list.md b/docs/mindspore/note/source_zh_cn/env_var_list.md
index e3e6edc5a71160a6772d061e230648fe64504776..958ade0b518c0ea8347dc970f9d846fda967df34 100644
--- a/docs/mindspore/note/source_zh_cn/env_var_list.md
+++ b/docs/mindspore/note/source_zh_cn/env_var_list.md
@@ -18,16 +18,18 @@
 |MS_RDR_ENABLE|MindSpore|是否开启程序运行数据记录器（RDR），如果MindSpore出现了运行异常，会自动导出MindSpore中预先记录的数据以辅助定位运行异常的原因|Integer|1：开启RDR功能 <br>0：关闭RDR功能|配合`MS_RDR_MODE`与`MS_RDR_PATH`使用|可选|无|
 |MS_RDR_MODE|MindSpore|指定运行数据记录器（RDR）导出数据的模式|Integer|1：仅在训练进程异常终止时导出数据 <br>2：训练进程异常终止或正常结束时导出数据|配合`MS_RDR_ENABLE=1`使用|可选|1|
 |MS_RDR_PATH|MindSpore|配置程序运行数据记录器（RDR）的文件导出的根目录路径|String|目录路径，仅支持绝对路径|配合`MS_RDR_ENABLE=1`使用，最终RDR文件将保存在`${MS_RDR_PATH}/rank_${RANK_ID}/rdr/`目录下。其中`RANK_ID`为多卡训练场景中的卡号，单卡场景默认`RANK_ID=0`。|可选|无|
-|GLOG_v|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)|Integer|0-DEBUG <br>1-INFO <br>2-WARNING <br>3-ERROR|无|可选|2|
-|GLOG_logtostderr|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)|Integer|1:日志输出到屏幕 <br> 0:日志输出到文件|与GLOG_log_dir一起使用|可选|1|
-|GLOG_log_dir|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)|String|文件路径，支持相对路径与绝对路径|与GLOG_logtostderr一起使用|可选|无|
-|GLOG_log_max|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)|Integer|正整数|无|可选|50|
-|MS_SUBMODULE_LOG_v|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)|Dict{String:Integer...}|LogLevel: 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR<br>SubModual: COMMON, MD, DEBUG, DEVICE, COMMON, IR...|无|可选|无|
-|GLOG_stderrthreshold|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)|Integer|0-DEBUG <br>1-INFO <br>2-WARNING <br>3-ERROR|无|可选|2
+|GLOG_v|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Integer|0-DEBUG <br>1-INFO <br>2-WARNING <br>3-ERROR|无|可选|2|
+|GLOG_logtostderr|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Integer|1:日志输出到屏幕 <br> 0:日志输出到文件|与GLOG_log_dir一起使用|可选|1|
+|GLOG_log_dir|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|String|文件路径，支持相对路径与绝对路径|与GLOG_logtostderr一起使用|可选|无|
+|GLOG_stderrthreshold|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Integer|0-DEBUG <br>1-INFO <br>2-WARNING <br>3-ERROR|无|可选|2
+|MS_SUBMODULE_LOG_v|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Dict{String:Integer...}|LogLevel: 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR<br>SubModual: COMMON, MD, DEBUG, DEVICE, COMMON, IR...|无|可选|无|
+|GLOG_log_max|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Integer|正整数|无|可选|50|
+|logger_maxBytes|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Integer|无|无|可选|52428800|
+|logger_backupCount|MindSpore|[日志功能与用法](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)|Integer|无|无|可选|30
 |OPTION_PROTO_LIB_PATH|MindSpore|RPOTO依赖库库路径|String|目录路径，支持相对路径与绝对路径|无|可选|无|
 |MS_OM_PATH|MindSpore|配置task异常时dump数据路径以及图编译出错时dump的analyze_fail.dat文件的保存目录，保存路径为：指定的路径/rank_${rand_id}/om|String|文件路径，支持相对路径与绝对路径|无|可选|无|
-|MINDSPORE_DUMP_CONFIG|MindSpore|指定[云侧Dump功能](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id6)或[端侧Dump功能](https://www.mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#dump)所依赖的配置文件的路径|String|文件路径，支持相对路径与绝对路径|无|可选|无|
-|MS_DIAGNOSTIC_DATA_PATH|MindSpore|使用[云侧Dump功能](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id6)时，如果Dump配置文件没有设置`path`字段或者设置为空字符串，则“$MS_DIAGNOSTIC_DATA_PATH/debug_dump”就会被当做path的值。若Dump配置文件中设置了`path`字段，则仍以该字段的实际取值为准。|String|文件路径，只支持绝对路径|与MINDSPORE_DUMP_CONFIG配合使用|可选|无|
+|MINDSPORE_DUMP_CONFIG|MindSpore|指定[云侧Dump功能](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#同步dump)或[端侧Dump功能](https://www.mindspore.cn/lite/docs/zh-CN/master/use/benchmark_tool.html#dump)所依赖的配置文件的路径|String|文件路径，支持相对路径与绝对路径|无|可选|无|
+|MS_DIAGNOSTIC_DATA_PATH|MindSpore|使用[云侧Dump功能](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#同步dump)时，如果Dump配置文件没有设置`path`字段或者设置为空字符串，则“$MS_DIAGNOSTIC_DATA_PATH/debug_dump”就会被当做path的值。若Dump配置文件中设置了`path`字段，则仍以该字段的实际取值为准。|String|文件路径，只支持绝对路径|与MINDSPORE_DUMP_CONFIG配合使用|可选|无|
 |MS_ENABLE_CACHE|MindData|是否开启dataset数据处理cache功能，可以实现数据处理过程中数据的cache能力，加速数据集读取及增强处理|String|TRUE：开启数据处理cache功能 <br>FALSE：关闭数据处理cache功能|与MS_CACHE_HOST、MS_CACHE_PORT一起使用|可选|无|
 |MS_CACHE_HOST|MindData|开启cache时，cache服务所在的IP|String|Cache Server所在机器的IP|与MS_ENABLE_CACHE=TRUE、MS_CACHE_PORT一起使用|可选|无|
 |MS_CACHE_PORT|MindData|开启cache时，cache服务所在的端口|String|Cache Server所在机器的端口|与MS_ENABLE_CACHE=TRUE、MS_CACHE_HOST一起使用|可选|无|
diff --git a/docs/mindspore/note/source_zh_cn/static_graph_syntax_support.md b/docs/mindspore/note/source_zh_cn/static_graph_syntax_support.md
index 2463107a23c18f969984a7c703515611b075aa67..7b38eea48bbde38e405ffadcebfad5c38839b9eb 100644
--- a/docs/mindspore/note/source_zh_cn/static_graph_syntax_support.md
+++ b/docs/mindspore/note/source_zh_cn/static_graph_syntax_support.md
@@ -381,7 +381,7 @@ x:[[1. 1. 1. 1.]
 
 但在构造时，参数只能通过位置参数方式传入，不支持通过键值对方式传入，即不支持在语法`cell = Cell(arg_name=value)`。
 
-当前不支持在网络调用`Cell`及其子类相关属性和接口，除非是在`Cell`自己的`contrcut`中通过`self`调用。
+当前不支持在网络调用`Cell`及其子类相关属性和接口，除非是在`Cell`自己的`construct`中通过`self`调用。
 
 `Cell`定义可参考文档：<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cell.html>
 
diff --git a/docs/mindspore/programming_guide/requirements.txt b/docs/mindspore/programming_guide/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/mindspore/programming_guide/requirements.txt
+++ b/docs/mindspore/programming_guide/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/mindspore/programming_guide/source_en/conf.py b/docs/mindspore/programming_guide/source_en/conf.py
index fd02f9f76234625b3cef358215c56c832dd01f7f..86b4e280cfc83128d9764621de8d0b84939e7fba 100644
--- a/docs/mindspore/programming_guide/source_en/conf.py
+++ b/docs/mindspore/programming_guide/source_en/conf.py
@@ -11,10 +11,10 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 
 # -- Project information -----------------------------------------------------
 
@@ -60,23 +60,19 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 #
 html_theme = 'sphinx_rtd_theme'
+#modify layout.html for sphinx_rtd_theme.
+import sphinx_rtd_theme
+layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
+layout_src = '../../../../resource/_static/layout.html'
+if os.path.exists(layout_target):
+    os.remove(layout_target)
+shutil.copy(layout_src, layout_target)
 
 html_static_path = ['_static']
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/mindspore/programming_guide/source_en/control_flow.md b/docs/mindspore/programming_guide/source_en/control_flow.md
index 834d3324854133e72260e81196187e5712b73940..f8ec74204b81fe51912a5fac045f7293d63b3687 100644
--- a/docs/mindspore/programming_guide/source_en/control_flow.md
+++ b/docs/mindspore/programming_guide/source_en/control_flow.md
@@ -518,7 +518,7 @@ The following table lists the side effect operators that are not supported in th
 | ScatterMul            |
 | ScatterNdAdd          |
 | ScatterNdSub          |
-| ScatterNdUpadte       |
+| ScatterNdUpdate       |
 | ScatterNonAliasingAdd |
 | ScatterSub            |
 | ScatterUpdate         |
diff --git a/docs/mindspore/programming_guide/source_en/convert_dataset.ipynb b/docs/mindspore/programming_guide/source_en/convert_dataset.ipynb
index b5aaab73df3b24459bcd45e0117d853d007c41ac..4fa8a78e2af016c1c5fdca981a7ac2a86cec646b 100644
--- a/docs/mindspore/programming_guide/source_en/convert_dataset.ipynb
+++ b/docs/mindspore/programming_guide/source_en/convert_dataset.ipynb
@@ -189,6 +189,7 @@
     "    This example will generate `test.mindrecord0`, `test.mindrecord0.db`, `test.mindrecord1`, `test.mindrecord1.db`, `test.mindrecord2`, `test.mindrecord2.db`, `test.mindrecord3`, `test.mindrecord3.db`, totally eight files, called MindRecord datasets. `test.mindrecord0` and `test.mindrecord0.db` are collectively referred to as a MindRecord file, where `test.mindrecord0` is the data file and `test.mindrecord0.db` is the index file.\n",
     "\n",
     "    **Interface Description:**\n",
+    "    - `FileWriter`: If the parameter shard_num > 1, the original dataset will be saved to shard_num of mindrecord files and each mindrecord file will save the metadata information of adjacent mindrecord files. Then, when using the `MindDataset` interface to read the mindrecord dataset, you can read all shard_num of mindrecord files through `MindDataset(dataset_files=\"./test.mindrecord0\")` and you can read only `test.mindrecord0` mindrecord file through `MindDataset(dataset_files=[\"./test.mindrecord0\"])`.\n",
     "    - `write_raw_data`: write data to memory.\n",
     "    - `commit`: write data in memory to disk.\n",
     "\n",
@@ -439,4 +440,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/docs/mindspore/programming_guide/source_en/custom_debugging_info.md b/docs/mindspore/programming_guide/source_en/custom_debugging_info.md
index de3c59a617173ef509d310de0552d42796d6083a..9b390ce86af2bba69338273f3b3515832d5f0aab 100644
--- a/docs/mindspore/programming_guide/source_en/custom_debugging_info.md
+++ b/docs/mindspore/programming_guide/source_en/custom_debugging_info.md
@@ -344,8 +344,10 @@ MindSpore uses glog to output logs. The following environment variables are comm
     Logs of C++ and Python will be output to different files. The file name of C++ log complies with the naming rule of `GLOG` log file. Here, the name is `mindspore.MachineName.UserName.log.LogLevel.Timestamp`. The file name of Python log is `mindspore.log`.  
     `GLOG_log_dir` can only contains characters such as uppercase letters, lowercase letters, digits, "-", "_" and "/".
 
-- `GLOG_log_max`
-    Each log file's max size is 50 MB by default. But we can change it by set this environment variable. When the log file reaches the max size, the next logs will be written to the new log file.
+- `GLOG_stderrthreshold`
+
+    The log module will print logs to the screen when these logs are output to a file. This environment variable is used to control the log level printed to the screen in this scenario.
+    The default value is 2, indicating the WARNING level. The values are as follows: 0: DEBUG; 1: INFO; 2: WARNING; 3: ERROR; 4: CRITICAL.
 
 - `MS_SUBMODULE_LOG_v`
 
@@ -354,11 +356,6 @@ MindSpore uses glog to output logs. The following environment variables are comm
     The specified sub module log level will overwrite the global log level. The meaning of sub module log level is the same as `GLOG_v`, the sub modules of MindSpore are categorized by source directory is shown in the below table.  
     E.g. when set `GLOG_v=1 MS_SUBMODULE_LOG_v="{PARSER:2,ANALYZER:2}"` then log levels of `PARSER` and `ANALYZER` are WARNING, other modules' log levels are INFO.
 
-- `GLOG_stderrthreshold`
-
-    The log module will print logs to the screen when these logs are output to a file. This environment variable is used to control the log level printed to the screen in this scenario.
-    The default value is 2, indicating the WARNING level. The values are as follows: 0: DEBUG; 1: INFO; 2: WARNING; 3: ERROR; 4: CRITICAL.
-
 Sub modules of MindSpore grouped by source directory:
 
 | Source Files | Sub Module Name |
@@ -388,4 +385,16 @@ Sub modules of MindSpore grouped by source directory:
 | mindspore/core/gvar | COMMON |
 | mindspore/core/ | CORE |
 
+- `GLOG_log_max`
+
+    It is used to control the size of the mindspire C + + module log file. The default maximum is 50MB. You can change the default maximum value of the log file through this environment variable. If the currently written log file exceeds the maximum value, the newly output log content will be written to the new log file.
+
+- `logger_maxBytes`
+
+    It is used to control the size of the mindspire Python module log file. The default is 52428800 bytes.
+
+- `logger_backupCount`
+
+    Used to control the number of mindspire Python module log files. The default is 30.
+
 > The glog does not support log rotate. To control the disk space occupied by log files, use the log file management tool provided by the operating system, such as: logrotate of Linux.
diff --git a/docs/mindspore/programming_guide/source_en/custom_operator_cpu.md b/docs/mindspore/programming_guide/source_en/custom_operator_cpu.md
index 62b7cf66812569752981cab82fd2a8563929d427..26e719e17b308ccbd96d3f7faa4d846d4d57a5ac 100644
--- a/docs/mindspore/programming_guide/source_en/custom_operator_cpu.md
+++ b/docs/mindspore/programming_guide/source_en/custom_operator_cpu.md
@@ -105,7 +105,7 @@ void TransposeCPUFwdKernel::InitKernel(const CNodePtr &kernel_node) {
 - The functions in the class `AnfRuntimeAlgorithm` implement various operations on operator nodes. `shape_` represents the shape of the first input of the operator. `axis_` represents the attribute "perm" of the operator.
 - The parameter "perm" of the`Transpose` operator's primitive is as an input, but "perm" is actually considered as the attribute of the operation when parsing.
 
-> For details of the class `AnfRuntimeAlgorithm`, please refer to the declaration in MindSpore source codes under [mindspore/ccsrc/backend/session/anf_runtime_algorithm.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/backend/session/anf_runtime_algorithm.h).
+> For details of the class `AnfRuntimeAlgorithm`, please refer to the declaration in MindSpore source codes under [mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h).
 
 The definition of the function `Launch` in the source file is as follows: First, get the address of each input and output in turn, and then transform the dimension according to `axis_`, and assign the value to the space pointed to by the output address.
 
diff --git a/docs/mindspore/programming_guide/source_en/cv_mobilenetv2_fine_tune.md b/docs/mindspore/programming_guide/source_en/cv_mobilenetv2_fine_tune.md
index afe5f0da3891f9e7d16f0ab17a95828e88181541..e3bde58dbb567612911e33aeb48a6f4792ee32dd 100644
--- a/docs/mindspore/programming_guide/source_en/cv_mobilenetv2_fine_tune.md
+++ b/docs/mindspore/programming_guide/source_en/cv_mobilenetv2_fine_tune.md
@@ -253,7 +253,7 @@ sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT_PATH]
 
 ## Loading Fine-Tuning Training
 
-Only `train.py` can be run on Windows when MobileNetV2 is used for fine-tuning training. You can run the shell script `run_train.sh` and input [parameters](https://www.mindspore.cn/docs/programming_guide/en/master/cv_mobilenetv2_fine_tune.html#id8) on Linux when MobileNetV2 is used for fine-tuning training.
+Only `train.py` can be run on Windows when MobileNetV2 is used for fine-tuning training. You can run the shell script `run_train.sh` and input [parameters](https://www.mindspore.cn/docs/programming_guide/en/master/cv_mobilenetv2_fine_tune.html#parameter-description) on Linux when MobileNetV2 is used for fine-tuning training.
 
 The Windows system outputs information to an interactive command line. When running `run_train.sh` on the Linux system, use `&> <log_file_path>` at the end of the command line to write the standard output and error output to the log file. After the fine-tuning is successful, training starts. The training time and loss of each epoch are continuously written into the `./train/rank*/log*.log` file. If the fine-tuning fails, an error message is recorded in the preceding log file.
 
@@ -383,7 +383,7 @@ The Windows system outputs information to an interactive command line. When runn
 
 ### Validating the Model
 
-Set mandatory [parameters](https://www.mindspore.cn/docs/programming_guide/en/master/cv_mobilenetv2_fine_tune.html#id8) when using the validation set to test model performance. The default value of `--platform` is `Ascend`. You can set it to `CPU` or `GPU`. Finally, the standard output and error output are displayed in the interactive command line or written to the `eval.log` file.
+Set mandatory [parameters](https://www.mindspore.cn/docs/programming_guide/en/master/cv_mobilenetv2_fine_tune.html#parameter-description) when using the validation set to test model performance. The default value of `--platform` is `Ascend`. You can set it to `CPU` or `GPU`. Finally, the standard output and error output are displayed in the interactive command line or written to the `eval.log` file.
 
 ```bash
 # Windows/Linux with Python
diff --git a/docs/mindspore/programming_guide/source_en/dataset_conversion.md b/docs/mindspore/programming_guide/source_en/dataset_conversion.md
index 62f8d59ab58a280e22be5cd60a68ea3ac4695e98..a1557e30344cbc179802ebaca8b741665f04b31b 100644
--- a/docs/mindspore/programming_guide/source_en/dataset_conversion.md
+++ b/docs/mindspore/programming_guide/source_en/dataset_conversion.md
@@ -78,7 +78,7 @@ Create a MindRecord file containing 100 records, whose samples include the `file
 3. Read MindRecord using `MindDataset`.
 
     ```python
-    data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE)
+    data_set = ds.MindDataset(dataset_files=MINDRECORD_FILE)
     decode_op = vision.Decode()
     data_set = data_set.map(operations=decode_op, input_columns=["data"], num_parallel_workers=2)
     count = 0
@@ -161,7 +161,7 @@ Create a MindRecord file containing 100 records, whose samples include eight fie
 3. Read MindRecord using `MindDataset`.
 
     ```python
-    data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE)
+    data_set = ds.MindDataset(dataset_files=MINDRECORD_FILE)
     count = 0
     for item in data_set.create_dict_iterator():
         count += 1
@@ -273,7 +273,7 @@ You can use the `Cifar10ToMR` class to convert the original CIFAR-10 data to Min
 4. Read MindRecord using `MindDataset`.
 
     ```python
-    data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE)
+    data_set = ds.MindDataset(dataset_files=MINDRECORD_FILE)
     decode_op = vision.Decode()
     data_set = data_set.map(operations=decode_op, input_columns=["data"], num_parallel_workers=2)
     count = 0
@@ -335,7 +335,7 @@ You can use the `ImageNetToMR` class to convert the original ImageNet data (imag
 4. Read MindRecord using `MindDataset`.
 
     ```python
-    data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE)
+    data_set = ds.MindDataset(dataset_files=MINDRECORD_FILE)
     decode_op = vision.Decode()
     data_set = data_set.map(operations=decode_op, input_columns=["image"], num_parallel_workers=2)
     count = 0
@@ -396,7 +396,7 @@ Create a CSV file containing 5 records, convert the CSV file to MindRecord using
 3. Read MindRecord using `MindDataset`.
 
     ```python
-    data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE)
+    data_set = ds.MindDataset(dataset_files=MINDRECORD_FILE)
     count = 0
     for item in data_set.create_dict_iterator(output_numpy=True):
         count += 1
@@ -529,7 +529,7 @@ Use TensorFlow to create a TFRecord file and convert the file to MindRecord usin
 4. Read MindRecord using `MindDataset`.
 
     ```python
-    data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE)
+    data_set = ds.MindDataset(dataset_files=MINDRECORD_FILE)
     decode_op = vision.Decode()
     data_set = data_set.map(operations=decode_op, input_columns=["image_bytes"], num_parallel_workers=2)
     count = 0
diff --git a/docs/mindspore/programming_guide/source_en/distributed_training_gpu.md b/docs/mindspore/programming_guide/source_en/distributed_training_gpu.md
index 91990b4cc0cdd65fcbd0848612d805045709fbe3..bde4c011b5935c2a29f1fccb1b8eac4dc543a109 100644
--- a/docs/mindspore/programming_guide/source_en/distributed_training_gpu.md
+++ b/docs/mindspore/programming_guide/source_en/distributed_training_gpu.md
@@ -24,12 +24,6 @@ The method of downloading and loading the dataset: <https://www.mindspore.cn/doc
 
   For details about how to install OpenMPI, see the official tutorial: <https://www.open-mpi.org/faq/?category=building#easy-build>.
 
-- `NCCL-2.7.6`: Nvidia collective communication library.
-
-  Download NCCL-2.7.6 from <https://developer.nvidia.com/nccl/nccl-legacy-downloads>.
-
-  For details about how to install NCCL, see the official tutorial: <https://docs.nvidia.com/deeplearning/nccl/install-guide/index.html#debian>.
-
 - Password-free login between hosts (required for multi-host training). If multiple hosts are involved in the training, you need to configure password-free login between them. The procedure is as follows:
   1. Ensure that the same user is used to log in to each host. (The root user is not recommended.)
   2. Run the `ssh-keygen -t rsa -P ""` command to generate a key.
diff --git a/docs/mindspore/programming_guide/source_en/dump_in_graph_mode.md b/docs/mindspore/programming_guide/source_en/dump_in_graph_mode.md
index 7ebfc9ca16a9a9b25cf4362dfaf9a52b64cbbd3b..187a24fd36d6e5003a38ae661677fafac7f37f20 100644
--- a/docs/mindspore/programming_guide/source_en/dump_in_graph_mode.md
+++ b/docs/mindspore/programming_guide/source_en/dump_in_graph_mode.md
@@ -409,7 +409,7 @@ Large networks (such as Bert Large) will cause memory overflow when using synchr
     - `kernels`: List of operator names. Turn on the IR save switch `context.set_context(save_graphs=True)` and execute the network to obtain the operator name from the generated `trace_code_graph_{graph_id}`IR file. `kernels` only supports TBE operator, AiCPU operator and communication operator. The data of communication operation input operator will be dumped if `kernels` is set to the name of communication operator. For details, please refer to [Saving IR](https://www.mindspore.cn/docs/programming_guide/en/master/design/mindir.html#saving-ir).
     - `support_device`: Supported devices, default setting is `[0,1,2,3,4,5,6,7]`. You can specify specific device ids to dump specific device data.
     - `enable`: Enable Asynchronous Dump. If synchronous dump and asynchronous dump are enabled at the same time, only synchronous dump will take effect.
-    - `op_debug_mode`: 0: disable overflow check function; 1: enable AiCore overflow check; 2: enable Atomic overflow check; 3: enable all overflow check function. If it is not set to 0, only the data of the overflow operator will be dumped.
+    - `op_debug_mode`: Reserved field, set to 0.
     - `file_format`: Dump file type. It can be either `npy` and `bin`. `npy`: data will be dumped in npy files as host format. `bin`: data will be dumped in protobuf file as device format and need to be transformed to parse using the provided data analysis tool. Please refer to [Asynchronous Dump Data Analysis Sample](#asynchronous-dump-data-analysis-sample) for details. The default value is `bin`.
 
 2. Set Dump environment.
diff --git a/docs/mindspore/programming_guide/source_en/forward_value_and_grad.md b/docs/mindspore/programming_guide/source_en/forward_value_and_grad.md
deleted file mode 100644
index 4b878ed88bda265229a8e32b79867eda8ce87a07..0000000000000000000000000000000000000000
--- a/docs/mindspore/programming_guide/source_en/forward_value_and_grad.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# Forward Value And Grad
-
-`Ascend` `GPU` `CPU` `Model Running`
-
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/mindspore/programming_guide/source_en/forward_value_and_grad.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
-
-## Overview
-
-ForwardValueAndGrad is used to generate the forward value and backend gradient of the input network. The `get_all`, `get_by_list`, and `sens_param` parameters are used to control the gradient calculation method. For details, see [mindspore API](https://www.mindspore.cn/docs/api/en/master/api_python/nn/mindspore.nn.ForwardValueAndGrad.html).
-
-The following is an example of using ForwardValueAndGrad.
-
-## First-order Derivation
-
-The first-order derivative method of MindSpore is `mindspore.nn.ForwardValueAndGrad (network, weights=None, get_all=False, get_by_list=False, sens_param=False)`. When `get_all` is set to `False`, the first input derivative is computed. When `get_all` is set to `True`, all input derivatives are computed. When `get_by_list` is set to `False`, weight derivation is not performed. When `get_by_list` is set to `True`, weight derivation is performed. `sens_param` scales the output value of the network to change the final gradient. Therefore, its dimension is consistent with the output dimension. The following uses the first-order derivation of the MatMul operator for in-depth analysis.
-
-For details about the complete sample code, see [First-order Derivation Sample Code](https://gitee.com/mindspore/docs/tree/master/docs/sample_code/high_order_differentiation/first_order).
-
-### Input Derivation
-
-The input derivation code is as follows:
-
-```python
-import numpy as np
-import mindspore.context as context
-import mindspore.nn as nn
-import mindspore.ops as ops
-from mindspore import Tensor
-from mindspore import ParameterTuple, Parameter
-from mindspore import dtype as mstype
-context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.matmul = ops.MatMul()
-        self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
-    def construct(self, x, y):
-        x = x * self.z
-        out = self.matmul(x, y)
-        return out
-
-class ForwardValueAndGradWrtX(nn.Cell):
-    def __init__(self, net):
-        super(ForwardValueAndGradWrtX, self).__init__()
-        self.net = net
-        self.grad = nn.ForwardValueAndGrad(self.net)
-    def construct(self, x, y):
-        ret = self.grad(x, y)
-        return ret
-
-x = Tensor([[0.8, 0.6, 0.2], [1.8, 1.3, 1.1]], dtype=mstype.float32)
-y = Tensor([[0.11, 3.3, 1.1], [1.1, 0.2, 1.4], [1.1, 2.2, 0.3]], dtype=mstype.float32)
-output = ForwardValueAndGradWrtX(Net())(x, y)
-print(output)
-```
-
-The output is as follows:
-
-```text
-(Tensor(shape=[2, 3], dtype=Float32, value=
-[[9.68000054e-01, 3.20000029e+00, 1.78000009e+00],
- [2.83800006e+00, 8.61999989e+00, 4.13000011e+00]]), Tensor(shape=[2, 3], dtype=Float32, value=
-[[4.50999975e+00, 2.70000005e+00, 3.60000014e+00],
- [4.50999975e+00, 2.70000005e+00, 3.60000014e+00]]))
-```
-
-If the derivatives of the `x` and `y` inputs are considered, you only need to set `self.grad = nn.ForwardValueAndGrad(self.net, get_all=True)` in `ForwardValueAndGradWrtX`.
-
-### Weight Derivation
-
-If the derivation of weights is considered, change `ForwardValueAndGradWrtX` to the following:
-
-```python
-class ForwardValueAndGradWrtX(nn.Cell):
-    def __init__(self, net):
-        super(ForwardValueAndGradWrtX, self).__init__()
-        self.net = net
-        self.params = ParameterTuple(net.trainable_params())
-        self.grad = nn.ForwardValueAndGrad(self.net, weights=self.params, get_by_list=True)
-    def construct(self, x, y):
-        ret = self.grad(x, y)
-        return ret
-```
-
-```python
-output = ForwardValueAndGradWrtX(Net())(x, y)
-print(output)
-```
-
-The output is as follows:
-
-```text
-(Tensor(shape=[2, 3], dtype=Float32, value=
-[[9.68000054e-01, 3.20000029e+00, 1.78000009e+00]
- [2.83800006e+00, 8.61999989e+00, 4.13000011e+00]]), (Tensor(shape=[1], dtype=Float32, value= [2.15359993e+01]),))
-```
-
-### Gradient Value Scaling
-
-You can use the `sens_param` parameter to control the scaling of the gradient value.
-
-```python
-class ForwardValueAndGradWrtX(nn.Cell):
-    def __init__(self, net):
-        super(ForwardValueAndGradWrtX, self).__init__()
-        self.net = net
-        self.grad = nn.ForwardValueAndGrad(self.net, sens_param=True)
-        self.grad_wrt_output = Tensor([[0.1, 0.6, 0.2], [0.8, 1.3, 1.1]], dtype=mstype.float32)
-    def construct(self, x, y):
-        ret = self.grad(x, y, self.grad_wrt_output)
-        return ret
-```
-
-```python
-output = ForwardValueAndGradWrtX(Net())(x, y)
-print(output)
-```
-
-The output is as follows:
-
-```text
-(Tensor(shape=[2, 3], dtype=Float32, value=
-[[9.68000054e-01, 3.20000029e+00, 1.78000009e+00],
- [2.83800006e+00, 8.61999989e+00, 4.13000011e+00]]), Tensor(shape=[2, 3], dtype=Float32, value=
-[[2.21099997e+00, 5.09999990e-01, 1.49000001e+00],
- [5.58799982e+00, 2.68000007e+00, 4.07000017e+00]]))
-```
-
-`self.grad_wrt_output` may be denoted as the following form:
-
-```python
-self.grad_wrt_output = Tensor([[s1, s2, s3], [s4, s5, s6]])
-```
-
-The output value after scaling is the product of the original output value and the element corresponding to `self.grad_wrt_output`.
diff --git a/docs/mindspore/programming_guide/source_en/images/dot_to_png.png b/docs/mindspore/programming_guide/source_en/images/dot_to_png.png
new file mode 100644
index 0000000000000000000000000000000000000000..9689a1e575c9900fbde64b4af206cc48ebad6dbb
Binary files /dev/null and b/docs/mindspore/programming_guide/source_en/images/dot_to_png.png differ
diff --git a/docs/mindspore/programming_guide/source_en/index.rst b/docs/mindspore/programming_guide/source_en/index.rst
index 082bb0c2bad15df4e7d3a7249ad7d85a1df92098..92198ea4a849f3682c8d95fc5dea66f45f94dd39 100644
--- a/docs/mindspore/programming_guide/source_en/index.rst
+++ b/docs/mindspore/programming_guide/source_en/index.rst
@@ -78,6 +78,7 @@ MindSpore Programming Guide
    constexpr
    hypermap
    optim
+   train_and_eval
 
 .. toctree::
    :glob:
@@ -816,18 +817,6 @@ MindSpore Programming Guide
                         </div>
                      </a>
                   </div>
-                  <div class="doc-article-item all hardware-ascend hardware-gpu hardware-cpu stage-Model-Running experience-local hidden">
-                     <a href="https://www.mindspore.cn/docs/programming_guide/en/master/forward_value_and_grad.html" class="article-link">
-                        <div>
-                              <div class="doc-article-head">
-                                 <span class="doc-head-content">Forward Value And Grad</span>
-                              </div>
-                              <div class="doc-article-desc">
-                              Forward Value And Grad is used to generate the forward value and backend gradient of the input network.
-                              </div>
-                        </div>
-                     </a>
-                  </div>
                   <div class="doc-article-item all hardware-ascend hardware-gpu hardware-cpu stage-Model-Development experience-local hidden">
                      <a href="https://www.mindspore.cn/docs/programming_guide/en/master/grad_operation.html" class="article-link">
                         <div>
diff --git a/docs/mindspore/programming_guide/source_en/multi_platform_inference.md b/docs/mindspore/programming_guide/source_en/multi_platform_inference.md
index c30aeb12b378cb7bd23254d5cf6575839dc1cf5a..ba512224d9bad63cc4a2dc3683ff8116d286fff4 100644
--- a/docs/mindspore/programming_guide/source_en/multi_platform_inference.md
+++ b/docs/mindspore/programming_guide/source_en/multi_platform_inference.md
@@ -173,4 +173,6 @@ MindSpore defines logical network structures and operator attributes through a u
   <td>YOLOv4</td>
   <td>YOLOv5</td>
 </tr>
-</table>
\ No newline at end of file
+</table>
+
+> In addition to the network in the above table, if the operator used in the user-defined network can be [exported to the MindIR model file](https://mindspore.cn/docs/programming_guide/en/master/save_model.html#export-mindir-model), the MindIR model file can also be used to execute inference tasks.
diff --git a/docs/mindspore/programming_guide/source_en/read_ir_files.md b/docs/mindspore/programming_guide/source_en/read_ir_files.md
index 8e2479968a06e023017620f2af73ad9a5dff7b43..24e7ff0aeaa7bf4af0a5926df8d000727607ba21 100644
--- a/docs/mindspore/programming_guide/source_en/read_ir_files.md
+++ b/docs/mindspore/programming_guide/source_en/read_ir_files.md
@@ -269,6 +269,20 @@ The [CNode](https://www.mindspore.cn/docs/programming_guide/en/master/design/min
   # Corresponding source code/#debug_name
 ```
 
+### dot Introduction
+
+We can use this file by [graphviz](http://graphviz.org/) as the input to generate images for users to view the model structure. For example, under the Linux operating system, we can convert a PNG image by the following command.
+
+```shell
+dot -Tpng -o 04_abstract_specialize_0014.png 04_abstract_specialize_0014.dot
+```
+
+The transformed image is shown below, and we can visually see the model structure. The Different black boxes distinguish different subgraphs, and the blue arrows between graphs represent calling another graph. The blue area represents the parameter, the rectangle represents the parameter list of the graph, the hexagon and the black arrow represent the parameter as the input of the CNode to participate in the calculation process. The yellow rectangle represents the CNode. As can be seen from the picture, the CNode input starts from index 0, and the 0th input (that is, the purple or green area) represents what calculation the operator will perform, which is connected by a dotted arrow. The type is usually an operator primitive, or it can also be another graph. The rest inputs are the parameters required for the calculation.
+
+![04_abstract_specialize_0014.png](./images/dot_to_png.png)
+
+For models with multiple operators, the picture will be very large. It is recommended using the visualization component [MindInsight](https://www.mindspore.cn/mindinsight/docs/en/master/dashboard.html#computational-graph-visualization) to visualize computing graphs.
+
 ## Reading analyze_fail.dat
 
 In the process of `MindSpore` compiling a graph, the exceptions about graph evaluating fail usually happen. But we can find
diff --git a/docs/mindspore/programming_guide/source_en/train_and_eval.md b/docs/mindspore/programming_guide/source_en/train_and_eval.md
new file mode 100644
index 0000000000000000000000000000000000000000..c70f93706c59998a8afba3f090f08218a2970ef9
--- /dev/null
+++ b/docs/mindspore/programming_guide/source_en/train_and_eval.md
@@ -0,0 +1,455 @@
+# Construct training and evaluation network
+
+`Ascend` `GPU` `CPU` `Model Development` `Model Serving` `Model Evaluation`
+
+Translator: [redundan3y](https://gitee.com/redundan3y)
+
+[![View source](https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png)](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/programming_guide/source_en/train_and_eval.md)
+
+## Overview
+
+The previous chapters explained the basic elements used by MindSpore to build the network, such as MindSpore's network basic unit, loss function and optimizer, etc. This document focuses on how to use these elements to train and evaluate networks.
+
+## Build a feedforward network
+
+You can use Cell to build a feedforward network, we construct a simple linear regression LinearNet here:
+
+> For more details about Cell
+<https://www.mindspore.cn/docs/programming_guide/en/master/build_net.html>
+
+```python
+import numpy as np
+import mindspore.nn as nn
+from mindspore.common.initializer import Normal
+
+class LinearNet(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.fc = nn.Dense(1, 1, Normal(0.02), Normal(0.02))
+
+    def construct(self, x):
+        return self.fc(x)
+```
+
+## Building a training network
+
+Building a training network requires stacking a loss function, backpropagation, and an optimizer on top of the forward network.
+
+### Wrapping function with training network
+
+MindSpore's nn module provides a training network encapsulation function `TrainOneStepCell`. Next, we will encapsulate the previously defined LinearNet into a training network using `nn.TrainOneStepCell`. The specific process is as follows:
+
+```python
+# Instantiate the feedforward network
+net = LinearNet()
+# Set the loss function and connect the feedforward network with the loss function
+crit = nn.MSELoss()
+net_with_criterion = nn.WithLossCell(net, crit)
+# Set optimizer
+opt = nn.Momentum(net.trainable_params(), learning_rate=0.005, momentum=0.9)
+# Define the training network
+train_net = nn.TrainOneStepCell(net_with_criterion, opt)
+# Set the network to training mode
+train_net.set_train()
+```
+
+`set_train` recursively configures the `training` attribute of a `Cell`. When implementing networks with different training and inference structures, the training and inference scenarios can be distinguished by the `training` attribute, such as `BatchNorm`, `Dropout`.
+
+The previous chapter [Loss Function](https://www.mindspore.cn/docs/programming_guide/en/master/loss.html) has introduced how to define the loss function and use `WithLossCell` to convert the feedforward network connected with the loss function, here is how to obtain gradients and update weights to form a complete training network. The specific implementation of `nn.TrainOneStepCell` provided by MindSpore is as follows:
+
+```python
+import mindspore.ops as ops
+from mindspore.context import get_auto_parallel_context, ParallelMode
+from mindspore.communication import get_group_size
+
+def get_device_num():
+    """Get the device num."""
+    parallel_mode = auto_parallel_context().get_parallel_mode()
+    if parallel_mode == "stand_alone":
+        device_num = 1
+        return device_num
+
+    if auto_parallel_context().get_device_num_is_set() is False:
+        device_num = get_group_size()
+    else:
+        device_num = auto_parallel_context().get_device_num()
+    return device_num
+
+class TrainOneStepCell(nn.Cell):
+    def __init__(self, network, optimizer, sens=1.0):
+        super(TrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_grad()
+        self.optimizer = optimizer
+        self.weights = self.optimizer.parameters
+        self.grad = ops.GradOperation(get_by_list=True, sens_param=True)
+        self.sens = sens
+        self.reducer_flag = False
+        self.grad_reducer = ops.identity
+        self.parallel_mode = auto_parallel_context().get_parallel_mode()
+        self.reducer_flag = self.parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL)
+        if self.reducer_flag:
+            self.mean = auto_parallel_context().get_gradients_mean()
+            self.degree = get_device_num()
+            self.grad_reducer = nn.DistributedGradReducer(self.weights, self.mean, self.degree)
+
+    def construct(self, *inputs):
+        loss = self.network(*inputs)
+        sens = F.fill(loss.dtype, loss.shape, self.sens)
+        grads = self.grad(self.network, self.weights)(*inputs, sens)
+        grads = self.grad_reducer(grads)
+        loss = F.depend(loss, self.optimizer(grads))
+        return loss
+```
+
+`TrainOneStepCell` contains following input parameters:
+
+- network (Cell): The network involved in training contains the computational logic of the feedforward network and the loss function, input data and labels, and output the loss function value.
+
+- optimizer (Cell): The chosen optimizer.
+
+- sens (float): Backpropagation scaling.
+
+The following parameters will also be defined when `TrainOneStepCell` is initialized:
+
+- GradOperation: Backpropagation function for backpropagating and obtaining gradients.
+
+- DistributedGradReducer: It is used for gradient broadcasting in distributed computing scenarios, it does not need to be used for a stand-alone machine with a single graphic card.
+
+The training execution process defined by `construct` includes 4 main steps:
+
+- `loss = self.network(*inputs)`: Execute the feedforward network and calculate the loss function value of the current input.
+- `grads = self.grad(self.network, self.weights)(*inputs, sens)`: Perform backpropagation and calculate the gradient.
+- `grads = self.grad_reducer(grads)`: Gradient broadcasting is performed in a distributed situation, and the input gradient is directly returned when using a stand-alone machine with a single graphic card.
+- `self.optimizer(grads)`: Use the optimizer to update the weights.
+
+### Generate dataset and perform training action
+
+Generate the dataset and perform data preprocessing:
+
+```python
+import mindspore.dataset as ds
+import numpy as np
+
+def get_data(num, w=2.0, b=3.0):
+    for _ in range(num):
+        x = np.random.uniform(-10.0, 10.0)
+        noise = np.random.normal(0, 1)
+        y = x * w + b + noise
+        yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32)
+
+def create_dataset(num_data, batch_size=16):
+    dataset = ds.GeneratorDataset(list(get_data(num_data)), column_names=['data', 'label'])
+    dataset = dataset.batch(batch_size)
+    return dataset
+
+train_dataset = create_dataset(num_data=160)
+```
+
+Train the model using the the loss function output value of the training network encapsulated by `nn.TrainOneStepCell`:
+
+```python
+# Get training data generated during the process
+epochs = 2
+for epoch in range(epochs):
+    for d in train_dataset.create_dict_iterator():
+        result = train_net(d["data"], d["label"])
+        print(result)
+```
+
+### Customize a network training wrapper function
+
+In general, users can use the `nn.TrainOneStepCell` provided by the framework to encapsulate the training network. When the `nn.TrainOneStepCell` cannot meet the requirements, you need to customize the `TrainOneStepCell` that meets the actual situation. For example:
+
+1. Based on `nn.TrainOneStepCell`, Bert in ModelZoo adds truncated gradient operation to achieve better training effect. The code snippet of the training wrapper function defined by Bert is as follows:
+
+> For more details about Bert network : https://gitee.com/mindspore/models/tree/master/official/nlp/bert
+
+```python
+GRADIENT_CLIP_TYPE = 1
+GRADIENT_CLIP_VALUE = 1.0
+
+clip_grad = ops.MultitypeFuncGraph("clip_grad")
+
+@clip_grad.register("Number", "Number", "Tensor")
+def _clip_grad(clip_type, clip_value, grad):
+    if clip_type not in (0, 1):
+        return grad
+    dt = ops.dtype(grad)
+    if clip_type == 0:
+        new_grad = ops.clip_by_value(grad, ops.cast(ops.tuple_to_array((-clip_value,)), dt),
+                                     ops.cast(ops.tuple_to_array((clip_value,)), dt))
+    else:
+        new_grad = nn.ClipByNorm()(grad, ops.cast(ops.tuple_to_array((clip_value,)), dt))
+    return new_grad
+
+class BertTrainOneStepCell(nn.TrainOneStepCell):
+    def __init__(self, network, optimizer, sens=1.0, enable_clip_grad=True):
+        super(BertTrainOneStepCell, self).__init__(network, optimizer, sens)
+        self.cast = ops.Cast()
+        self.hyper_map = ops.HyperMap()
+        self.enable_clip_grad = enable_clip_grad
+
+    def construct(self, *inputs):
+        weights = self.weights
+        loss = self.network(*inputs)
+        grads = self.grad(self.network, weights)(*inputs, self.cast(ops.tuple_to_array((self.sens,)), mstype.float32))
+        if self.enable_clip_grad:
+            # perform gradient truncation
+            grads = self.hyper_map(ops.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        grads = self.grad_reducer(grads)
+        self.optimizer(grads)
+        return loss
+```
+
+2. Wide&Deep outputs two loss function values, and performs back-propagation and parameter update for the Wide and Deep parts of the network respectively, while `nn.TrainOneStep` is only suitable to one loss function value, so Wide&Deep in ModelZoo has a customized trainning wrapper function, the code snippet is as follows:
+
+> For more Wide&Deep network details: <https://gitee.com/mindspore/models/tree/master/official/recommend/wide_and_deep>.
+
+```python
+class IthOutputCell(nn.Cell):
+    """
+    IthOutputCell
+    """
+    def __init__(self, network, output_index):
+        super(IthOutputCell, self).__init__()
+        self.network = network
+        self.output_index = output_index
+
+    def construct(self, *inputs):
+        """
+        IthOutputCell construct
+        """
+        predict = self.network(*inputs)[self.output_index]
+        return predict
+
+class TrainStepWrap(nn.Cell):
+    def __init__(self, network, config, sens=1000.0):
+        super(TrainStepWrap, self).__init__()
+        self.network = network
+        self.network.set_train()
+        self.trainable_params = network.trainable_params()
+        weights_w = []
+        weights_d = []
+        for params in self.trainable_params:
+            if 'wide' in params.name:
+                weights_w.append(params)
+            else:
+                weights_d.append(params)
+
+        self.weights_w = ParameterTuple(weights_w)
+        self.weights_d = ParameterTuple(weights_d)
+        self.optimizer_w = nn.FTRL(learning_rate=config.ftrl_lr,
+                                   params=self.weights_w,
+                                   l1=5e-4,
+                                   l2=5e-4,
+                                   initial_accum=0.1,
+                                   loss_scale=sens)
+
+        self.optimizer_d = nn.Adam(self.weights_d,
+                                   learning_rate=config.adam_lr,
+                                   eps=1e-6,
+                                   loss_scale=sens)
+
+        self.hyper_map = ops.HyperMap()
+
+        self.grad_w = ops.GradOperation(get_by_list=True, sens_param=True)
+        self.grad_d = ops.GradOperation(get_by_list=True, sens_param=True)
+
+        self.sens = sens
+        self.loss_net_w = IthOutputCell(network, output_index=0)
+        self.loss_net_d = IthOutputCell(network, output_index=1)
+        self.loss_net_w.set_grad()
+        self.loss_net_w.set_grad()
+
+        self.reducer_flag = False
+        self.grad_reducer_w = None
+        self.grad_reducer_d = None
+        parallel_mode = get_auto_parallel_context("parallel_mode")
+        if parallel_mode in (ParallelMode.DATA_PARALLEL,
+                             ParallelMode.HYBRID_PARALLEL):
+            self.reducer_flag = True
+        if self.reducer_flag:
+            mean = get_auto_parallel_context("gradients_mean")
+            degree = get_auto_parallel_context("device_num")
+            self.grad_reducer_w = DistributedGradReducer(
+                self.optimizer_w.parameters, mean, degree)
+            self.grad_reducer_d = DistributedGradReducer(
+                self.optimizer_d.parameters, mean, degree)
+
+    def construct(self, *inputs):
+        """
+        TrainStepWrap construct
+        """
+        weights_w = self.weights_w
+        weights_d = self.weights_d
+        loss_w, loss_d = self.network(*inputs)
+
+        sens_w = ops.Fill()(ops.DType()(loss_w), ops.Shape()(loss_w), self.sens)
+        sens_d = ops.Fill()(ops.DType()(loss_d), ops.Shape()(loss_d), self.sens)
+        grads_w = self.grad_w(self.loss_net_w, weights_w)(*inputs, sens_w)
+        grads_d = self.grad_d(self.loss_net_d, weights_d)(*inputs, sens_d)
+        if self.reducer_flag:
+            # apply grad reducer on grads
+            grads_w = self.grad_reducer_w(grads_w)
+            grads_d = self.grad_reducer_d(grads_d)
+        return ops.depend(loss_w, self.optimizer_w(grads_w)), ops.depend(
+            loss_d, self.optimizer_d(grads_d))
+```
+
+## Build an evaluation network
+
+The function of the evaluation network is to output predicted values and true labels in order to evaluate the effect of model training on the validation set. MindSpore also provides an evaluation network wrapper function `nn.WithEvalCell`.
+
+### Use the evaluation network wrapper function
+
+Build an evaluation network using the previously defined feedforward network and loss function:
+
+```python
+# Build an evaluation network
+eval_net = nn.WithEvalCell(net, crit)
+eval_net.set_train(False)
+```
+
+You can obtain the model evaluation results by executing `eval_net` to output predicted values and labels, processing them with evaluation metrics. The specific definition of `nn.WithEvalCell` is as follows:
+
+```python
+class WithEvalCell(nn.Cell):
+    def __init__(self, network, loss_fn, add_cast_fp32=False):
+        super(WithEvalCell, self).__init__(auto_prefix=False)
+        self._network = network
+        self._loss_fn = loss_fn
+
+    def construct(self, data, label):
+        outputs = self._network(data)
+        if self.add_cast_fp32:
+            label = F.mixed_precision_cast(mstype.float32, label)
+            outputs = F.cast(outputs, mstype.float32)
+        loss = self._loss_fn(outputs, label)
+        return loss, outputs, label
+```
+
+`WithEvalCell` contains following input parameters:
+
+- network (Cell): A feedforward network that inputs data and labels, and outputs predicted values.
+
+- loss_fn (Cell): The loss function used. The `WithEvalCell` provided by MindSpore outputs `loss`, so that the loss function can also be used as an evaluation indicator. In actual scenarios, `loss` is not a necessary output item.
+
+- add_cast_fp32 (Bool): Whether to use float32 precision to calculate the loss function, currently this parameter only takes effect when `Model` uses `nn.WithEvalCell` to build an evaluation network.
+
+The training execution process defined by `construct` includes 2 main steps:
+
+- `outputs = self._network(data)`: Executes a feedforward network to compute predictions for the current input data.
+
+- `return loss, outputs, label`: Output the loss function value, predicted value and label of the current input.
+
+### Create dataset and perform evaluation
+
+Define model evaluation metrics:
+
+```python
+mae = nn.MAE()
+loss = nn.Loss()
+```
+
+Create a validation set using the `DatasetGenerator` defined earlier:
+
+```python
+eval_dataset = create_dataset(num_data=160)
+```
+
+Iterate through the dataset, execute `eval_net`, and use the output of `eval_net` to calculate evaluation metrics:
+
+```python
+mae.clear()
+loss.clear()
+for d in eval_dataset.create_dict_iterator():
+    outputs = eval_net(d["data"], d["label"])
+    mae.update(outputs[1], outputs[2])
+    loss.update(outputs[0])
+
+mae_result = mae.eval()
+loss_result = loss.eval()
+print("mae: ", mae_result)
+print("loss: ", loss_result)
+```
+
+`nn.WithEvalCell` outputs the value of the loss function to facilitate the calculation of the evaluation index `Loss`, if not needed, this output can be ignored.
+
+Since the data and weights are random, the training results are also random.
+
+### Customize evaluation network wrapper function
+
+Earlier we explained the computation logic of `nn.WithEvalCell`, and noticed that `nn.WithEvalCell` only has two inputs data and label, which are obviously not applicable when there are multiple data or labels. In this case, if you want to build an evaluation network, you need to customize `WithEvalCell`. This is because the evaluation network needs to use the data to calculate predictions and output labels. When the user passes more than two inputs to `WithEvalCell`, the framework cannot identify which of these inputs are data and which are labels. There is no need to define `loss_fn` if the loss function is not required as an evaluation index while customizing.
+Taking the input of three inputs `data`, `label1`, `label2` as an example, you can customize `WithEvalCell`:
+
+```python
+class CustomWithEvalCell(nn.Cell):
+    def __init__(self, network):
+        super(CustomWithEvalCell, self).__init__(auto_prefix=False)
+        self._network = network
+
+    def construct(self, data, label1, label2):
+        outputs = self._network(data)
+        return outputs, label1, label2
+
+eval_net = CustomWithEvalCell(net)
+eval_net.set_train(False)
+```
+
+The basic evaluation index provided by MindSpore is only applicable to two inputs logits and label. When the evaluation network outputs multiple labels or multiple predicted values, you need to call the set_indexes function to specify which outputs are used to calculate the evaluation index. If multiple outputs need to be used to calculate evaluation indicators, it means that the built-in evaluation indicators in MindSpore cannot meet the requirements and need to be customized.
+
+Please refer to <<https://www.mindspore.cn/docs/programming_guide/en/master/self_define_metric.html>> for the usage and customization methods of Metric.
+
+## Build the weight sharing of the network
+
+As can be seen from the previous introduction, the feedforward network, training network and evaluation network have different logics, so we need to build three networks when needed. We often use the trained model for inference and evaluation, which requires the same weight values in the inference and evaluation network as in the training network. Use the model save and load interface to save the trained model and load it into the inference and evaluation network to ensure the same weight values. When model training is completed on the training platform, and then inference is performed on other inference platforms, model saving and loading are essential.
+
+However, in the process of network debugging, or using the training-while-verification method for model tuning, model training, evaluation or inference are often completed in the same Python script. At this time, MindSpore's weight sharing mechanism can ensure that the weights between different networks are consistent.
+
+When using MindSpore to build different network structures, as long as these network structures are encapsulated on the basis of an instance, all the weights in this instance are shared, when the weights in a single network change, weights in other networks will be changed to are synchronized.
+
+In this document, the weight sharing mechanism is used when defining the training and evaluation network:
+
+```python
+# Instantiate the feedforward network
+net = LinearNet()
+# Set the loss function and connect to the feedforward network
+crit = nn.MSELoss()
+net_with_criterion = nn.WithLossCell(net, crit)
+# Set optimizer
+opt = nn. Adam(params=net.trainable_params())
+# Define the training network
+train_net = nn.TrainOneStepCell(net_with_criterion, opt)
+train_net.set_train()
+# Build the evaluation network
+eval_net = nn.WithEvalCell(net, crit)
+eval_net.set_train(False)
+```
+
+Both `train_net` and `eval_net` are encapsulated basded on `net` instance, so during model evaluation the weights of `train_net` do not need to be loaded.
+
+If the feedforward network is re-defined when `eval_net` is constructed, there will be no shared weights between `train_net` and `eval_net`:
+
+```python
+# Instantiate the feedforward network
+net = LinearNet()
+# Set the loss function and connect to the feedforward network
+crit = nn.MSELoss()
+net_with_criterion = nn.WithLossCell(net, crit)
+# Set optimizer
+opt = nn. Adam(params=net.trainable_params())
+# Define the training network
+train_net = nn.TrainOneStepCell(net_with_criterion, opt)
+train_net.set_train()
+
+# Instantiate the feedforward network again
+net2 = LinearNet()
+# Build the evaluation network
+eval_net = nn.WithEvalCell(net2, crit)
+eval_net.set_train(False)
+```
+
+Till this point, if you need to evaluate the model after training, the weights from `train_net` need to be loaded into `eval_net`. Taking advantage of the weight sharing mechanism is a shortcut to perform model training, evaluation, and inference in the same script. It should be noted that if two scenarios of training and inference are constructed in the feedforward network structure, it is also necessary to ensure that the conditions of weight sharing are met. If the same weight is used in the branch statement, the network structure related to the weight should only be instantiated once.
+
+This section explains how to build and execute network models, later chapters will further explain how to train and evaluate models through the high-level API `Model`.
\ No newline at end of file
diff --git a/docs/mindspore/programming_guide/source_zh_cn/apply_dimention_reduce_training.md b/docs/mindspore/programming_guide/source_zh_cn/apply_dimention_reduce_training.md
index b66e10220deeb0209af402ef1b547fae8b37511d..a2483e66deb41871af31b3e1b8d23879c833a205 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/apply_dimention_reduce_training.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/apply_dimention_reduce_training.md
@@ -237,11 +237,10 @@ load_param_into_net(net, weight_dict)
 ```python
 # define loss
 loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=0.1, num_classes=1001)
-loss_scale = FixedLossScaleManager(1024, drop_overflow_update=False)
 
 # define optimizer
 group_params = init_group_params(net)
-opt = SGD(group_params, learning_rate=1, loss_scale=loss_scale)
+opt = SGD(group_params, learning_rate=1)
 
 # define metrics
 metrics = {"acc"}
@@ -268,8 +267,7 @@ boost_dict = {
 }
 
 # define model
-model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=metrics,
-              amp_level="O2", boost_level="O1", keep_batchnorm_fp32=False, boost_config_dict=boost_dict)
+model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, boost_level="O1", boost_config_dict=boost_dict)
 ```
 
 ### 训练模型
diff --git a/docs/mindspore/programming_guide/source_zh_cn/apply_parameter_server_training.md b/docs/mindspore/programming_guide/source_zh_cn/apply_parameter_server_training.md
index 7ebd7d74c531c2d67a2aa392478fb3a5b7266910..000a8164d22a5c0a80598622c7d562b4c2468b41 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/apply_parameter_server_training.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/apply_parameter_server_training.md
@@ -31,7 +31,7 @@ MindSpore的参数服务器采用了自研的通信框架作为基础架构，
 1. 首先调用`mindspore.context.set_ps_context(enable_ps=True)`开启Parameter Server训练模式.
 
     - 此接口需在`mindspore.communication.init()`之前调用。
-    - 若没有调用此接口，下面的[环境变量设置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_parameter_server_training.html#id5)则不会生效。
+    - 若没有调用此接口，下面的[环境变量设置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_parameter_server_training.html#环境变量设置)则不会生效。
     - 调用`mindspore.context.reset_ps_context()`可以关闭Parameter Server训练模式。
 
 2. 在本训练模式下，有以下两种调用接口方式以控制训练参数是否通过Parameter Server进行更新，并且可以控制参数初始化位置：
diff --git a/docs/mindspore/programming_guide/source_zh_cn/auto_parallel.md b/docs/mindspore/programming_guide/source_zh_cn/auto_parallel.md
index 8458c5264521b9846dd5beacef9deae1830f7c4a..d4dfb88d93a97c961c5def060a7aae438f585613 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/auto_parallel.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/auto_parallel.md
@@ -197,7 +197,7 @@ context.get_auto_parallel_context("gradient_fp32_sync")
 
 `auto_parallel_search_mode`字段现已替换为`search_mode`，`auto_parallel_search_mode`将会在未来版本中删除。该字段用于指示并行策略搜索使用的算法。当前，MindSpore提供了`dynamic_programming`，`recursive_programming`和`sharding_propagation`三种搜索策略的算法用于搜索算子级并行策略，默认是`dynamic_programming`。
 
-`dynamic_programming`能够搜索出代价模型刻画的最优策略，但在搜索巨大网络模型的并行策略时耗时较长；而`recursive_programming`能瞬间搜索出并行策略，同时在已验证的常用网络中搜索出来的策略是最优策略，但在未经验证的某些特殊网络中可能找到次优策略。`sharding_propagation`要求用户配置一些算子的并行策略，并以此为基础向整个网络传播。在传播时，算法会尽量选取引发张量[重排布](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#id10)通信最少的策略。MindSpore提供了参数，让用户自由选择搜索算法。
+`dynamic_programming`能够搜索出代价模型刻画的最优策略，但在搜索巨大网络模型的并行策略时耗时较长；而`recursive_programming`能瞬间搜索出并行策略，同时在已验证的常用网络中搜索出来的策略是最优策略，但在未经验证的某些特殊网络中可能找到次优策略。`sharding_propagation`要求用户配置一些算子的并行策略，并以此为基础向整个网络传播。在传播时，算法会尽量选取引发张量[重排布](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#自动并行原理)通信最少的策略。MindSpore提供了参数，让用户自由选择搜索算法。
 
 代码样例如下：
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/callback.md b/docs/mindspore/programming_guide/source_zh_cn/callback.md
index 30af93c1270b26c2fe45a3c9186c0ac8cd77039e..030f92acf501c84876e5663970c14d772a66e028 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/callback.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/callback.md
@@ -44,6 +44,6 @@ MindSpore不但有功能强大的内置回调函数，还可以支持用户自
 
 2. 实现保存训练过程中精度最高的checkpoint文件，用户可以自定义在每一轮迭代后都保存当前精度最高的模型。
 
-详细内容，请参考[自定义Callback官网教程](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id3)。
+详细内容，请参考[自定义Callback官网教程](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#自定义callback)。
 
 根据教程，用户可以很容易实现具有其他功能的自定义回调函数，如实现在每一轮训练结束后都输出相应的详细训练信息，包括训练进度、训练轮次、训练名称、loss值等；如实现在loss或模型精度达到一定值后停止训练，用户可以设定loss或模型精度的阈值，当loss或模型精度达到该阈值后就提前终止训练等。
diff --git a/docs/mindspore/programming_guide/source_zh_cn/conf.py b/docs/mindspore/programming_guide/source_zh_cn/conf.py
index 4885e346836121843be7ac36f22b18d4fd62b568..d9f67fa67c04d9fc0103d7e7929f4e73364b1804 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/conf.py
+++ b/docs/mindspore/programming_guide/source_zh_cn/conf.py
@@ -11,11 +11,9 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import IPython
-import re
 import sys
-import nbsphinx as nbs
-
 # -- Project information -----------------------------------------------------
 
 project = 'MindSpore'
@@ -60,6 +58,13 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 #
 html_theme = 'sphinx_rtd_theme'
+#modify layout.html for sphinx_rtd_theme.
+import sphinx_rtd_theme
+layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
+layout_src = '../../../../resource/_static/layout.html'
+if os.path.exists(layout_target):
+    os.remove(layout_target)
+shutil.copy(layout_src, layout_target)
 
 html_static_path = ['_static']
 
@@ -67,22 +72,9 @@ html_search_language = 'zh'
 
 html_search_options = {'dict': '../../../resource/jieba.txt'}
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
-
-
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/mindspore/programming_guide/source_zh_cn/control_flow.ipynb b/docs/mindspore/programming_guide/source_zh_cn/control_flow.ipynb
index 2b1f9cb7bcf762902ad459af85308d730ba03f73..79e726d22a61860f523e15866ec3533b4ced8d27 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/control_flow.ipynb
+++ b/docs/mindspore/programming_guide/source_zh_cn/control_flow.ipynb
@@ -710,7 +710,7 @@
     "| ScatterMul            |\n",
     "| ScatterNdAdd          |\n",
     "| ScatterNdSub          |\n",
-    "| ScatterNdUpadte       |\n",
+    "| ScatterNdUpdate       |\n",
     "| ScatterNonAliasingAdd |\n",
     "| ScatterSub            |\n",
     "| ScatterUpdate         |\n",
diff --git a/docs/mindspore/programming_guide/source_zh_cn/convert_dataset.ipynb b/docs/mindspore/programming_guide/source_zh_cn/convert_dataset.ipynb
index 37de2ca7807ad221e5afb6f3589b8f2251714e3b..39e657e9c615999b87119db415b76be2278326b4 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/convert_dataset.ipynb
+++ b/docs/mindspore/programming_guide/source_zh_cn/convert_dataset.ipynb
@@ -204,6 +204,8 @@
     "\n",
     "接口说明：\n",
     "\n",
+    "- `FileWriter`：如果参数shard_num>1，那么在生成MindRecord文件时，会将原始数据集保存至shard_num个MindRecord文件中，每个MindRecord文件会保存相邻MindRecord文件的元数据信息。然后在使用`MindDataset`接口读取MindRecord数据集时，通过`MindDataset(dataset_files=\"./test.mindrecord0\")`可以读取全部shard_num个MindRecord文件；通过`MindDataset(dataset_files=[\"./test.mindrecord0\"])`只会读取`test.mindrecord0`文件中的数据。\n",
+    "\n",
     "- `write_raw_data`：将数据写入到内存之中。\n",
     "\n",
     "- `commit`：将最终内存中的数据写入到磁盘。"
@@ -424,7 +426,7 @@
    "source": [
     "file_name = './datasets/convert_dataset_to_mindrecord/data_to_mindrecord/test.mindrecord0'\n",
     "# create MindDataset for reading data\n",
-    "define_data_set = ds.MindDataset(dataset_file=file_name)\n",
+    "define_data_set = ds.MindDataset(dataset_files=file_name)\n",
     "# create a dictionary iterator and read a data record through the iterator\n",
     "count = 0\n",
     "for item in define_data_set.create_dict_iterator(output_numpy=True):\n",
@@ -471,4 +473,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/docs/mindspore/programming_guide/source_zh_cn/custom_debugging_info.md b/docs/mindspore/programming_guide/source_zh_cn/custom_debugging_info.md
index 121bc8666b5be4b85770de7a15ff2e55c0137e7c..2bbafa6c8634cee9ac25ac36aa9b237c4dabad5f 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/custom_debugging_info.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/custom_debugging_info.md
@@ -264,11 +264,11 @@ Tensor(shape=[2, 2], dtype=Int32, value=
 
 ### 同步Dump功能使用方法
 
-同步Dump功能使用参考[同步Dump操作步骤](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id6)。
+同步Dump功能使用参考[同步Dump操作步骤](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#同步dump)。
 
 ### 异步Dump功能使用方法
 
-异步Dump功能使用参考[异步Dump操作步骤](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id11)。
+异步Dump功能使用参考[异步Dump操作步骤](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#异步dump)。
 
 ## Running Data Recorder
 
@@ -365,9 +365,10 @@ MindSpore采用glog来输出日志，常用的几个环境变量如下：
     C++和Python的日志会被输出到不同的文件中，C++日志的文件名遵从`GLOG`日志文件的命名规则，这里是`mindspore.机器名.用户名.log.日志级别.时间戳.进程ID`，Python日志的文件名为`mindspore.log.进程ID`。  
     `GLOG_log_dir`只能包含大小写字母、数字、"-"、"_"、"/"等字符。
 
-- `GLOG_log_max`
+- `GLOG_stderrthreshold`
 
-    单个日志文件的默认最大为50MB，可以通过该环境变量更改日志文件默认的最大值。如果当前写入的日志文件超过最大值，则新输出的日志内容会写入到新的日志文件中。
+    日志模块在将日志输出到文件的同时也会将日志打印到屏幕，该环境变量用于控制此种场景下打印到屏幕的日志级别。
+    该环境变量默认值为2，即WARNING级别，对应关系如下：0-DEBUG、1-INFO、2-WARNING、3-ERROR、4-CRITICAL。
 
 - `MS_SUBMODULE_LOG_v`
 
@@ -376,11 +377,6 @@ MindSpore采用glog来输出日志，常用的几个环境变量如下：
     其中被指定子模块的日志级别将覆盖`GLOG_v`在此模块内的设置，此处子模块的日志级别`LogLevel`与`GLOG_v`的日志级别含义相同，MindSpore子模块的划分如下表。  
     例如可以通过`GLOG_v=1 MS_SUBMODULE_LOG_v="{PARSER:2,ANALYZER:2}"`把`PARSER`和`ANALYZER`模块的日志级别设为WARNING，其他模块的日志级别设为INFO。
 
-- `GLOG_stderrthreshold`
-
-    日志模块在将日志输出到文件的同时也会将日志打印到屏幕，该环境变量用于控制此种场景下打印到屏幕的日志级别。
-    该环境变量默认值为2，即WARNING级别，对应关系如下：0-DEBUG、1-INFO、2-WARNING、3-ERROR、4-CRITICAL。
-
 MindSpore子模块按照目录划分如下：
 
 | Source Files                                 | Sub Module Name |
@@ -410,4 +406,16 @@ MindSpore子模块按照目录划分如下：
 | mindspore/core/gvar                          | COMMON          |
 | mindspore/core/                              | CORE            |
 
+- `GLOG_log_max`
+
+    用于控制MindSpore C++模块日志单文件大小，默认最大为50MB，可以通过该环境变量更改日志文件默认的最大值。如果当前写入的日志文件超过最大值，则新输出的日志内容会写入到新的日志文件中。
+
+- `logger_maxBytes`
+
+    用于控制MindSpore Python模块日志单文件大小，默认是52428800 bytes。
+
+- `logger_backupCount`
+
+    用于控制MindSpore Python模块日志文件数量，默认是30个。
+
 > glog不支持日志文件的绕接，如果需要控制日志文件对磁盘空间的占用，可选用操作系统提供的日志文件管理工具，例如：Linux的logrotate。  
diff --git a/docs/mindspore/programming_guide/source_zh_cn/custom_operator_cpu.md b/docs/mindspore/programming_guide/source_zh_cn/custom_operator_cpu.md
index ec8a203ed1b32376fa5257c054f76722a151660b..7479d2991a6092068275da756e0db188370bfb0c 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/custom_operator_cpu.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/custom_operator_cpu.md
@@ -102,7 +102,7 @@ void TransposeCPUFwdKernel::InitKernel(const CNodePtr &kernel_node) {
 - `AnfRuntimeAlgorithm`类中的函数实现了各种对算子节点的操作，`shape_`表示算子第1个输入的shape，`axis_`表示算子的属性perm。
 - `Transpose`算子原语中参数“perm”作为输入传入，但是在解析时元组类型的“perm”实际被认为是算子的属性。
 
-> `AnfRuntimeAlgorithm`类的详细内容可参考MindSpore源码中[mindspore/ccsrc/backend/session/anf_runtime_algorithm.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/backend/session/anf_runtime_algorithm.h)下的声明。
+> `AnfRuntimeAlgorithm`类的详细内容可参考MindSpore源码中[mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/backend/common/session/anf_runtime_algorithm.h)下的声明。
 
 源文件中`Launch`函数的定义如下：首先依次获取每个输入输出的地址，然后根据`axis_`变换维度，把值赋给输出地址指向的空间。
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/cv_mobilenetv2_fine_tune.md b/docs/mindspore/programming_guide/source_zh_cn/cv_mobilenetv2_fine_tune.md
index 94bab9fd45e642316d2116e8ef27b18469dc7899..f046b7980096e9b3ada94d0a27c8eadd5fe2001b 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/cv_mobilenetv2_fine_tune.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/cv_mobilenetv2_fine_tune.md
@@ -256,7 +256,7 @@ sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT_PATH]
 
 ## 加载微调训练
 
-Windows系统上，MobileNetV2做微调训练时，只能运行`train.py`。Linux系统上，使用MobileNetV2做微调训练时，可以选择运行`run_train.sh`， 并在运行Shell脚本文件时传入[参数](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cv_mobilenetv2_fine_tune.html#id8)。
+Windows系统上，MobileNetV2做微调训练时，只能运行`train.py`。Linux系统上，使用MobileNetV2做微调训练时，可以选择运行`run_train.sh`， 并在运行Shell脚本文件时传入[参数](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cv_mobilenetv2_fine_tune.html#参数简介)。
 
 Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_train.sh`时，命令行结尾使用`&> <log_file_path>`将标准输出与错误输出写入log文件。微调成功开始训练，`./train/rank*/log*.log`中会持续写入每一个epoch的训练时间与Loss等信息。若未成功，上述log文件会写入失败报错信息。
 
@@ -386,7 +386,7 @@ Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_
 
 ### 验证模型
 
-使用验证集测试模型性能，需要输入必要[参数](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cv_mobilenetv2_fine_tune.html#id8)，`--platform`默认为“Ascend”，可自行设置为"CPU"或"GPU"。最终在交互式命令行中展示标准输出与错误输出，或者将其写入`eval.log`文件。
+使用验证集测试模型性能，需要输入必要[参数](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/cv_mobilenetv2_fine_tune.html#参数简介)，`--platform`默认为“Ascend”，可自行设置为"CPU"或"GPU"。最终在交互式命令行中展示标准输出与错误输出，或者将其写入`eval.log`文件。
 
 ```bash
 # Windows/Linux with Python
diff --git a/docs/mindspore/programming_guide/source_zh_cn/cv_resnet50_second_order_optimizer.md b/docs/mindspore/programming_guide/source_zh_cn/cv_resnet50_second_order_optimizer.md
index 06d9d7317025b5d454ddb8c89ec4fce6751a072b..f00359cb019e2ca802b9a7330003b232308aed5f 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/cv_resnet50_second_order_optimizer.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/cv_resnet50_second_order_optimizer.md
@@ -73,11 +73,11 @@ MindSpore开发团队在现有的自然梯度算法的基础上，对FIM矩阵
 
 #### Ascend 910
 
-Ascend 910 AI处理器的分布式环境变量配置参考[分布式并行训练 (Ascend)](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id4)。
+Ascend 910 AI处理器的分布式环境变量配置参考[分布式并行训练 (Ascend)](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#配置分布式环境变量)。
 
 #### GPU
 
-GPU的分布式环境配置参考[分布式并行训练 (GPU)](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#id4)。
+GPU的分布式环境配置参考[分布式并行训练 (GPU)](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#配置分布式环境)。
 
 ## 加载处理数据集
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/design/mindir.md b/docs/mindspore/programming_guide/source_zh_cn/design/mindir.md
index 52e01c82089f0dc3a896213e6a15e4ffe81462a7..e9d35f2f806bdf14fad442483dec2caba0b910c0 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/design/mindir.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/design/mindir.md
@@ -14,7 +14,7 @@ MindIR是一种基于图表示的函数式IR，其最核心的目的是服务于
 
 - ir后缀结尾的IR文件：一种比较直观易懂的以文本格式描述模型结构的文件，可以直接用文本编辑软件查看。
 - dat后缀结尾的IR文件：一种相对于ir后缀结尾的文件格式定义更为严谨的描述模型结构的文件，包含的内容更为丰富，可以直接用文本编辑软件查看。
-- dot后缀结尾的IR文件：描述了不同节点间的拓扑关系，可以用[graphviz](http://graphviz.org)将此文件作为输入生成图片，方便用户直观地查看模型结构。对于算子比较多的模型，推荐使用可视化组件[MindInsight](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id5)对计算图进行可视化。
+- dot后缀结尾的IR文件：描述了不同节点间的拓扑关系，可以用[graphviz](http://graphviz.org)将此文件作为输入生成图片，方便用户直观地查看模型结构。对于算子比较多的模型，推荐使用可视化组件[MindInsight](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#计算图可视化)对计算图进行可视化。
 
 ## 文法定义
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_inference.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_inference.md
index 1726bbffa09d7974e7e6786935649b4be640144c..ec993e494d50e49b8da55eba9496aecb0121be93 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_inference.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_inference.md
@@ -85,7 +85,7 @@ CheckPoint文件在训练过程中产生。CheckPoint具体用法可参考: [Che
 代码如下：
 `load_distributed_checkpoint(model, ckpt_file_list, predict_strategy)`
 
-`load_distributed_checkpoint`的具体用法可参考：[分布式推理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_910.html#id1)。
+`load_distributed_checkpoint`的具体用法可参考：[分布式推理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_910.html#概述)。
 
 最后，就可以导出在分布式推理场景下的MindIR文件。
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training.rst b/docs/mindspore/programming_guide/source_zh_cn/distributed_training.rst
index 12262539bac3a0f1ab9ba9f7def185b4ef5a4c7d..00c796d1f0c5e95a8f093ae68d4eba007e535430 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training.rst
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training.rst
@@ -14,7 +14,7 @@
 
   - `dynamic_programming`：动态规划策略搜索算法。能够搜索出代价模型刻画的最优策略，但在搜索巨大网络模型的并行策略时耗时较长。其代价模型是围绕Ascend 910芯片基于内存的计算开销和通信开销对训练时间建模。
   - `recursive_programming`：双递归策略搜索算法。对于巨大网络以及大规模多卡切分能够保证瞬间生成最优策略。其基于符号运算的代价模型可以自由适配不同的加速器集群。
-  - `sharding_propagation`：切分策略传播算法。由配置并行策略的算子向未配置的算子传播并行策略。在传播时，算法会尽量选取引发张量重排布通信最少的策略。关于算子的并行策略配置和张量重排布，可参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#id10)。
+  - `sharding_propagation`：切分策略传播算法。由配置并行策略的算子向未配置的算子传播并行策略。在传播时，算法会尽量选取引发张量重排布通信最少的策略。关于算子的并行策略配置和张量重排布，可参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#自动并行原理)。
 
 - `SEMI_AUTO_PARALLEL`：半自动并行模式，相较于自动并行，该模式需要用户对算子手动配置切分策略实现并行。
 - `HYBRID_PARALLEL`：在MindSpore中特指用户通过手动切分模型实现混合并行的场景。
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_ascend.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_ascend.md
index fedf0465b33a853a1801a3a1f33699f22ee12005..1511a3589e168d8ea219b356bd79f588161a67a1 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_ascend.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_ascend.md
@@ -35,7 +35,7 @@
 
 其中，`rank_table_16pcs.json`、`rank_table_8pcs.json`、`rank_table_2pcs.json`是配置当前多卡环境的组网信息文件。`resnet.py`、`resnet50_distributed_training.py`等文件是定义网络结构的脚本。`run.sh`、`run_cluster.sh`是执行脚本。
 
-此外在[定义网络](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id7)和[分布式训练模型参数保存和加载](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id18)小节中我们针对手动混合并行模式和半自动并行模式的使用做了特殊说明。
+此外在[定义网络](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#定义网络)和[分布式训练模型参数保存和加载](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#分布式训练模型参数保存和加载)小节中我们针对手动混合并行模式和半自动并行模式的使用做了特殊说明。
 
 ## 准备环节
 
@@ -181,7 +181,7 @@ def create_dataset(data_path, repeat_num=1, batch_size=32, rank_id=0, rank_size=
 
 手动混合并行模式在数据并行模式的基础上，对`parameter`增加了模型并行`layerwise_parallel`配置，包含此配置的`parameter`将以切片的形式保存并参与计算，在优化器计算时不会进行梯度累加。在该模式下，框架不会自动插入并行算子前后需要的计算和通信操作，为了保证计算逻辑的正确性，用户需要手动推导并写在网络结构中，适合对并行原理深入了解的用户使用。
 
-以下面的代码为例，将`self.weight`指定为模型并行配置，即`self.weight`和`MatMul`的输出在第二维`channel`上存在切分。这时再在第二维上进行`ReduceSum`得到的仅是单卡累加结果，还需要引入`AllReduce.Sum`通信操作对每卡的结果做加和。关于并行算子的推导原理可以参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#id10)。
+以下面的代码为例，将`self.weight`指定为模型并行配置，即`self.weight`和`MatMul`的输出在第二维`channel`上存在切分。这时再在第二维上进行`ReduceSum`得到的仅是单卡累加结果，还需要引入`AllReduce.Sum`通信操作对每卡的结果做加和。关于并行算子的推导原理可以参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#自动并行原理)。
 
 ```python
 from mindspore import Tensor
@@ -208,7 +208,7 @@ class HybridParallelNet(nn.Cell):
 
 ### 半自动并行模式
 
-半自动并行模式相较于自动并行模式需要用户手动配置并行策略进行调优。关于算子并行策略的定义可以参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#id10)。
+半自动并行模式相较于自动并行模式需要用户手动配置并行策略进行调优。关于算子并行策略的定义可以参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#自动并行原理)。
 
 以前述的`HybridParallelNet`为例，在半自动并行模式下的脚本代码如下，`MatMul`的切分策略为`((1, 1),(1, 2))`，指定`self.weight`在第二维度上被切分两份。
 
@@ -558,7 +558,7 @@ bash run_cluster.sh /path/dataset /path/rank_table.json 16 8
 
 ### 非下沉场景训练方式
 
-图模式下，用户可以通过设置环境变量[GRAPH_OP_RUN](https://www.mindspore.cn/docs/note/zh-CN/master/env_var_list.html)=1来指定以非下沉方式训练模型。该方式需要采用OpenMPI的mpirun进行分布式训练，并且需要设置环境变量HCCL_WHITELIST_DISABLE=1。除此之外，训练启动脚本和[GPU分布式训练](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#id7)一致。
+图模式下，用户可以通过设置环境变量[GRAPH_OP_RUN](https://www.mindspore.cn/docs/note/zh-CN/master/env_var_list.html)=1来指定以非下沉方式训练模型。该方式需要采用OpenMPI的mpirun进行分布式训练，并且需要设置环境变量HCCL_WHITELIST_DISABLE=1。除此之外，训练启动脚本和[GPU分布式训练](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#定义网络)一致。
 
 ## 分布式训练模型参数保存和加载
 
@@ -626,7 +626,7 @@ load_distributed_checkpoint(model.train_network, ckpt_file_list, layout_dict)
 model.train(2, dataset)
 ```
 
-> 分布式推理场景可以参考教程：[分布式推理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_910.html#id1)。
+> 分布式推理场景可以参考教程：[分布式推理](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/multi_platform_inference_ascend_910.html#概述)。
 
 ### 数据并行模式
 
@@ -646,7 +646,7 @@ context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, grad
 
 ### 半自动并行模式
 
-半自动并行模式（Semi Auto Parallel）下checkpoint使用方法，与自动并行模式（Auto Parallel）和数据并行模式（Data Parallel）的用法相同，不同之处在于网络的定义，半自动并行模式（Semi Auto Parallel）下网络模型的定义请参考本教程中定义网络部分的[半自动并行模式](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id9)。
+半自动并行模式（Semi Auto Parallel）下checkpoint使用方法，与自动并行模式（Auto Parallel）和数据并行模式（Data Parallel）的用法相同，不同之处在于网络的定义，半自动并行模式（Semi Auto Parallel）下网络模型的定义请参考本教程中定义网络部分的[半自动并行模式](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#半自动并行模式)。
 
 保存模型时，可以使用如下代码来实现：
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_dataset_slice.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_dataset_slice.md
index 5cd371efe4ff5f2e1a234b021fd9bf169419f79c..e8645aae2712855fadbe948744663c0318b96183 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_dataset_slice.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_dataset_slice.md
@@ -115,4 +115,4 @@ dataset = create_dataset(data_path, batch_size=batch_size, slice_h_num=slice_h_n
 
 ## 运行代码
 
-上述流程的数据，代码和执行过程，可以参考：<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id15>。差异点在于，将执行脚本更改为run_dataset_slice.sh。
+上述流程的数据，代码和执行过程，可以参考：<https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#单机多卡训练>。差异点在于，将执行脚本更改为run_dataset_slice.sh。
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_fault_recover.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_fault_recover.md
index 358a0909d21237338aef53232cf3193c241aadfb..3ebaf87cdfd363d8c3e8b067ec7c1e84e413d86f 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_fault_recover.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_fault_recover.md
@@ -7,7 +7,7 @@
 ## 概述
 
 在进行分布式训练时，遇到故障是非常普遍的，类似于单卡训练，可以通过加载训练过程中保存的权重信息继续进行训练。区别于纯数据并行训练，当应用了模型并行后，权重是进行了切分的，卡与卡之间保存的权重信息可能不一致。
-为了解决这个问题，一个方案是在保存权重checkpoint文件前，就将权重通过[AllGather](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ops.html#allgather) 算子进行汇聚，每张卡均存储一个完整的权重信息，这一个功能在[分布式训练模型参数保存和加载](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id18) 中已经介绍了。
+为了解决这个问题，一个方案是在保存权重checkpoint文件前，就将权重通过[AllGather](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ops.html#allgather) 算子进行汇聚，每张卡均存储一个完整的权重信息，这一个功能在[分布式训练模型参数保存和加载](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#分布式训练模型参数保存和加载) 中已经介绍了。
 但是，对于大模型来说，使用汇聚保存对各种资源的开销都过于巨大，因此，本文档介绍的是每张卡仅仅保存自身的权重信息的恢复方案。对于大模型来说，往往会同时应用上数据并行与模型并行，而数据并行的维度所划分的设备，它们持有的权重信息是完全一致的，这也为大模型提供了冗余的备份，本文档也将指出如何去获取这个冗余信息。
 关于并行策略与权重的切片划分的关系，可以进行如下映射。关于数据并行，模型并行的概念，请参考[分布式训练](https://mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_ascend.html#ascend) 、关于优化器并行，请参考[优化器并行](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_parallel_opt.html) 。
 
@@ -109,7 +109,7 @@ def recover_train():
 
 ## 运行代码
 
-首先，请参考分布式并行训练Transformer模型教程中的[准备环节](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_transformer.html#id7) 准备好数据集。
+首先，请参考分布式并行训练Transformer模型教程中的[准备环节](https://mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_transformer.html#准备环节) 准备好数据集。
 进入代码目录后，执行保存切片权重的训练脚本。
 
 ```bash
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_gpu.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_gpu.md
index 136a88e1d926bceef3594a7185c1b556b427ea32..ef47a3532cfd65209753d82b7a67adb4bb374187 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_gpu.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_gpu.md
@@ -45,12 +45,6 @@
 
   参考OpenMPI官网教程安装：<https://www.open-mpi.org/faq/?category=building#easy-build>。
 
-- `NCCL-2.7.6`：Nvidia集合通信库。
-
-  NCCL-2.7.6下载地址：<https://developer.nvidia.com/nccl/nccl-legacy-downloads>。
-
-  参考NCCL官网教程安装：<https://docs.nvidia.com/deeplearning/nccl/install-guide/index.html#debian>。
-
 - 主机间免密登陆（涉及多机训练时需要）。若训练涉及多机，则需要配置多机间免密登陆，可参考以下步骤进行配置：
     1. 每台主机确定同一个用户作为登陆用户（不推荐root）；
     2. 执行`ssh-keygen -t rsa -P ""`生成密钥；
@@ -409,7 +403,7 @@ pytest -s -v ./resnet50_distributed_training_gpu.py > train.log 2>&1 &
 
 ## 分布式训练模型参数保存与加载
 
-在GPU上进行分布式训练时，模型参数的保存和加载的方法与Ascend上一致，可参考[分布式训练模型参数保存和加载](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id18)
+在GPU上进行分布式训练时，模型参数的保存和加载的方法与Ascend上一致，可参考[分布式训练模型参数保存和加载](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#分布式训练模型参数保存和加载)
 。
 
 ## 不依赖OpenMPI进行训练
@@ -420,7 +414,7 @@ OpenMPI在分布式训练的场景中，起到在Host侧同步数据以及进程
 
 参考[Parameter Server模式](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_parameter_server_training.html)训练教程，将多个MindSpore训练进程作为`Worker`启动，并且额外启动一个`Scheduler`，对脚本做少量修改，即可执行**不依赖OpenMPI的分布式训练**。
 
-执行Worker脚本前需要导出环境变量，如[环境变量设置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_parameter_server_training.html#id5):
+执行Worker脚本前需要导出环境变量，如[环境变量设置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_parameter_server_training.html#环境变量设置):
 
 ```text
 export MS_SERVER_NUM=0                # Server number
@@ -461,7 +455,7 @@ if __name__ == "__main__":
 - 默认情况下，安全加密通道是关闭的，需要通过`set_ps_context`正确配置安全加密通道或者关闭安全加密通道后，才能调用init("nccl")，否则初始化组网会失败。
 
 若想使用安全加密通道，请设置`context.set_ps_context(config_file_path="/path/to/config_file.json", enable_ssl=True, client_password="123456", server_password="123456")`
-等配置，详细参数配置说明请参考Python API [mindspore.context.set_ps_context](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.context.html#mindspore.context.set_ps_context)，以及本文档[安全认证](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#id17)章节。
+等配置，详细参数配置说明请参考Python API [mindspore.context.set_ps_context](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.context.html#mindspore.context.set_ps_context)，以及本文档[安全认证](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#安全认证)章节。
 
 脚本内容`run_gpu_cluster.sh`如下，在启动Worker和Scheduler之前，需要添加相关环境变量设置：
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_mode.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_mode.md
index d3cde51433c7d094853c9dae60934285feab4f19..f45d1ed20454275a37c52022d89dc400c42f0921 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_mode.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_mode.md
@@ -17,7 +17,7 @@
 
 当前MindSpore提供分布式并行训练的功能。它支持了上述的多种模式，可以通过`context.set_auto_parallel_context()`接口设置对应的并行模式。
 
-在用户调用分布式训练流程时，需要调用如下代码进行通信的初始化，并且配置对应的rank_table_file，可以参考[分布式训练(Ascend)](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.3/distributed_training_ascend.html#id20)的**多机多卡训练**章节。
+在用户调用分布式训练流程时，需要调用如下代码进行通信的初始化，并且配置对应的rank_table_file，可以参考[分布式训练(Ascend)](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.3/distributed_training_ascend.html#多机多卡训练)的**多机多卡训练**章节。
 
 ```python
 from mindspore.communication import init, get_rank, get_group_size
@@ -81,7 +81,7 @@ model.train(*args, **kwargs)
 
 ### 半自动并行
 
-相较于自动并行，半自动并行模式需要用户对算子手动配置切分**策略**实现并行。关于算子并行策略的定义可以参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#id10)。
+相较于自动并行，半自动并行模式需要用户对算子手动配置切分**策略**实现并行。关于算子并行策略的定义可以参考这篇[设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/distributed_training_design.html#自动并行原理)。
 
 - 启动半自动和自动模式进行训练时，**必须**通过`model.train(*args, **kwargs)`接口进行训练，不支持自定义循环进行网络训练。
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_parallel_opt.md b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_parallel_opt.md
index 421435261c2c9490c8d0bc0c84e77f5d78209f8b..b5b0073652a49d6b6675e4adca531d5e1542187d 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/distributed_training_parallel_opt.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/distributed_training_parallel_opt.md
@@ -59,7 +59,7 @@ import numpy as np
 from mindspore import Parameter, Tensor
 param = Parameter(Tensor(np.ones((10, 2))), name='weight1', parallel_optimizer=True)
 
-# 或者通过下述的方式配置
+# Another way to set the parallel_optimizer attribute
 param2 = Parameter(Tensor(np.ones((10, 2))), name='weight2')
 param2.parallel_optimizer = False
 ```
@@ -68,56 +68,73 @@ param2.parallel_optimizer = False
 
 在设置参数优化器并行一节中，我们阐述了如何配置每个参数的优化器并行属性。在全/半自动模式下，每个参数都会产生一个对应的AllGather操作和ReduceScatter操作。这些通信算子是自动并行框架自动插入的。然而，随着参数量增多，对应的通信算子也会增多，通信操作产生的算子调度和启动都会产生更多的开销。因此，可以通过`cell`提供的`set_comm_fusion`方法，对每个`cell`内的参数对应的AllGather和ReduceScatter操作配置融合标记。
 
-`Transformer`接口提供了`lambda_func`参数来自定义每层的分组融合标记。如下代码所示，通过此接口将Transformer中每一层的参数配置了fusion值，融合的通信算子个数设置为2个。
+如下述的代码所示，针对实例化后的DenseLayer，调用`set_comm_fusion`方法，为每一层设置fusion值。
 
 ```python
 """Parallel Optimizer Fusion Example"""
 from mindspore.communication import init
+from mindspore import nn
 from mindspore import context, ParallelMode
-from mindspore.nn.transformer import Transformer, TransformerOpParallelConfig
-
 init()
 context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, enable_parallel_optimizer=True)
-def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers):
-
-    # 将transformer的融合层数设置为4个
-    gradient_aggregation_group = 4
-    dis = max(int((layers + offset) / gradient_aggregation_group), 1)
-    # 此处的network是一个cell，用户可以针对自己的网络层调用set_comm_fusion方法
-    network.set_comm_fusion(int((layer_id + offset) / dis) + 1)
-
-model_parallel_config = TransformerOpParallelConfig()
-net = Transformer(encoder_layers=1, decoder_layers=1,
-                  batch_size=4, src_seq_length=10,
-                  tgt_seq_length=10, hidden_size=24,
-                  num_heads=8, attention_dropout_rate=0.0,
-                  hidden_dropout_rate=0.0, lambda_func=set_parallel_configure_for_layer,
-                  ffn_hidden_size=24, parallel_config=model_parallel_config)
+
+class DenseLayer(nn.Cell):
+    """A base layer with two dense layer"""
+    def __init__(self):
+        super().__init__()
+        self.input_mapping = nn.Dense(10, 10)
+        self.output_mapping = nn.Dense(10, 10)
+    def construct(self, x):
+        x = self.input_mapping(x)
+        return self.output_mapping(x)
+
+class Net(nn.Cell):
+    """An network with many dense layers"""
+    def __init__(self):
+        super().__init__()
+        self.layer1 = DenseLayer()
+        self.layer2 = DenseLayer()
+        self.layer3 = DenseLayer()
+        self.layer1.set_comm_fusion(0)
+        self.layer2.set_comm_fusion(1)
+        self.layer3.set_comm_fusion(2)
+    def construct(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        return x
+
+net = Net()
 for item in net.trainable_params():
-    if 'dense1' in item.name:
-        print(f"The parameter {item.name}'s fusion id is {item.comm_fusion}")
+    print(f"The parameter {item.name}'s fusion id is {item.comm_fusion}")
 ```
 
 对应的输出如下，表示了每层特定dense的funsion值：
 
 ```text
-The parameter encoder.blocks.0.attention.dense1.weight's fusion id is 1
-The parameter encoder.blocks.0.attention.dense1.bias's fusion id is 1
-The parameter decoder.blocks.0.attention.dense1.weight's fusion id is 2
-The parameter decoder.blocks.0.attention.dense1.bias's fusion id is 2
-The parameter decoder.blocks.0.cross_attention.dense1.weight's fusion id is 2
-The parameter decoder.blocks.0.cross_attention.dense1.bias's fusion id is 2
+The parameter layer1.input_mapping.weight's fusion id is 0
+The parameter layer1.input_mapping.bias's fusion id is 0
+The parameter layer1.output_mapping.weight's fusion id is 0
+The parameter layer1.output_mapping.bias's fusion id is 0
+The parameter layer2.input_mapping.weight's fusion id is 1
+The parameter layer2.input_mapping.bias's fusion id is 1
+The parameter layer2.output_mapping.weight's fusion id is 1
+The parameter layer2.output_mapping.bias's fusion id is 1
+The parameter layer3.input_mapping.weight's fusion id is 2
+The parameter layer3.input_mapping.bias's fusion id is 2
+The parameter layer3.output_mapping.weight's fusion id is 2
+The parameter layer3.output_mapping.bias's fusion id is 2
 ```
 
 >在编译图的流程中，相同融合标记并且是相同的通信操作，会被融合成一个通信操作。从而减少通信操作的数量。对于融合标记为0的通信算子时，优化流程中不会对它们进行融合。
 
 开启优化器切分时，网络中每个参数都会产生一个相应的通信算子，然而频繁地调用通信算子将造成较多的算子启动消耗。将这些通信算子融合成一个通信算子，是最有效减少通信算子个数的办法。MindSpore提供了但这样会导致计算资源的浪费。例如，将所有的通信算子融合成一个算子后，在当前训练迭代中，NPU需要等待切分的参数汇聚完成后才能进行网络的前向计算。这样会造成设备的等待。
 
-为了避免上述问题，可以将网络参数进行分组融合：在上一组参数进行的计算的同时，进行下组参数的通信，使得计算和通信能够互相隐藏。这就是上述代码将通信算子个数融合成2个而不是1个的原因。
+为了避免上述问题，可以将网络参数进行分组融合：在上一组参数进行的计算的同时，进行下组参数的通信，使得计算和通信能够互相隐藏。这就是上述代码将`layer2`和`layer3`设置不同fusion值的原因。
 
 ## 运行代码
 
-上述代码需要在配置分布式变量后才可以运行。Ascend环境需要配置RANK_TABLE_FILE、RANK_ID和DEVICE_ID。配置的过程请参考[此处](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id4)，GPU环境需要配置[OpenMPI](https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_gpu.html?highlight=openmpi)、NCCL和[HOST_FILE](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#id14)，配置的过程请参考[此处](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#id3)。
+上述代码需要在配置分布式变量后才可以运行。Ascend环境需要配置RANK_TABLE_FILE、RANK_ID和DEVICE_ID。配置的过程请参考[此处](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#配置分布式环境变量)，GPU环境需要配置[OpenMPI](https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_gpu.html?highlight=openmpi)、NCCL和[HOST_FILE](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#多机多卡训练)，配置的过程请参考[此处](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_gpu.html#配置分布式环境)。
 
 Ascend分布式相关的环境变量有:
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/dump_in_graph_mode.md b/docs/mindspore/programming_guide/source_zh_cn/dump_in_graph_mode.md
index 677b5c3c3abab44772ab928041607b5f923f2f31..75f5ce516dfacb715c450e79f3872220bb57065b 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/dump_in_graph_mode.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/dump_in_graph_mode.md
@@ -49,7 +49,7 @@
 
 1. 静态图算子结果分析。
 
-   通过Dump功能获得的IR图，可以了解脚本代码与执行算子的映射关系（详情见[MindSpore IR简介](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/mindir.html#id1)）。结合执行算子的输入和输出数据，可以分析训练过程中可能存在的溢出、梯度爆炸与消失等问题，反向跟踪到脚本中可能存在问题的代码。
+   通过Dump功能获得的IR图，可以了解脚本代码与执行算子的映射关系（详情见[MindSpore IR简介](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/mindir.html#简介)）。结合执行算子的输入和输出数据，可以分析训练过程中可能存在的溢出、梯度爆炸与消失等问题，反向跟踪到脚本中可能存在问题的代码。
 
 2. 特征图分析。
 
@@ -104,7 +104,7 @@ MindSpore提供了同步Dump与异步Dump两种模式：
     - `iteration`：指定需要Dump数据的迭代。类型为str，用“|”分离要保存的不同区间的step的数据。如"0|5-8|100-120"表示Dump第1个，第6个到第9个， 第101个到第121个step的数据。指定“all”，表示Dump所有迭代的数据。
     - `saved_data`: 指定Dump的数据。类型为str，取值成"tensor"，表示Dump出完整张量数据；取值成"statistic"，表示只Dump张量的统计信息；取值"full"代表两种都要。同步Dump统计信息现只支持GPU场景，CPU或Ascend场景若选"statistic"或"full"便会错误退出。默认取值为"tensor"。
     - `input_output`：设置成0，表示Dump出算子的输入和算子的输出；设置成1，表示Dump出算子的输入；设置成2，表示Dump出算子的输出。
-    - `kernels`：算子的名称列表。开启IR保存开关`context.set_context(save_graphs=True)`并执行用例，从生成的IR文件`trace_code_graph_{graph_id}`中获取算子名称。详细说明可以参照教程：[如何保存IR](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/read_ir_files.html#id2)。
+    - `kernels`：算子的名称列表。开启IR保存开关`context.set_context(save_graphs=True)`并执行用例，从生成的IR文件`trace_code_graph_{graph_id}`中获取算子名称。详细说明可以参照教程：[如何保存IR](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/read_ir_files.html#如何保存ir)。
     - `support_device`：支持的设备，默认设置成0到7即可；在分布式训练场景下，需要dump个别设备上的数据，可以只在`support_device`中指定需要Dump的设备Id。该配置参数在CPU上无效，因为CPU下没有device这个概念，但是在json格式的配置文件中仍需保留该字段。
     - `enable`：设置成true，表示开启同步Dump；设置成false时，在Ascend上会使用异步Dump，在GPU上仍然使用同步Dump。
     - `trans_flag`：开启格式转换。将设备上的数据格式转换成NCHW格式。若为`True`，则数据会以Host侧的4D格式（NCHW）格式保存；若为`False`，则保留Device侧的数据格式。该配置参数在CPU上无效，因为CPU上没有format转换，但是在json格式的配置文件中仍需保留该字段。
@@ -406,9 +406,9 @@ numpy.load("Conv2D.Conv2D-op107.2.2.1623124369613540.output.0.DefaultFormat.npy"
     - `iteration`：指定需要Dump的迭代。类型为str，用“|”分离要保存的不同区间的step的数据。如"0|5-8|100-120"表示Dump第1个，第6个到第9个， 第101个到第121个step的数据。指定“all”，表示Dump所有迭代的数据。
     - `saved_data`: 指定Dump的数据。类型为str，取值成"tensor"，表示Dump出完整张量数据；取值成"statistic"，表示只Dump张量的统计信息；取值"full"代表两种都要。异步Dump统计信息只有在`file_format`设置为`npy`时可以成功，若在`file_format`设置为`bin`时选"statistic"或"full"便会错误退出。默认取值为"tensor"。
     - `input_output`：设置成0，表示Dump出算子的输入和算子的输出；设置成1，表示Dump出算子的输入；设置成2，表示Dump出算子的输出。
-    - `kernels`：算子的名称列表。开启IR保存开关`context.set_context(save_graphs=True)`并执行用例，从生成的`trace_code_graph_{graph_id}`IR文件中获取算子名称。`kernels`仅支持TBE算子、AiCPU算子、通信算子，若设置成通信算子的名称，将会Dump出通信算子的输入算子的数据。详细说明可以参照教程：[如何保存IR](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/read_ir_files.html#id2)。
+    - `kernels`：算子的名称列表。开启IR保存开关`context.set_context(save_graphs=True)`并执行用例，从生成的`trace_code_graph_{graph_id}`IR文件中获取算子名称。`kernels`仅支持TBE算子、AiCPU算子、通信算子，若设置成通信算子的名称，将会Dump出通信算子的输入算子的数据。详细说明可以参照教程：[如何保存IR](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/read_ir_files.html#如何保存ir)。
     - `support_device`：支持的设备，默认设置成0到7即可；在分布式训练场景下，需要dump个别设备上的数据，可以只在`support_device`中指定需要Dump的设备Id。
-    - `op_debug_mode`：该属性用于算子溢出调试，设置成0，表示不开启溢出；设置成1，表示开启AiCore溢出检测；设置成2，表示开启Atomic溢出检测；设置成3，表示开启全部溢出检测功能。在Dump数据的时候请设置成0，若设置成其他值，则只会Dump溢出算子的数据。
+    - `op_debug_mode`：预留字段，设置为0。
     - `file_format`: dump数据的文件类型，只支持`npy`和`bin`两种取值。设置成`npy`，则dump出的算子张量数据将为host侧格式的npy文件；设置成`bin`，则dump出的数据将为device侧格式的protobuf文件，需要借助转换工具进行处理，详细步骤请参考[异步Dump数据分析样例](#id15)。默认取值为`bin`。
 
 2. 设置数据Dump的环境变量。
@@ -441,7 +441,7 @@ numpy.load("Conv2D.Conv2D-op107.2.2.1623124369613540.output.0.DefaultFormat.npy"
 
 ### 异步Dump数据对象目录
 
-若配置文件中`file_format`值设置为`npy`，则数据对象目录参考[同步Dump数据对象目录](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id8) 。
+若配置文件中`file_format`值设置为`npy`，则数据对象目录参考[同步Dump数据对象目录](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#同步dump数据对象目录) 。
 
 若未配置`file_format`值或`file_format`值为`bin`，数据对象目录为以下结构。
 
@@ -488,7 +488,7 @@ numpy.load("Conv2D.Conv2D-op107.2.2.1623124369613540.output.0.DefaultFormat.npy"
 
 ### 异步Dump数据文件介绍
 
-若配置文件中`file_format`值设置为`npy`，则数据文件介绍参考[同步Dump数据文件介绍](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#id9) 。
+若配置文件中`file_format`值设置为`npy`，则数据文件介绍参考[同步Dump数据文件介绍](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/dump_in_graph_mode.html#同步dump数据文件介绍) 。
 
 若未配置`file_format`值或`file_format`值为`bin`，启动训练后，异步Dump生成的原始数据文件是protobuf格式的文件，需要用到海思Run包中自带的数据解析工具进行解析，详见[如何查看dump数据文件](https://support.huawei.com/enterprise/zh/doc/EDOC1100206690/640e796d) 。
 
diff --git a/docs/mindspore/programming_guide/source_zh_cn/forward_value_and_grad.md b/docs/mindspore/programming_guide/source_zh_cn/forward_value_and_grad.md
deleted file mode 100644
index 7909084775201229617320c545be2d34b2728e55..0000000000000000000000000000000000000000
--- a/docs/mindspore/programming_guide/source_zh_cn/forward_value_and_grad.md
+++ /dev/null
@@ -1,131 +0,0 @@
-# ForwardValueAndGrad
-
-`Ascend` `GPU` `CPU` `模型运行`
-
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/mindspore/programming_guide/source_zh_cn/forward_value_and_grad.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
-
-## 概述
-
-`ForwardValueAndGrad` 用来返回输入网络或者函数的正向计算结果和参数的反向梯度值。`get_all`, `get_by_list`, 和 `sens_param` 参数用来控制梯度的计算范围。详细说明可见 [mindspore API](https://www.mindspore.cn/docs/api/en/master/api_python/nn/mindspore.nn.ForwardValueAndGrad.html).
-
-下面是使用`ForwardValueAndGrad`的用例说明.
-
-## 一阶求导
-
-MindSpore可以用`mindspore.nn.ForwardValueAndGrad (network, weights=None, get_all=False, get_by_list=False, sens_param=False)`来计算正向结果和一阶导数。 当`get_all` 和`get_by_list` 都设置成 `False`，将会输出第一个入参的导数。 当 `get_all` 设置成 `True`，所有入参的导数将会被输出。当 `get_by_list` 设置成 `False`，权重的导数将不会被输出。当 `get_by_list` 被设置成 `True`，权重的导数将会被输出。`sens_param` 用来缩放网络的正向输出值，用而影响梯度的计算结果。因此，它的维度和网络的正向输出结果一致。下面将举例说明。
-
-### 对输入求导
-
-```python
-import numpy as np
-import mindspore.context as context
-import mindspore.nn as nn
-import mindspore.ops as ops
-from mindspore import Tensor
-from mindspore import ParameterTuple, Parameter
-from mindspore import dtype as mstype
-context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-class Net(nn.Cell):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.matmul = ops.MatMul()
-        self.z = Parameter(Tensor(np.array([1.0], np.float32)), name='z')
-    def construct(self, x, y):
-        x = x * self.z
-        out = self.matmul(x, y)
-        return out
-
-class ForwardValueAndGradWrtX(nn.Cell):
-    def __init__(self, net):
-        super(ForwardValueAndGradWrtX, self).__init__()
-        self.net = net
-        self.grad = nn.ForwardValueAndGrad(self.net)
-    def construct(self, x, y):
-        ret = self.grad(x, y)
-        return ret
-
-x = Tensor([[0.8, 0.6, 0.2], [1.8, 1.3, 1.1]], dtype=mstype.float32)
-y = Tensor([[0.11, 3.3, 1.1], [1.1, 0.2, 1.4], [1.1, 2.2, 0.3]], dtype=mstype.float32)
-output = ForwardValueAndGradWrtX(Net())(x, y)
-print(output)
-```
-
-输出结果是:
-
-```text
-(Tensor(shape=[2, 3], dtype=Float32, value=
-[[9.68000054e-01, 3.20000029e+00, 1.78000009e+00],
- [2.83800006e+00, 8.61999989e+00, 4.13000011e+00]]), Tensor(shape=[2, 3], dtype=Float32, value=
-[[4.50999975e+00, 2.70000005e+00, 3.60000014e+00],
- [4.50999975e+00, 2.70000005e+00, 3.60000014e+00]]))
-```
-
-如果需要同时输出 `x` 和 `y` 的梯度，可以在 `ForwardValueAndGradWrtX` 设置 `self.grad = nn.ForwardValueAndGrad(self.net, get_all=True)`。
-
-### 对权重求导
-
-如果需要输出权重的梯度，可以参照下面的例子:
-
-```python
-class ForwardValueAndGradWrtX(nn.Cell):
-    def __init__(self, net):
-        super(ForwardValueAndGradWrtX, self).__init__()
-        self.net = net
-        self.params = ParameterTuple(net.trainable_params())
-        self.grad = nn.ForwardValueAndGrad(self.net, weights=self.params, get_by_list=True)
-    def construct(self, x, y):
-        ret = self.grad(x, y)
-        return ret
-```
-
-```python
-output = ForwardValueAndGradWrtX(Net())(x, y)
-print(output)
-```
-
-输出结果是:
-
-```text
-(Tensor(shape=[2, 3], dtype=Float32, value=
-[[9.68000054e-01, 3.20000029e+00, 1.78000009e+00]
- [2.83800006e+00, 8.61999989e+00, 4.13000011e+00]]), (Tensor(shape=[1], dtype=Float32, value= [2.15359993e+01]),))
-```
-
-### 梯度值缩放
-
-你可以使用参数 `sens_param` 来控制梯度的缩放值。
-
-```python
-class ForwardValueAndGradWrtX(nn.Cell):
-    def __init__(self, net):
-        super(ForwardValueAndGradWrtX, self).__init__()
-        self.net = net
-        self.grad = nn.ForwardValueAndGrad(self.net, sens_param=True)
-        self.grad_wrt_output = Tensor([[0.1, 0.6, 0.2], [0.8, 1.3, 1.1]], dtype=mstype.float32)
-    def construct(self, x, y):
-        ret = self.grad(x, y, self.grad_wrt_output)
-        return ret
-```
-
-```python
-output = ForwardValueAndGradWrtX(Net())(x, y)
-print(output)
-```
-
-输出结果是:
-
-```text
-(Tensor(shape=[2, 3], dtype=Float32, value=
-[[9.68000054e-01, 3.20000029e+00, 1.78000009e+00],
- [2.83800006e+00, 8.61999989e+00, 4.13000011e+00]]), Tensor(shape=[2, 3], dtype=Float32, value=
-[[2.21099997e+00, 5.09999990e-01, 1.49000001e+00],
- [5.58799982e+00, 2.68000007e+00, 4.07000017e+00]]))
-```
-
-`self.grad_wrt_output` 可以统一写成以下的格式:
-
-```python
-self.grad_wrt_output = Tensor([[s1, s2, s3], [s4, s5, s6]])
-```
-
-原始的正向输出结果与参数 `self.grad_wrt_output` 对应的元素相乘，产生缩放之后的输出结果。
diff --git a/docs/mindspore/programming_guide/source_zh_cn/images/dot_to_png.png b/docs/mindspore/programming_guide/source_zh_cn/images/dot_to_png.png
new file mode 100644
index 0000000000000000000000000000000000000000..9689a1e575c9900fbde64b4af206cc48ebad6dbb
Binary files /dev/null and b/docs/mindspore/programming_guide/source_zh_cn/images/dot_to_png.png differ
diff --git a/docs/mindspore/programming_guide/source_zh_cn/load_dataset_gnn.ipynb b/docs/mindspore/programming_guide/source_zh_cn/load_dataset_gnn.ipynb
index 334ae6e52d7f8ffe7e3aa26adc00a673b0927d40..0c11d9109c1db91c543cc0cb6f9a107905a53133 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/load_dataset_gnn.ipynb
+++ b/docs/mindspore/programming_guide/source_zh_cn/load_dataset_gnn.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "MindSpore提供的`mindspore.dataset`模块可以帮助用户构建数据集对象，分批次地读取文本数据。同时，在各个数据集类中还内置了数据处理和数据分词算子，使得数据在训练过程中能够像经过pipeline管道的水一样源源不断地流向训练系统，提升数据训练效果。\n",
     "\n",
-    "此外，MindSpore还支持分布式场景数据加载，用户可以在加载数据集时指定分片数目，具体用法参见[数据并行模式加载数据集](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id6)。\n",
+    "此外，MindSpore还支持分布式场景数据加载，用户可以在加载数据集时指定分片数目，具体用法参见[数据并行模式加载数据集](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#数据并行模式加载数据集)。\n",
     "\n",
     "下面，本文将简要演示如何使用MindSpore加载和处理图数据。"
    ]
diff --git a/docs/mindspore/programming_guide/source_zh_cn/load_dataset_image.ipynb b/docs/mindspore/programming_guide/source_zh_cn/load_dataset_image.ipynb
index ba8ba94501cb845b562b0da831fb6a88339fa18b..3daa49d4d4097548430c9703a9b80657aa45c19b 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/load_dataset_image.ipynb
+++ b/docs/mindspore/programming_guide/source_zh_cn/load_dataset_image.ipynb
@@ -31,7 +31,7 @@
    "source": [
     "在计算机视觉任务中，图像数据往往因为容量限制难以直接全部读入内存。MindSpore提供的`mindspore.dataset`模块可以帮助用户构建数据集对象，分批次地读取图像数据。同时，在各个数据集类中还内置了数据处理和数据增强算子，使得数据在训练过程中能够像经过pipeline管道的水一样源源不断地流向训练系统，提升数据训练效果。\n",
     "\n",
-    "此外，MindSpore还支持分布式场景数据加载，用户可以在加载数据集时指定分片数目，具体用法参见[数据并行模式加载数据集](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id6)。\n",
+    "此外，MindSpore还支持分布式场景数据加载，用户可以在加载数据集时指定分片数目，具体用法参见[数据并行模式加载数据集](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#数据并行模式加载数据集)。\n",
     "\n",
     "本教程将以加载[MNIST训练数据集[1]](#参考文献)为例，演示如何使用MindSpore加载和处理图像数据。"
    ],
diff --git a/docs/mindspore/programming_guide/source_zh_cn/load_dataset_text.ipynb b/docs/mindspore/programming_guide/source_zh_cn/load_dataset_text.ipynb
index 053334f9393c1db37e53c2a7f754304a06b43fca..62a4d541514951a9670f0674bfdee5c9fb549b28 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/load_dataset_text.ipynb
+++ b/docs/mindspore/programming_guide/source_zh_cn/load_dataset_text.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "MindSpore提供的`mindspore.dataset`模块可以帮助用户构建数据集对象，分批次地读取文本数据。同时，在各个数据集类中还内置了数据处理和数据分词算子，使得数据在训练过程中能够像经过pipeline管道的水一样源源不断地流向训练系统，提升数据训练效果。\n",
     "\n",
-    "此外，MindSpore还支持分布式场景数据加载，用户可以在加载数据集时指定分片数目，具体用法参见[数据并行模式加载数据集](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#id6)。\n",
+    "此外，MindSpore还支持分布式场景数据加载，用户可以在加载数据集时指定分片数目，具体用法参见[数据并行模式加载数据集](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/distributed_training_ascend.html#数据并行模式加载数据集)。\n",
     "\n",
     "下面，本教程将简要演示如何使用MindSpore加载和处理文本数据。"
    ],
diff --git a/docs/mindspore/programming_guide/source_zh_cn/multi_platform_inference.md b/docs/mindspore/programming_guide/source_zh_cn/multi_platform_inference.md
index 10dba1a909d5a8894e23b1b361c43fc67d33d5a4..eeca9d298b45d8942ce5bf9f29b383c730f23905 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/multi_platform_inference.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/multi_platform_inference.md
@@ -173,4 +173,6 @@ MindSpore通过统一IR定义了网络的逻辑结构和算子的属性，将Min
   <td>YOLOv4</td>
   <td>YOLOv5</td>
 </tr>
-</table>
\ No newline at end of file
+</table>
+
+> 除了上述表格中的网络外，用户自定义网络中使用到的算子，如果能被[导出到MindIR模型文件](https://mindspore.cn/docs/programming_guide/zh-CN/master/save_model.html#导出mindir格式文件)中，也能使用该MindIR模型文件执行推理任务。
diff --git a/docs/mindspore/programming_guide/source_zh_cn/read_ir_files.md b/docs/mindspore/programming_guide/source_zh_cn/read_ir_files.md
index 653d588012e8741fc88c5e25b84ee9cbe6ca8751..891b65cc55fe1001a88fe53e6ddf00cb4c259ebc 100644
--- a/docs/mindspore/programming_guide/source_zh_cn/read_ir_files.md
+++ b/docs/mindspore/programming_guide/source_zh_cn/read_ir_files.md
@@ -6,11 +6,11 @@
 
 ## 概述
 
-在图模式`context.set_context(mode=context.GRAPH_MODE)`下运行用MindSpore编写的模型时，若配置中设置了`context.set_context(save_graphs=True)`，运行时会输出一些图编译过程中生成的一些中间文件，我们称为IR文件。当前主要有三种格式的IR文件：
+在图模式`context.set_context(mode=context.GRAPH_MODE)`下运行用MindSpore编写的模型时，若配置中设置了`context.set_context(save_graphs=True)`，运行时会输出一些图编译过程中生成的中间文件，我们称为IR文件。当前主要有三种格式的IR文件：
 
 - ir后缀结尾的IR文件：一种比较直观易懂的以文本格式描述模型结构的文件，可以直接用文本编辑软件查看。
 - dat后缀结尾的IR文件：一种相对于ir后缀结尾的文件格式定义更为严谨的描述模型结构的文件，包含的内容更为丰富，可以直接用文本编辑软件查看。
-- dot后缀结尾的IR文件：描述了不同节点间的拓扑关系，可以用[graphviz](http://graphviz.org)将此文件作为输入生成图片，方便用户直观地查看模型结构。对于算子比较多的模型，推荐使用可视化组件[MindInsight](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id5)对计算图进行可视化。
+- dot后缀结尾的IR文件：描述了不同节点间的拓扑关系，可以用[graphviz](http://graphviz.org)将此文件作为输入生成图片，方便用户直观地查看模型结构。对于算子比较多的模型，推荐使用可视化组件[MindInsight](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#计算图可视化)对计算图进行可视化。
 
 ## 如何保存IR
 
@@ -164,7 +164,7 @@ print(out)
 第8行告诉我们该网络解析出来的图的数量，该IR文件展示了三张图的信息。 分别为第42行的入口图`1_construct_wrapper.21`；第32行的图`3_func.23`，对应着网络中定义的函数`func(x, y)`；第12行的图`2_construct.22`，即对应`construct`函数。
 对于具体的图来说（此处我们以图`2_construct.22`为例），第10-28行展示了图结构的信息，图中含有若干个节点，即`CNode`。该图包含`Sub`、`Add`、`Mul`这些已经在`__init___`函数中定义过的算子。另外还有一处（第19行）以`call @3_func.23`的形式，调用了图`3_func.23`，对应脚本中调用函数`func`执行两数相除的行为。
 
-`CNode`（[ANF-IR的设计请查看](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/mindir.html#id2)）的信息遵循如下格式，从左到右分别为序号、节点名称-debug_name、算子名称-op_name、输入节点-arg、节点的属性-primitive_attrs、输入和输出的规格、源码解析调用栈等信息。
+`CNode`（[ANF-IR的设计请查看](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/mindir.html#文法定义)）的信息遵循如下格式，从左到右分别为序号、节点名称-debug_name、算子名称-op_name、输入节点-arg、节点的属性-primitive_attrs、输入和输出的规格、源码解析调用栈等信息。
 由于ANF图为单向无环图，所以此处仅根据输入关系来体现节点与节点的连接关系。关联代码行则体现了`CNode`与脚本源码之间的关系，例如第15行表明该节点是由脚本中`a = self.sub(x, 1)`这一行解析而来。
 
 ```text
@@ -257,13 +257,27 @@ print(out)
 第34-39表示图中计算节点的执行序，与代码执行的先后顺序对应。格式为：`序号: 所属图名称:节点名称{[0]: 第一个输入的信息, [1]: 第二个输入的信息, ...}`。 对于`CNode`而言，第一个输入表示该节点承载的计算方式。
 第58行表示图的数量，此处为3。
 
-`CNode`（[ANF-IR的设计请查看](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/mindir.html#id2)）的信息遵循如下格式，从左到右分别为序号、输出规格、算子名称-op_name、节点的属性-attr、输入节点-arg、输入节点的规格、所在的命名空间、关联代码行等信息。
+`CNode`（[ANF-IR的设计请查看](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/design/mindir.html#文法定义)）的信息遵循如下格式，从左到右分别为序号、输出规格、算子名称-op_name、节点的属性-attr、输入节点-arg、输入节点的规格、所在的命名空间、关联代码行等信息。
 
 ```text
 %[序号] : [输出规格] = [op_name]{[prim_type]}[attr0, attr1, ...](arg0, arg1, ...)    #(输入参数规格)#[命名空间]
   # 关联代码行/#debug_name
 ```
 
+### dot文件介绍
+
+可以用[graphviz](http://graphviz.org)将`dot`格式的IR文件作为输入生成图片。例如，在Linux操作系统下，可以通过以下命令转换成一张PNG图片。
+
+```shell
+dot -Tpng -o 04_abstract_specialize_0014.png 04_abstract_specialize_0014.dot
+```
+
+转换后的图片如下所示，我们可以直观地查看模型结构。不同的黑框区分了不同的子图，图与图之间的蓝色箭头表示相互之间的调用。蓝色区域表示参数，矩形表示图的参数列表，六边形和黑色箭头表示该参数作为CNode的输入参与计算过程。黄色矩形表示CNode节点，从图中可以看出，CNode输入从下标0开始，第0个输入（即紫色或绿色区域）表示该算子将要进行怎样的计算，通过虚箭头连接。类型一般为算子原语，也可以是另一张图。下标1之后的输入则为计算所需要的参数。
+
+![04_abstract_specialize_0014.png](./images/dot_to_png.png)
+
+对于算子比较多的模型，图片会过于庞大，推荐使用可视化组件[MindInsight](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#计算图可视化)对计算图进行可视化。
+
 ## 如何根据analyze_fail.dat文件分析图推导失败的原因
 
 MindSpore在编译图的过程中，经常会出现`abstract_specialize`阶段的图推导失败的报错，通常我们能根据报错信息以及analyze_fail.dat文件，来定位出脚本中存在的问题。
diff --git a/docs/notebook/enable_graph_kernel_fusion/mindspore_enable_graph_kernel_fusion.ipynb b/docs/notebook/enable_graph_kernel_fusion/mindspore_enable_graph_kernel_fusion.ipynb
index 485ce83cdc72ff85a7bb4afff0c482c3abc54e38..4711a731fef7c9d86ed6d7e7ceb51069fa100e6a 100644
--- a/docs/notebook/enable_graph_kernel_fusion/mindspore_enable_graph_kernel_fusion.ipynb
+++ b/docs/notebook/enable_graph_kernel_fusion/mindspore_enable_graph_kernel_fusion.ipynb
@@ -148,7 +148,7 @@
    "source": [
     "3. 查看计算图\n",
     "\n",
-    "    在当前工作目录下执行`mindinsight start --summary-base-dir ./log/enable_graph_kernel_fusion`，其中`./log/enable_graph_kernel_fusion`为保存的所有计算图的主目录。启动MindInsight可视化工具查看计算图，训练看板中`graph1`目录保存的为图算融合前的计算图，`graph2`目录保存的为图算融合后的计算图（参考[MindInsight计算图可视化教程](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id5)）。\n",
+    "    在当前工作目录下执行`mindinsight start --summary-base-dir ./log/enable_graph_kernel_fusion`，其中`./log/enable_graph_kernel_fusion`为保存的所有计算图的主目录。启动MindInsight可视化工具查看计算图，训练看板中`graph1`目录保存的为图算融合前的计算图，`graph2`目录保存的为图算融合后的计算图（参考[MindInsight计算图可视化教程](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#计算图可视化)）。\n",
     "\n",
     "    ![未开启图算融合](https://gitee.com/mindspore/docs/raw/master/docs/notebook/enable_graph_kernel_fusion/images/graph1.png)\n",
     "\n",
diff --git a/docs/notebook/mindinsight/mindinsight_dashboard.ipynb b/docs/notebook/mindinsight/mindinsight_dashboard.ipynb
index 291f96a23df729b60a3b1cd164966bda3c44cfbe..6746d1eac372ebdcd0cda8c5c393c53a8b496798 100644
--- a/docs/notebook/mindinsight/mindinsight_dashboard.ipynb
+++ b/docs/notebook/mindinsight/mindinsight_dashboard.ipynb
@@ -94,7 +94,7 @@
     "os.makedirs(test_path, exist_ok=True)\n",
     "os.makedirs(train_path, exist_ok=True)\n",
     "if not os.path.exists(os.path.join(test_path, \"test_batch.bin\")):\n",
-    "    shutil.move(\"./datasets/cifar10/test_batch.bin\", test_path)\n",
+    "    shutil.move(\"./datasets/cifar-10-batches-bin/test_batch.bin\", test_path)\n",
     "[shutil.move(\"./datasets/cifar-10-batches-bin/\"+i, train_path) for i in os.listdir(\"./datasets/cifar-10-batches-bin/\") if os.path.isfile(\"./datasets/cifar-10-batches-bin/\"+i) and not i.endswith(\".html\") and not os.path.exists(os.path.join(train_path, i))]"
    ]
   },
@@ -468,7 +468,7 @@
    "source": [
     "> 更多的Summary数据收集方式可以参考官方文档《[收集Summary数据](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html)》。\n",
     ">\n",
-    "> 收集Summary数据时，需将训练过程中的数据下层模式关闭，即`dataset_sink_mode=False`。更多注意事项可参考[官网文档](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html#id7)。\n",
+    "> 收集Summary数据时，需将训练过程中的数据下层模式关闭，即`dataset_sink_mode=False`。更多注意事项可参考[官网文档](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html#注意事项)。\n",
     "\n",
     "## 执行验证\n",
     "\n",
diff --git a/docs/notebook/mindspore_apply_quantization_aware_training.ipynb b/docs/notebook/mindspore_apply_quantization_aware_training.ipynb
index 327fae2e7b69005de61699b16635ed3215a6d1b3..95a94156e472e06aad039355beb4e030704797f1 100644
--- a/docs/notebook/mindspore_apply_quantization_aware_training.ipynb
+++ b/docs/notebook/mindspore_apply_quantization_aware_training.ipynb
@@ -287,7 +287,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> 除了自动量化网络外，还能[手动模式构建量化网络](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_quantization_aware_training.html#id10)，而且手动模式构建量化网络的方法由于引入了专门的量化参数[quant_config](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.compression.html#mindspore.compression.quant.create_quant_config)，可以更细粒度的调节模型量化程度，比如量化的类型，指定量化的通道等。并且使用了专门的量化计算节点，在构建网络时就已经插入了伪量化节点，可以不必使用`QuantizationAwareTraining.quantize`接口来进行量化。"
+    "> 除了自动量化网络外，还能[手动模式构建量化网络](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/apply_quantization_aware_training.html#手动构建量化网络)，而且手动模式构建量化网络的方法由于引入了专门的量化参数[quant_config](https://www.mindspore.cn/docs/api/zh-CN/master/api_python/mindspore.compression.html#mindspore.compression.quant.create_quant_config)，可以更细粒度的调节模型量化程度，比如量化的类型，指定量化的通道等。并且使用了专门的量化计算节点，在构建网络时就已经插入了伪量化节点，可以不必使用`QuantizationAwareTraining.quantize`接口来进行量化。"
    ]
   },
   {
diff --git a/docs/probability/api/requirements.txt b/docs/probability/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/probability/api/requirements.txt
+++ b/docs/probability/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/probability/docs/requirements.txt b/docs/probability/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/probability/docs/requirements.txt
+++ b/docs/probability/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/probability/docs/source_en/conf.py b/docs/probability/docs/source_en/conf.py
index 294fabf2ee60db08794a8133f6c18f231aa580aa..60557762c858f90d41b7b3dd51e65a407f204f66 100644
--- a/docs/probability/docs/source_en/conf.py
+++ b/docs/probability/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 import sphinx.ext.autosummary.generate as g
 from sphinx.ext import autodoc as sphinx_autodoc
@@ -128,20 +127,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/probability/docs/source_en/index.rst b/docs/probability/docs/source_en/index.rst
index 3efa66c4e26b75305a6139622b87d11ca9c06807..b85afff952528898e3e7c4cfcc95458ec422b65e 100644
--- a/docs/probability/docs/source_en/index.rst
+++ b/docs/probability/docs/source_en/index.rst
@@ -9,8 +9,9 @@ Probabilistic programming provides the following functions:
 - Combinable probabilistic programming modules for developers to use the logic of the deep learning model to build a deep probabilistic model
 - A toolbox for uncertainty estimation and anomaly detection to extend Bayesian applications
 
-.. image:: ./probability_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/probability/docs/source_en/probability_en.png" width="700px" alt="" >
 
 Typical MindSpore Probability Application Scenarios
 ----------------------------------------------------
diff --git a/docs/probability/docs/source_zh_cn/conf.py b/docs/probability/docs/source_zh_cn/conf.py
index 63ae61923add48c5d3c4f8ec0057f1568f66642c..77d7528c5552bb6bc9334a24b3daf4e0e4bf71b1 100644
--- a/docs/probability/docs/source_zh_cn/conf.py
+++ b/docs/probability/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 import sphinx.ext.autosummary.generate as g
 from sphinx.ext import autodoc as sphinx_autodoc
@@ -132,20 +131,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/probability/docs/source_zh_cn/index.rst b/docs/probability/docs/source_zh_cn/index.rst
index 120c1b105dd0cd27c720f46fa18cc5a0ba5bb76b..3be29a5590bad59fc66f5a8d5b998c1b7e4494d6 100644
--- a/docs/probability/docs/source_zh_cn/index.rst
+++ b/docs/probability/docs/source_zh_cn/index.rst
@@ -9,8 +9,9 @@ MindSpore Probability文档
 - 提供可组合的概率编程模块，让开发者可以用开发深度学习模型的逻辑来构造深度概率模型。
 - 提供不确定估计和异常检测的工具箱，拓展贝叶斯应用功能。
 
-.. image:: ./probability_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/probability/docs/source_zh_cn/probability_cn.png" width="700px" alt="" >
 
 使用概率编程的典型场景
 -----------------------
diff --git a/docs/reinforcement/api/requirements.txt b/docs/reinforcement/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/reinforcement/api/requirements.txt
+++ b/docs/reinforcement/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/reinforcement/docs/requirements.txt b/docs/reinforcement/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/reinforcement/docs/requirements.txt
+++ b/docs/reinforcement/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/reinforcement/docs/source_en/conf.py b/docs/reinforcement/docs/source_en/conf.py
index e1ecbf34480fcb2bba2c35a596e9d343173c3d59..c6a6b0bf533b5a0526fc0bfcc4264fd5e8949faa 100644
--- a/docs/reinforcement/docs/source_en/conf.py
+++ b/docs/reinforcement/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -108,20 +107,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/reinforcement/docs/source_en/index.rst b/docs/reinforcement/docs/source_en/index.rst
index 57eb636217ac6e6afb9a3572741f9855d802aff7..d9d4236fdc2d587086b14b60a12f627f81a27bf7 100644
--- a/docs/reinforcement/docs/source_en/index.rst
+++ b/docs/reinforcement/docs/source_en/index.rst
@@ -3,8 +3,9 @@ MindSpore Reinforcement Documents
 
 MindSpore Reinforcement is an open-source reinforcement learning framework that supports the distributed training of agents using reinforcement learning algorithms. MindSpore Reinforcement offers a clean API abstraction for writing reinforcement learning algorithms, which decouples the algorithm from deployment and execution considerations, including the use of accelerators, the level of parallelism and the distribution of computation across a cluster of workers. MindSpore Reinforcement translates the reinforcement learning algorithm into a series of compiled computational graphs, which are then run efficiently by the MindSpore framework on CPUs, GPUs, and Ascend AI processors.
 
-.. image:: ./reinforcement_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/reinforcement/docs/source_en/reinforcement_en.png" width="700px" alt="" >
 
 Unique Design Features
 -----------------------
diff --git a/docs/reinforcement/docs/source_zh_cn/conf.py b/docs/reinforcement/docs/source_zh_cn/conf.py
index 84a0ddb4e04d690f01a47882d48e3d1b7cee0c85..6c22edd8424b52c3bf4f72c1b1c6f18f118d6501 100644
--- a/docs/reinforcement/docs/source_zh_cn/conf.py
+++ b/docs/reinforcement/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -110,20 +109,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/reinforcement/docs/source_zh_cn/index.rst b/docs/reinforcement/docs/source_zh_cn/index.rst
index 7a25b8e92a51a6909a8f66a964aa8e3d1c95c149..31e9ee1b77118c92c5d50236f5cae77a8403bfa3 100644
--- a/docs/reinforcement/docs/source_zh_cn/index.rst
+++ b/docs/reinforcement/docs/source_zh_cn/index.rst
@@ -3,8 +3,9 @@ MindSpore Reinforcement 文档
 
 MindSpore Reinforcement是一个开源的强化学习框架，支持使用强化学习算法对agent进行分布式训练。MindSpore Reinforcement为编写强化学习算法提供了简洁的API抽象，它将算法与具体的部署和执行过程解耦，包括加速器的使用、并行度以及跨节点的计算调度。MindSpore Reinforcement将强化学习算法转换为一系列编译后的计算图，然后由MindSpore框架在CPU、GPU或AscendAI处理器上高效运行。
 
-.. image:: ./reinforcement_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/reinforcement/docs/source_zh_cn/reinforcement_cn.png" width="700px" alt="" >
 
 设计特点
 --------
diff --git a/docs/sample_code/dimension_reduce_training/train_boost_stage_2.py b/docs/sample_code/dimension_reduce_training/train_boost_stage_2.py
index f877feb308c1c4f91829a333c5e921cd236b36c7..5223754be17f8e5145c8d6918e88d0cfa93dd3eb 100644
--- a/docs/sample_code/dimension_reduce_training/train_boost_stage_2.py
+++ b/docs/sample_code/dimension_reduce_training/train_boost_stage_2.py
@@ -21,7 +21,6 @@ from mindspore.nn import SGD
 from mindspore import Model
 from mindspore import ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-from mindspore.train import FixedLossScaleManager
 from mindspore.communication import init
 from mindspore import set_seed
 from mindspore.parallel import set_algo_parameters
@@ -81,11 +80,10 @@ if __name__ == '__main__':
 
     # define loss
     loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=0.1, num_classes=1001)
-    loss_scale = FixedLossScaleManager(1024, drop_overflow_update=False)
 
     # define optimizer
     group_params = init_group_params(net)
-    opt = SGD(group_params, learning_rate=1, loss_scale=1024)
+    opt = SGD(group_params, learning_rate=1)
 
     # define metrics
     metrics = {"acc"}
@@ -112,8 +110,7 @@ if __name__ == '__main__':
     }
 
     # define model
-    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=metrics,
-                  amp_level="O2", boost_level="O1", keep_batchnorm_fp32=False, boost_config_dict=boost_dict)
+    model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, boost_level="O1", boost_config_dict=boost_dict)
 
     # define callback
     cb = [TimeMonitor(data_size=step_size), LossMonitor()]
diff --git a/docs/sample_code/distributed_optimizer_parallel/fusion_example.py b/docs/sample_code/distributed_optimizer_parallel/fusion_example.py
index 4dc60667c864af8da549491fbadf33f7a0d3cb9d..c1cd220341caf203aef4f81f76790c6908a895c1 100644
--- a/docs/sample_code/distributed_optimizer_parallel/fusion_example.py
+++ b/docs/sample_code/distributed_optimizer_parallel/fusion_example.py
@@ -1,25 +1,36 @@
 """Parallel Optimizer Fusion Example"""
 from mindspore.communication import init
+from mindspore import nn
 from mindspore import context, ParallelMode
-from mindspore.nn.transformer import Transformer, TransformerOpParallelConfig
-
 init()
 context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, enable_parallel_optimizer=True)
-def set_parallel_configure_for_layer(network, layer_id, offset, parallel_config, layers):
 
-    # 将transformer的融合层数设置为4个
-    gradient_aggregation_group = 4
-    dis = max(int((layers + offset) / gradient_aggregation_group), 1)
-    # 此处的network是一个cell，用户可以针对自己的网络层调用set_comm_fusion方法
-    network.set_comm_fusion(int((layer_id + offset) / dis) + 1)
+class DenseLayer(nn.Cell):
+    """A base layer with two dense layer"""
+    def __init__(self):
+        super().__init__()
+        self.input_mapping = nn.Dense(10, 10)
+        self.output_mapping = nn.Dense(10, 10)
+    def construct(self, x):
+        x = self.input_mapping(x)
+        return self.output_mapping(x)
+
+class Net(nn.Cell):
+    """An network with many dense layers"""
+    def __init__(self):
+        super().__init__()
+        self.layer1 = DenseLayer()
+        self.layer2 = DenseLayer()
+        self.layer3 = DenseLayer()
+        self.layer1.set_comm_fusion(0)
+        self.layer2.set_comm_fusion(1)
+        self.layer3.set_comm_fusion(2)
+    def construct(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        return x
 
-model_parallel_config = TransformerOpParallelConfig()
-net = Transformer(encoder_layers=1, decoder_layers=1,
-                  batch_size=4, src_seq_length=10,
-                  tgt_seq_length=10, hidden_size=24,
-                  num_heads=8, attention_dropout_rate=0.0,
-                  hidden_dropout_rate=0.0, lambda_func=set_parallel_configure_for_layer,
-                  ffn_hidden_size=24, parallel_config=model_parallel_config)
+net = Net()
 for item in net.trainable_params():
-    if 'dense1' in item.name:
-        print(f"The parameter {item.name}'s fusion id is {item.comm_fusion}")
+    print(f"The parameter {item.name}'s fusion id is {item.comm_fusion}")
diff --git a/docs/serving/api/requirements.txt b/docs/serving/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/serving/api/requirements.txt
+++ b/docs/serving/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/serving/docs/requirements.txt b/docs/serving/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/serving/docs/requirements.txt
+++ b/docs/serving/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/serving/docs/source_en/conf.py b/docs/serving/docs/source_en/conf.py
index 835f35ed90e7ba999c3a0dfcf4650741a2104e6c..61ce7297396ad468c36d78cd402e2aa307a7d79a 100644
--- a/docs/serving/docs/source_en/conf.py
+++ b/docs/serving/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -115,20 +114,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/serving/docs/source_en/index.rst b/docs/serving/docs/source_en/index.rst
index bb4721c1cdeb3a5925f5f8519b79b9ad9c8b1901..42b66ce3103c46b00bc9fc20b7d3b7efb2479fe2 100644
--- a/docs/serving/docs/source_en/index.rst
+++ b/docs/serving/docs/source_en/index.rst
@@ -11,8 +11,9 @@ MindSpore Serving provides the following functions:
 - The gRPC APIs and easy-to-use Python encapsulation APIs on the client
 - The RESTful APIs on the client
 
-.. image:: ./images/serving_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/serving/docs/source_en/images/serving_en.png" width="700px" alt="" >
 
 Typical MindSpore Serving Application Scenarios
 ------------------------------------------------
diff --git a/docs/serving/docs/source_zh_cn/conf.py b/docs/serving/docs/source_zh_cn/conf.py
index 29dc97adbd6f17f1073fab6ca36b6666725aa7df..2aa0ccb4cebb5fe07bbd5dfd4f5100458c66512b 100644
--- a/docs/serving/docs/source_zh_cn/conf.py
+++ b/docs/serving/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -115,20 +114,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
 import search_code
diff --git a/docs/serving/docs/source_zh_cn/faq.md b/docs/serving/docs/source_zh_cn/faq.md
index dafb3d332324be3a05b2523a496f5d55af0c7540..919bad9a3a3d988f26fce9db38dff7cc9eee724a 100644
--- a/docs/serving/docs/source_zh_cn/faq.md
+++ b/docs/serving/docs/source_zh_cn/faq.md
@@ -1,6 +1,6 @@
 # FAQ
 
-<a href="https://gitee.com/mindspore/docs/blob/master/docs/serving/faq/source_zh_cn/faq.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/serving/docs/source_zh_cn/faq.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
 
 <font size=3>**Q：MindSpore Serving是否支持热更新，避免推理服务中断？**</font>
 
@@ -38,7 +38,7 @@ A：首先，需要确认是否安装MindSpore Serving所依赖的MindSpore；
 
 <font size=3>**Q：如何控制Serving日志输出？**</font>
 
-A：MindSpore Serving采用glog来输出日志，详细可参考[日志相关的环境变量和配置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)，在此基础上，额外补充的内容：
+A：MindSpore Serving采用glog来输出日志，详细可参考[日志相关的环境变量和配置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)，在此基础上，额外补充的内容：
 
 - MS_SUBMODULE_LOG_v
 
diff --git a/docs/serving/docs/source_zh_cn/index.rst b/docs/serving/docs/source_zh_cn/index.rst
index d03032b97fbcb26c7070bb4398f01c358cd2fd89..88434f69fe3a3ab3155e0f43b9e95816e039070d 100644
--- a/docs/serving/docs/source_zh_cn/index.rst
+++ b/docs/serving/docs/source_zh_cn/index.rst
@@ -11,8 +11,9 @@ MindSpore Serving包含以下功能：
 - 支持客户端gRPC接口，提供简单易用的客户端Python封装接口。
 - 支持客户端RESTful接口。
 
-.. image:: ./images/serving_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/serving/docs/source_zh_cn/images/serving_cn.png" width="700px" alt="" >
 
 使用MindSpore Serving的典型场景
 --------------------------------
diff --git a/docs/serving/faq/requirements.txt b/docs/serving/faq/requirements.txt
index 96cdfc3e0c7ee0ae6a01e59c1081111fdc792bb6..9e0a48e898dd0ef56f2f0503a66694e919562f73 100644
--- a/docs/serving/faq/requirements.txt
+++ b/docs/serving/faq/requirements.txt
@@ -1,5 +1,6 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 jieba
\ No newline at end of file
diff --git a/docs/serving/faq/source_zh_cn/faq.md b/docs/serving/faq/source_zh_cn/faq.md
index dafb3d332324be3a05b2523a496f5d55af0c7540..0087e2136255657e7ad36279e9c04c140f9ab7c1 100644
--- a/docs/serving/faq/source_zh_cn/faq.md
+++ b/docs/serving/faq/source_zh_cn/faq.md
@@ -38,7 +38,7 @@ A：首先，需要确认是否安装MindSpore Serving所依赖的MindSpore；
 
 <font size=3>**Q：如何控制Serving日志输出？**</font>
 
-A：MindSpore Serving采用glog来输出日志，详细可参考[日志相关的环境变量和配置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#id11)，在此基础上，额外补充的内容：
+A：MindSpore Serving采用glog来输出日志，详细可参考[日志相关的环境变量和配置](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/custom_debugging_info.html#日志相关的环境变量和配置)，在此基础上，额外补充的内容：
 
 - MS_SUBMODULE_LOG_v
 
diff --git a/docs/xai/api/requirements.txt b/docs/xai/api/requirements.txt
index 2a02803179fc4f0092b3793f092fe9ffd05f6e4c..a7d57f0645d5b74878d294b407346a52e5ba3e19 100644
--- a/docs/xai/api/requirements.txt
+++ b/docs/xai/api/requirements.txt
@@ -1,3 +1,4 @@
 sphinx >= 2.2.1, <= 2.4.4
-sphinx_rtd_theme
+docutils == 0.16
+sphinx_rtd_theme == 0.5.2
 numpy
diff --git a/docs/xai/docs/requirements.txt b/docs/xai/docs/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/docs/xai/docs/requirements.txt
+++ b/docs/xai/docs/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/docs/xai/docs/source_en/conf.py b/docs/xai/docs/source_en/conf.py
index 73632e50dd772e1089e77f87a1ea14f1556be7c1..1603e8bf6b59f180623ecd95d7adf6c4d6c5d6ae 100644
--- a/docs/xai/docs/source_en/conf.py
+++ b/docs/xai/docs/source_en/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -115,20 +114,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/xai/docs/source_en/index.rst b/docs/xai/docs/source_en/index.rst
index 8a399de1f3700011e5be6f2725cf8357d2ff39df..7d5c566d68ba6b968ac3f992e54e3e8da7a29972 100644
--- a/docs/xai/docs/source_en/index.rst
+++ b/docs/xai/docs/source_en/index.rst
@@ -3,8 +3,9 @@ MindSpore XAI Documents
 
 MindSpore XAI is an explainable AI toolbox based on MindSpore. Currently, most deep learning models are black-box models with good performance but poor explainability. XAI aims to provide users decision explanations of the model, help users better understand the model, trust the model, and improve the model when an error occurs. Besides a variety of explanation methods, we also provide a set of evaluation methods to evaluate the explanation methods from various dimensions. It helps users compare and select the explanation methods that are most suitable for a particular scenario.
 
-.. image:: ./images/xai_en.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/xai/docs/source_en/images/xai_en.png" width="700px" alt="" >
 
 Typical MindSpore XAI Application Scenarios
 --------------------------------------------
diff --git a/docs/xai/docs/source_zh_cn/conf.py b/docs/xai/docs/source_zh_cn/conf.py
index 15e702126491298e844792346c6b25b902e7379d..57a03594fff4e117171e3a8b541fb77ceb7534a7 100644
--- a/docs/xai/docs/source_zh_cn/conf.py
+++ b/docs/xai/docs/source_zh_cn/conf.py
@@ -14,7 +14,6 @@ import os
 import sys
 import IPython
 import re
-import nbsphinx as nbs
 sys.path.append(os.path.abspath('../_ext'))
 from sphinx.ext import autodoc as sphinx_autodoc
 
@@ -115,20 +114,9 @@ with open(autodoc_source_path, "r+", encoding="utf8") as f:
     exec(get_param_func_str, sphinx_autodoc.__dict__)
     exec(code_str, sphinx_autodoc.__dict__)
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../../../resource/search'))
diff --git a/docs/xai/docs/source_zh_cn/index.rst b/docs/xai/docs/source_zh_cn/index.rst
index b82f4dfb5e43116e8f89f6f70a90e011b70a33e3..1a0a1ab60f49f953c28d2728da6541f0ac00ff27 100644
--- a/docs/xai/docs/source_zh_cn/index.rst
+++ b/docs/xai/docs/source_zh_cn/index.rst
@@ -3,8 +3,9 @@ MindSpore XAI 文档
 
 MindSpore XAI是一个基于昇思MindSpore的可解释AI工具箱。当前深度学习模型多为黑盒模型，性能表现好但可解释性较差。XAI旨在为用户提供对模型决策的解释，帮助用户更好地理解模型、信任模型，以及当模型出现错误时有针对性地改进模型。除了提供多种解释方法，还提供了一套对解释方法效果评分的度量方法，从多种维度评估解释方法的效果，从而帮助用户比较和选择最适合于特定场景的解释方法。
 
-.. image:: ./images/xai_cn.png
-  :width: 700px
+.. raw:: html
+
+   <img src="https://gitee.com/mindspore/docs/raw/master/docs/xai/docs/source_zh_cn/images/xai_cn.png" width="700px" alt="" >
 
 使用MindSpore XAI的典型场景
 ---------------------------
diff --git a/docs/xai/docs/source_zh_cn/using_benchmarks.md b/docs/xai/docs/source_zh_cn/using_benchmarks.md
index a9a16d3d32cdff2d46ad4e28200a7bb480c07494..9d8a4b57360e8bc1ad99b15925e77fd210f3b0d2 100644
--- a/docs/xai/docs/source_zh_cn/using_benchmarks.md
+++ b/docs/xai/docs/source_zh_cn/using_benchmarks.md
@@ -10,7 +10,7 @@
 
 以下教程参考了 [using_benchmarks.py](https://gitee.com/mindspore/xai/blob/master/examples/using_benchmarks.py) 。
 
-请参阅 [下载教程数据集及模型](https://www.mindspore.cn/xai/docs/zh-CN/master/using_explainers.html#id4) 以下载所有本教程所需的文件。
+请参阅 [下载教程数据集及模型](https://www.mindspore.cn/xai/docs/zh-CN/master/using_explainers.html#下载教程数据集及模型) 以下载所有本教程所需的文件。
 
 下载教程数据集及模型后，我们要加载一张样本图片，一个训练好的分类器，一个解释器和一张热力图(可选)：
 
diff --git a/docs/xai/docs/source_zh_cn/using_mindinsight.md b/docs/xai/docs/source_zh_cn/using_mindinsight.md
index 9a0ebc7507be36ff9e7780f06dd83a263c20cfbc..d2995a78d0ced37e3789232d3914bb64812418b2 100644
--- a/docs/xai/docs/source_zh_cn/using_mindinsight.md
+++ b/docs/xai/docs/source_zh_cn/using_mindinsight.md
@@ -27,7 +27,7 @@
 
 ### 下载教程数据集及模型
 
-请参阅 [下载教程数据集及模型](https://www.mindspore.cn/xai/docs/zh-CN/master/using_explainers.html#id4) 以下载所有本教程所需的文件。
+请参阅 [下载教程数据集及模型](https://www.mindspore.cn/xai/docs/zh-CN/master/using_explainers.html#下载教程数据集及模型) 以下载所有本教程所需的文件。
 
 ### 准备脚本
 
diff --git a/install/mindspore_ascend310_install_binary.md b/install/mindspore_ascend310_install_binary.md
index 259d615b455a23787a5b7e2796f6027be9d48f21..b7253162b6a78bd73c8457cfcf66ef459cf529cc 100644
--- a/install/mindspore_ascend310_install_binary.md
+++ b/install/mindspore_ascend310_install_binary.md
@@ -31,7 +31,7 @@
     - Python 3.7.5版本 64位，下载地址：[官网](https://www.python.org/ftp/python/3.7.5/Python-3.7.5.tgz)或[华为云](https://mirrors.huaweicloud.com/python/3.7.5/Python-3.7.5.tgz)。
     - Python 3.9.0版本 64位，下载地址：[官网](https://www.python.org/ftp/python/3.9.0/Python-3.9.0.tgz)或[华为云](https://mirrors.huaweicloud.com/python/3.9.0/Python-3.9.0.tgz)。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
     - 安装Ascend AI处理器配套软件包提供的whl包，whl包随配套软件包发布，升级配套软件包之后需要重新安装。
diff --git a/install/mindspore_ascend310_install_binary_en.md b/install/mindspore_ascend310_install_binary_en.md
index ad562c0294811a8d1b41e60501801547463a8e1d..38256373ba66b5199fafc2a9f61a698903f3f390 100644
--- a/install/mindspore_ascend310_install_binary_en.md
+++ b/install/mindspore_ascend310_install_binary_en.md
@@ -29,7 +29,7 @@ The following describes how to quickly install MindSpore by pip on Linux in the
     - Python 3.7.5 (64-bit): [Python official website](https://www.python.org/ftp/python/3.7.5/Python-3.7.5.tgz) or [HUAWEI CLOUD](https://mirrors.huaweicloud.com/python/3.7.5/Python-3.7.5.tgz).
     - Python 3.9.0 (64-bit): [Python official website](https://www.python.org/ftp/python/3.9.0/Python-3.9.0.tgz) or [HUAWEI CLOUD](https://mirrors.huaweicloud.com/python/3.9.0/Python-3.9.0.tgz).
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that you have permissions to access the installation path `/usr/local/Ascend` of the Ascend AI Processor software package. If not, ask the user root to add you to a user group to which `/usr/local/Ascend` belongs. For details about the configuration, see the description document in the software package.
     - Install the .whl package provided with the Ascend AI Processor software package. The .whl package is released with the software package. After the software package is upgraded, you need to reinstall the .whl package.
diff --git a/install/mindspore_ascend310_install_conda.md b/install/mindspore_ascend310_install_conda.md
index 11b5d4d53b8b5461fb95bf32d37a9ab5c08f016c..00ff95133d7a416c830db3d164f8dbed903d06a8 100644
--- a/install/mindspore_ascend310_install_conda.md
+++ b/install/mindspore_ascend310_install_conda.md
@@ -30,7 +30,7 @@
     - 如果您喜欢Conda提供的完整能力，可以选择下载[Anaconda3](https://repo.anaconda.com/archive/)。
     - 如果您需要节省磁盘空间，或者喜欢自定义安装Conda软件包，可以选择下载[Miniconda3](https://repo.anaconda.com/miniconda/)。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
 
diff --git a/install/mindspore_ascend310_install_conda_en.md b/install/mindspore_ascend310_install_conda_en.md
index 2b933ab3e315fc3c1c80b373d2e7c46259fe622a..183b5425beabcd4ba4d27284b4cb8fc53f0b724a 100644
--- a/install/mindspore_ascend310_install_conda_en.md
+++ b/install/mindspore_ascend310_install_conda_en.md
@@ -28,7 +28,7 @@ The following describes how to quickly install MindSpore by Conda on Linux in th
     - If you prefer the complete capabilities provided by Conda, you can choose to download [Anaconda3](https://repo.anaconda.com/archive/).
     - If you want to save disk space or prefer custom Conda installation, you can choose to download [Miniconda3](https://repo.anaconda.com/miniconda/).
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that you have permissions to access the installation path `/usr/local/Ascend` of the Ascend AI Processor software package. If not, ask the user root to add you to a user group to which `/usr/local/Ascend` belongs. For details about the configuration, see the description document in the software package.
 
diff --git a/install/mindspore_ascend310_install_pip.md b/install/mindspore_ascend310_install_pip.md
index a5f14868cf5125b7ecdc01712d50ed582e47b563..7773e59066534d587304c3a6d39b45ff951ca271 100644
--- a/install/mindspore_ascend310_install_pip.md
+++ b/install/mindspore_ascend310_install_pip.md
@@ -32,7 +32,7 @@
 
 - 如果您的环境为ARM架构，请确认当前使用的Python配套的pip版本>=19.3。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
     - 安装Ascend AI处理器配套软件包提供的whl包，whl包随配套软件包发布，升级配套软件包之后需要重新安装。
diff --git a/install/mindspore_ascend310_install_pip_en.md b/install/mindspore_ascend310_install_pip_en.md
index 2b1fd28d5bd6f0bb892e7d50676abc75f2d69b7e..4ec224f4c288635d0ada09be10a265b8748a51a1 100644
--- a/install/mindspore_ascend310_install_pip_en.md
+++ b/install/mindspore_ascend310_install_pip_en.md
@@ -31,7 +31,7 @@ The following describes how to quickly install MindSpore by pip on Linux in the
 
 - If you are using a ARM architecture system, please ensure that pip installed for current Python has a version >= 19.3.
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that you have permissions to access the installation path `/usr/local/Ascend` of the Ascend AI Processor software package. If not, ask the user root to add you to a user group to which `/usr/local/Ascend` belongs. For details about the configuration, see the description document in the software package.
     - Install the .whl package provided with the Ascend AI Processor software package. The .whl package is released with the software package. After the software package is upgraded, you need to reinstall the .whl package.
diff --git a/install/mindspore_ascend310_install_source.md b/install/mindspore_ascend310_install_source.md
index d6877c80b68e348cbb60c149ae4ebd8249c9f196..444f83cbd4c15c810c39863527ed5915104ba68b 100644
--- a/install/mindspore_ascend310_install_source.md
+++ b/install/mindspore_ascend310_install_source.md
@@ -37,7 +37,7 @@
 - 确认安装[patch 2.5及以上版本](https://ftp.gnu.org/gnu/patch/)。
     - 安装完成后将patch所在路径添加到系统环境变量中。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
     - 安装Ascend AI处理器配套软件包提供的whl包，whl包随配套软件包发布，升级配套软件包之后需要重新安装。
diff --git a/install/mindspore_ascend310_install_source_en.md b/install/mindspore_ascend310_install_source_en.md
index 400f27701205bffe3f8d151e90c3772ef50c014d..bfef6a89c4bb500dcb74225c6673eb941e58c872 100644
--- a/install/mindspore_ascend310_install_source_en.md
+++ b/install/mindspore_ascend310_install_source_en.md
@@ -37,7 +37,7 @@ The following describes how to quickly install MindSpore by compiling the source
 - Ensure that [patch 2.5 or later](https://ftp.gnu.org/gnu/patch/) is installed.
     - After installation, add the patch path to the system environment variables.
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that you have permissions to access the installation path `/usr/local/Ascend` of the Ascend AI Processor software package. If not, ask the user root to add you to a user group to which `/usr/local/Ascend` belongs. For details about the configuration, see the description document in the software package.
     - Install the .whl package provided with the Ascend AI Processor software package. The .whl package is released with the software package. After the software package is upgraded, you need to reinstall the .whl package.
diff --git a/install/mindspore_ascend_install_conda.md b/install/mindspore_ascend_install_conda.md
index f217923990b882faf2f51be24779c5529a4897c1..78bfa900a512cab10bdaa7299ca7ae45a3c5135e 100644
--- a/install/mindspore_ascend_install_conda.md
+++ b/install/mindspore_ascend_install_conda.md
@@ -33,7 +33,7 @@
     - 如果您喜欢Conda提供的完整能力，可以选择下载[Anaconda3](https://repo.anaconda.com/archive/)。
     - 如果您需要节省磁盘空间，或者喜欢自定义安装Conda软件包，可以选择下载[Miniconda3](https://repo.anaconda.com/miniconda/)。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
 
diff --git a/install/mindspore_ascend_install_conda_en.md b/install/mindspore_ascend_install_conda_en.md
index f005fa35350d731fb2be53a418f534b6c58c5d95..4831ba77c3d5d97176fe1b0ab10cac374f3b8ee6 100644
--- a/install/mindspore_ascend_install_conda_en.md
+++ b/install/mindspore_ascend_install_conda_en.md
@@ -30,7 +30,7 @@ This document describes how to quickly install MindSpore in a Linux system with
     - If you prefer the complete capabilities provided by Conda, you can choose to download [Anaconda3](https://repo.anaconda.com/archive/).
     - If you want to save disk space or prefer custom Conda installation, you can choose to download [Miniconda3](https://repo.anaconda.com/miniconda/).
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that the current user has the right to access the installation path `/usr/local/Ascend`of Ascend AI processor software package, If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
 
diff --git a/install/mindspore_ascend_install_docker.md b/install/mindspore_ascend_install_docker.md
index 80980649f38d23cbe4c4b64aebee2b10e04852cd..c1719ac5d23f90c0d9b1a94516d6ee9cb4b5e4b0 100644
--- a/install/mindspore_ascend_install_docker.md
+++ b/install/mindspore_ascend_install_docker.md
@@ -33,7 +33,7 @@ MindSpore的Ascend 910镜像托管在[Ascend Hub](https://ascend.huawei.com/asce
 
 - 确认安装[Docker 18.03或更高版本](https://docs.docker.com/get-docker/)。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
     - 在完成安装基础驱动与配套软件包的基础上，确认安装CANN软件包中的toolbox实用工具包，即Ascend-cann-toolbox-{version}.run，该工具包提供了Ascend NPU容器化支持的Ascend Docker runtime工具。
diff --git a/install/mindspore_ascend_install_docker_en.md b/install/mindspore_ascend_install_docker_en.md
index abdd19c5339ec11d4a09933a6e74bf5c653bff6f..60f06bec95750e8847743d9e7bb740f29631d0c0 100644
--- a/install/mindspore_ascend_install_docker_en.md
+++ b/install/mindspore_ascend_install_docker_en.md
@@ -33,7 +33,7 @@ The current support for containerized build options is as follows:
 
 - Ensure that [Docker 18.03 or later](https://docs.docker.com/get-docker/) is installed.
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that the current user has the right to access the installation path `/usr/local/Ascend`of Ascend AI processor software package. If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
     - After installing basic driver and corresponding software packages, ensure that the toolbox utility package in the CANN software package is installed, namely Ascend-cann-toolbox-{version}.run. The toolbox provides Ascend Docker runtime tools supported by Ascend NPU containerization.
diff --git a/install/mindspore_ascend_install_pip.md b/install/mindspore_ascend_install_pip.md
index 2f4851fc7d21a39a97c276cafd3ae7603990c71c..ff5c633ed8a930b59bc2cfc2bf87cdcb46d404ee 100644
--- a/install/mindspore_ascend_install_pip.md
+++ b/install/mindspore_ascend_install_pip.md
@@ -32,7 +32,7 @@
 
 - 如果您的环境为ARM架构，请确认当前使用的Python配套的pip版本>=19.3。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
     - 安装Ascend AI处理器配套软件包提供的whl包，whl包随配套软件包发布，参考如下命令完成安装。
diff --git a/install/mindspore_ascend_install_pip_en.md b/install/mindspore_ascend_install_pip_en.md
index 218a469eeb1479d859ac2ee7acea3df959086d6b..2357edd210da98c733750b6f0d11150b249b3a5f 100644
--- a/install/mindspore_ascend_install_pip_en.md
+++ b/install/mindspore_ascend_install_pip_en.md
@@ -32,7 +32,7 @@ This document describes how to quickly install MindSpore in a Linux system with
 
 - If you are using a ARM architecture system, please ensure that pip installed for current Python has a version >= 19.3.
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that the current user has the right to access the installation path `/usr/local/Ascend`of Ascend AI processor software package, If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
     - Install the .whl package provided in Ascend AI processor software package. The .whl package is released with the software package. After software package is upgraded, reinstall the .whl package.
diff --git a/install/mindspore_ascend_install_source.md b/install/mindspore_ascend_install_source.md
index 2fea447fafc2e8f4ca65026cafd2b22e00ef1d76..f996e1345e50c7672b5ab5b494a8ea223537e051 100644
--- a/install/mindspore_ascend_install_source.md
+++ b/install/mindspore_ascend_install_source.md
@@ -42,7 +42,7 @@
 
 - 确认安装[wheel 0.32.0及以上版本](https://pypi.org/project/wheel/)。
 
-- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.3），安装方式请参考[配套指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003)。
+- 确认安装Ascend AI处理器配套软件包（Ascend Data Center Solution 21.0.4），安装方式请参考[配套指南]。
 
     - 确认当前用户有权限访问Ascend AI处理器配套软件包的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组。
     - 安装Ascend AI处理器配套软件包提供的whl包，whl包随配套软件包发布，参考如下命令完成安装。
diff --git a/install/mindspore_ascend_install_source_en.md b/install/mindspore_ascend_install_source_en.md
index 886a8f7a4f0f6be106dfaf968c7ec861075ed5fd..4947848e41d4b0e8f7f1b3ec126c1dbb0ffc3714 100644
--- a/install/mindspore_ascend_install_source_en.md
+++ b/install/mindspore_ascend_install_source_en.md
@@ -42,7 +42,7 @@ This document describes how to quickly install MindSpore in a Linux system with
 
 - Ensure that [wheel 0.32.0 or later](https://pypi.org/project/wheel/) is installed.
 
-- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.3) are installed, please refer to the [Installation Guide](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003).
+- Ensure that the Ascend AI processor software package (Ascend Data Center Solution 21.0.4) are installed, please refer to the [Installation Guide].
 
     - Ensure that the current user has the right to access the installation path `/usr/local/Ascend`of Ascend AI processor software package, If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
     - Install the .whl package provided in Ascend AI processor software package. The .whl package is released with the software package. After software package is upgraded, reinstall the .whl package.
diff --git a/install/mindspore_cpu_mac_install_conda.md b/install/mindspore_cpu_mac_install_conda.md
new file mode 100644
index 0000000000000000000000000000000000000000..3879b5fd316a91334f0165403efc13f3c2c9de0e
--- /dev/null
+++ b/install/mindspore_cpu_mac_install_conda.md
@@ -0,0 +1,93 @@
+# Conda方式安装MindSpore CPU版本 (macOS)
+
+<!-- TOC -->
+
+- [Conda方式安装MindSpore CPU版本 (macOS)](#conda方式安装mindspore-cpu版本-macos)
+    - [确认系统环境信息](#确认系统环境信息)
+    - [创建并进入Conda虚拟环境](#创建并进入conda虚拟环境)
+    - [安装MindSpore](#安装mindspore)
+    - [验证是否成功安装](#验证是否成功安装)
+    - [升级MindSpore版本](#升级mindspore版本)
+
+<!-- /TOC -->
+
+<a href="https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_conda.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source.png"></a>
+
+[Conda](https://docs.conda.io/en/latest/)是一个开源跨平台语言无关的包管理与环境管理系统，允许用户方便地安装不同版本的二进制软件包与该计算平台需要的所有库。
+
+本文档介绍如何在macOS系统上，使用Conda方式快速安装MindSpore。
+
+## 确认系统环境信息
+
+- 根据下表中的系统及芯片情况确定合适的Python与Conda版本，macOS版本及芯片信息可点击桌面左上角苹果标志->`关于本机`获悉：
+
+    |芯片|计算架构|macOS版本|支持Python版本|支持Conda版本|
+    |-|-|-|-|-|
+    |M1|ARM|11.3|Python 3.9.1+（M1当前不支持3.7, 3.9支持最低版本为3.9.1）|Mambaforge 或 Miniforge|
+    |Intel|x86_64|10.15/11.3|Python 3.7.5/Python 3.9.0|Anaconda 或 MiniConda|
+
+- 确认安装与当前系统及芯片型号兼容的Conda版本。
+
+    - 如果您喜欢Conda提供的完整能力，可以选择下载[Anaconda3](https://repo.anaconda.com/archive/)或[Mambaforge](https://github.com/conda-forge/miniforge)。
+    - 如果您需要节省磁盘空间，或者喜欢自定义安装Conda软件包，可以选择下载[Miniconda3](https://repo.anaconda.com/miniconda/)或[Miniforge](https://github.com/conda-forge/miniforge)。
+
+## 创建并进入Conda虚拟环境
+
+根据您希望使用的Python版本创建对应的Conda虚拟环境并进入虚拟环境。
+
+- 如果您希望使用Python3.7.5版本(适配64-bit macOS 10.15)：
+
+  ```bash
+  conda create -n mindspore_py37 -c conda-forge python=3.7.5
+  conda activate mindspore_py37
+  ```
+
+- 如果您希望使用Python3.9.0版本(适配64-bit macOS 10.15)：
+
+  ```bash
+  conda create -n mindspore_py39 -c conda-forge python=3.9.0
+  conda activate mindspore_py39
+  ```
+
+- 如果您希望使用Python3.9.1版本(适配64-bit macOS 11.3)：
+
+  ```bash
+  conda create -n mindspore_py39 -c conda-forge python=3.9.1
+  conda activate mindspore_py39
+  ```
+
+## 安装MindSpore
+
+确认您处于Conda虚拟环境中，并执行如下命令安装MindSpore。
+
+```bash
+conda install mindspore-cpu={version} -c mindspore -c conda-forge
+```
+
+其中：
+
+- 在联网状态下，安装Conda安装包时会自动下载MindSpore安装包的依赖项（依赖项详情参见[setup.py](https://gitee.com/mindspore/mindspore/blob/master/setup.py)中的required_package），其余情况需自行安装。运行模型时，需要根据[ModelZoo](https://gitee.com/mindspore/models/tree/master/)中不同模型指定的requirements.txt安装额外依赖，常见依赖可以参考[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt)。
+- `{version}`表示MindSpore版本号，例如安装1.5.0-rc1版本MindSpore时，`{version}`应写为1.5.0rc1。
+
+## 验证是否成功安装
+
+```bash
+python -c "import mindspore;mindspore.run_check()"
+```
+
+如果输出：
+
+```text
+MindSpore version: 版本号
+The result of multiplication calculation is correct, MindSpore has been installed successfully!
+```
+
+说明MindSpore安装成功了。
+
+## 升级MindSpore版本
+
+当需要升级MindSpore版本时，可执行如下命令：
+
+```bash
+conda update mindspore-cpu -c mindspore -c conda-forge
+```
diff --git a/install/mindspore_cpu_mac_install_conda_en.md b/install/mindspore_cpu_mac_install_conda_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..83da6cd026cc7ecd0682efdf43f1eacb3df78101
--- /dev/null
+++ b/install/mindspore_cpu_mac_install_conda_en.md
@@ -0,0 +1,93 @@
+# Installing MindSpore CPU by Conda (macOS)
+
+<!-- TOC -->
+
+- [Installing MindSpore CPU by Conda (macOS)](#installing-mindspore-cpu-by-conda-macos)
+    - [System Environment Information Confirmation](#system-environment-information-confirmation)
+    - [Creating and Accessing the Conda Virtual Environment](#creating-and-accessing-the-conda-virtual-environment)
+    - [Installing MindSpore](#installing-mindspore)
+    - [Installation Verification](#installation-verification)
+    - [Version Update](#version-update)
+
+<!-- /TOC -->
+
+<a href="https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_conda_en.md" target="_blank"><img src="https://gitee.com/mindspore/docs/raw/master/resource/_static/logo_source_en.png"></a>
+
+[Conda](https://docs.conda.io/en/latest/) is an open-source, cross-platform, language-agnostic package manager and environment management system. It allows users to easily install different versions of binary software packages and any required libraries appropriate for their computing platform.
+
+This document describes how to quickly install MindSpore by Conda in a macOS system.
+
+## System Environment Information Confirmation
+
+- According to your Macbook configuration(click `About This Mac` to get chip/arch info), choose the right Python and Conda version based on following table:
+
+    |Chip|Architecture|macOS Version|Supported Python Version|Supported Conda Version|
+    |-|-|-|-|-|
+    |M1|ARM|11.3|Python 3.9.1+(3.7.x is not supported with M1, 3.9.1 is the least supported version)|Mambaforge or Miniforge|
+    |Intel|x86_64|10.15/11.3|Python 3.7.5/Python 3.9.0|Anaconda or Miniconda|
+
+- Ensure that the Conda version is compatible with the current system and chip.
+
+    - If you prefer the complete capabilities provided by Conda. You may download [Anaconda3](https://repo.anaconda.com/archive/) or [Mambaforge](https://github.com/conda-forge/miniforge).
+    - If you want to save disk space or prefer custom Conda installation. You may download [Miniconda3](https://repo.anaconda.com/miniconda/) or [Miniforge](https://github.com/conda-forge/miniforge).
+
+## Creating and Accessing the Conda Virtual Environment
+
+Create a Conda virtual environment based on the Python version you want to use and go to the virtual environment.
+
+- If you want to use Python 3.7.5 (for 64-bit macOS 10.15):
+
+  ```bash
+  conda create -n mindspore_py37 -c conda-forge python=3.7.5
+  conda activate mindspore_py37
+  ```
+
+- If you want to use Python 3.9.0 (for 64-bit macOS 10.15):
+
+  ```bash
+  conda create -n mindspore_py39 -c conda-forge python=3.9.0
+  conda activate mindspore_py39
+  ```
+
+- If you want to use Python 3.9.1 (for 64-bit macOS 11.3):
+
+  ```bash
+  conda create -n mindspore_py39 -c conda-forge python=3.9.1
+  conda activate mindspore_py39
+  ```
+
+## Installing MindSpore
+
+Ensure that you are in the Conda virtual environment and run the following command to install MindSpore:
+
+```bash
+conda install mindspore-cpu={version} -c mindspore -c conda-forge
+```
+
+In the preceding information:
+
+- When the network is connected, dependency items are automatically downloaded during .whl package installation. (For details about the dependency, see required_package in [setup.py](https://gitee.com/mindspore/mindspore/blob/master/setup.py) .) In other cases, you need to install it by yourself. When running models, you need to install additional dependencies based on requirements.txt specified for different models in [ModelZoo](https://gitee.com/mindspore/models/tree/master/). For details about common dependencies, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt).
+- `{version}` denotes the version of MindSpore. For example, when you are installing MindSpore 1.5.0-rc1, `{version}` should be 1.5.0rc1.
+
+## Installation Verification
+
+```bash
+python -c "import mindspore;mindspore.run_check()"
+```
+
+The outputs should be the same as:
+
+```text
+MindSpore version: __version__
+The result of multiplication calculation is correct, MindSpore has been installed successfully!
+```
+
+It means MindSpore has been installed successfully.
+
+## Version Update
+
+Using the following command if you need to update the MindSpore version:
+
+```bash
+conda update mindspore-cpu -c mindspore -c conda-forge
+```
diff --git a/install/mindspore_cpu_mac_install_pip.md b/install/mindspore_cpu_mac_install_pip.md
index 29f6a451d96ba2b0c00a1aefca4efc4aa1d33cb1..7b174bd4ea09244338d0a7d34209c2428e4d84a2 100644
--- a/install/mindspore_cpu_mac_install_pip.md
+++ b/install/mindspore_cpu_mac_install_pip.md
@@ -16,28 +16,35 @@
 
 ## 确认系统环境信息
 
-- 确认macOS版本在10.15和11.3之间，其中M1芯片当前只支持11.3。
+- 根据下表中的系统及芯片情况确定合适的Python版本，macOS版本及芯片信息可点击桌面左上角苹果标志->`关于本机`获悉：
 
-- 确认安装Python 3.7或Python 3.9版本。如果未安装或者已安装其他版本的Python，可以选择下载并安装：
-    - Python 3.7.5 (64-bit macOS 10.15)：[Python官网](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) or [华为云](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg)。
-    - Python 3.9.0 (64-bit macOS 10.15)：[Python官网](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) or [华为云](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg)。
-    - Python 3.9.1 (64-bit macOS 11.3)：[Python官网](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) or [华为云](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg)。
+    |芯片|计算架构|macOS版本|支持Python版本|
+    |-|-|-|-|
+    |M1|ARM|11.3|Python 3.9.1+（M1当前不支持3.7, 3.9支持最低版本为3.9.1）|
+    |Intel|x86_64|10.15/11.3|Python 3.7.5/Python 3.9.0|
+
+- 确认安装对应的Python版本。如果未安装或者已安装其他版本的Python，可以选择下载并安装：
+
+    - Python 3.7.5 (64-bit)：[官网](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) 或者 [华为云](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg)。
+    - Python 3.9.0 (64-bit)：[官网](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) 或者 [华为云](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg)。
+    - Python 3.9.1 (64-bit)：[官网](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) 或者 [华为云](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg)。
 
 ## 安装MindSpore
 
 参考[版本列表](https://www.mindspore.cn/versions)先进行SHA-256完整性校验，校验一致后再执行如下命令安装MindSpore。
 
 ```bash
-pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindSpore/cpu/{arch}/mindspore-{version}-{python_version}-macosx_{platform_version}_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindSpore/cpu/{arch}/mindspore-{version}-{python_version}-macosx_{platform_version}_{platform_arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 其中：
 
 - 在联网状态下，安装whl包时会自动下载mindspore安装包的依赖项（依赖项详情参见[setup.py](https://gitee.com/mindspore/mindspore/blob/master/setup.py)中的required_package），其余情况需自行安装。运行模型时，需要根据[ModelZoo](https://gitee.com/mindspore/models/tree/master/)中不同模型指定的requirements.txt安装额外依赖，常见依赖可以参考[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt)。
-- `{version}`表示MindSpore版本号，例如安装1.6.0版本MindSpore时，`{version}`应写为1.6.0，而安装1.6.0-rc1版本时，第一个`{version}`代表下载路径应写为1.6.0-rc1，第二个`{version}`代表版本号应写为1.6.0rc1。
+- `{version}`表示MindSpore版本号，例如安装1.5.0版本MindSpore时，`{version}`应写为1.5.0，而安装1.5.0-rc1版本时，第一个`{version}`代表下载路径应写为1.5.0-rc1，第二个`{version}`代表版本号应写为1.5.0rc1。
+- `{arch}`表示架构，例如使用的macOS系统是x86架构64位时，`{arch}`应写为`x86_64`。如果是M1芯片，则写为`aarch64`。
 - `{python_version}`表示用户的Python版本，Python版本为3.7.5时，`{python_version}`应写为`cp37-cp37m`。Python版本为3.9.0时，则写为`cp39-cp39`。
-- `{platform_version}`表示系统版本，例如系统版本为macOS 10.15时，`{platform_version}`应写为`10_15`。
-- `{arch}`表示系统架构，例如使用的macOS系统是x86架构64位时，`{arch}`应写为`x86_64`。如果系统是ARM架构64位，则写为`arm64`。
+- `{platform_version}`表示系统版本，如果是M1芯片，`{platform_version}`应写为`11_0`，否则`{platform_version}`应写为`10_15`。
+- `{platform_arch}`表示系统架构，例如使用的macOS系统是x86架构64位时，`{platform_arch}`应写为`x86_64`。如果是M1芯片，则写为`arm64`。
 
 ## 验证是否成功安装
 
@@ -64,4 +71,4 @@ pip install --upgrade mindspore=={version}
 
 其中：
 
-- 如果升级到正式版本，`=={version}`字段可以缺省。
+- 升级到rc版本时，需要手动指定`{version}`为rc版本号，例如1.5.0rc1；如果升级到正式版本，`=={version}`字段可以缺省。
diff --git a/install/mindspore_cpu_mac_install_pip_en.md b/install/mindspore_cpu_mac_install_pip_en.md
index c647e8d7937f85182f7be5c2d7e2269af19cbf98..172c59743aca082354f442d7de1742e324c32132 100644
--- a/install/mindspore_cpu_mac_install_pip_en.md
+++ b/install/mindspore_cpu_mac_install_pip_en.md
@@ -16,29 +16,35 @@ This document describes how to quickly install MindSpore on macOS by pip.
 
 ## System Environment Information Confirmation
 
-- Ensure macOS version between 10.15 and 11.3, for M1 chip currently only support 11.3.
+- According to your Macbook configuration(click "About This Mac" to get chip/arch info),choose the right Python version based on following table:
 
-- Ensure that Python 3.7 or Python 3.9 is installed. If not installed, download and install Python from:
+    |Chip|Architecture|macOS Version|Supported Python Version|
+    |-|-|-|-|
+    |M1|ARM|11.3|Python 3.9.1+(3.7.x is not supported with M1, 3.9.1 is the least supported version)|
+    |Intel|x86_64|10.15/11.3|Python 3.7.5/Python 3.9.0|
 
-    - Python 3.7.5 (64-bit for macOS 10.15)：[Python official website](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg).
-    - Python 3.9.0 (64-bit for macOS 10.15)：[Python official website](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg).
-    - Python 3.9.1 (64-bit for macOS 11.3)：[Python official website](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) or [HUAWEI CLOUD](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg).
+- Ensure that right Python version is installed. If not installed, download and install Python from:
+
+    - Python 3.7.5 (64-bit)：[Python official website](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg).
+    - Python 3.9.0 (64-bit)：[Python official website](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg).
+    - Python 3.9.1 (64-bit)：[Python official website](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) or [HUAWEI CLOUD](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg).
 
 ## Installing MindSpore
 
 It is recommended to refer to [Version List](https://www.mindspore.cn/versions/en) to perform SHA-256 integrity verification, and then execute the following command to install MindSpore after the verification is consistent.
 
 ```bash
-pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindSpore/cpu/{arch}/mindspore-{version}-{python_version}-macosx_{platform_version}_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindSpore/cpu/{arch}/mindspore-{version}-{python_version}-macosx_{platform_version}_{platform_arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 Of which,
 
 - When the network is connected, dependency items are automatically downloaded during .whl package installation. (For details about the dependency, see required_package in [setup.py](https://gitee.com/mindspore/mindspore/blob/master/setup.py) .) In other cases, you need to install it by yourself. When running models, you need to install additional dependencies based on requirements.txt specified for different models in [ModelZoo](https://gitee.com/mindspore/models/tree/master/). For details about common dependencies, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt).
-- `{version}` specifies the MindSpore version number. For example, when installing MindSpore 1.6.0, set `{version}` to 1.6.0, when installing MindSpore 1.6.0-rc1, the first `{version}` which represents download path should be written as 1.6.0-rc1, and the second `{version}` which represents file name should be 1.6.0rc1.
+- `{version}` specifies the MindSpore version number. For example, when installing MindSpore 1.5.0, set `{version}` to 1.5.0, when installing MindSpore 1.5.0-rc1, the first `{version}` which represents download path should be written as 1.5.0-rc1, and the second `{version}` which represents file name should be 1.5.0rc1.
+- `{arch}` denotes the architecture. For example, the macOS you are using is x86 architecture 64-bit, `{arch}` should be `x86_64`. If you use M1 chip, then it should be `aarch64`.
 - `{python_version}` spcecifies the python version for which MindSpore is built. If you wish to use Python3.7.5,`{python_version}` should be `cp37-cp37m`. If Python3.9.0 is used, it should be `cp39-cp39`.
-- `{platform_version}` specifies the macOS version number. For example, when installing macOS 10.15, set `{platform_version}` to `10_15`.
-- `{arch}` denotes the system architecture. For example, the macOS you are using is x86 architecture 64-bit, `{arch}` should be `x86_64`. If the system is ARM architecture 64-bit, then it should be `arm64`.
+- `{platform_version}` specifies the macOS version number. If you use M1 chip, set `{platform_version}` to `11_0`, otherwise set `{platform_version}` to `10_15`.
+- `{platform_arch}` denotes the system architecture. For example, the macOS you are using is x86 architecture 64-bit, `{platform_arch}` should be `x86_64`. If you use M1 chip, then it should be `arm64`.
 
 ## Installation Verification
 
@@ -65,4 +71,4 @@ pip install --upgrade mindspore=={version}
 
 Of which,
 
-- When updating to a standard release, `=={version}` could be removed.
+- When updating to a release candidate (rc) version, `{version}` should be specified, e.g. 1.5.0rc1; When updating to a standard release, `=={version}` could be removed.
diff --git a/install/mindspore_cpu_mac_install_source.md b/install/mindspore_cpu_mac_install_source.md
index 85e07a5dcc824a1b322ef6102c6e8b193dc4f132..539846bf3dac5695d5e3dfa53e8e04b77d1b65bb 100644
--- a/install/mindspore_cpu_mac_install_source.md
+++ b/install/mindspore_cpu_mac_install_source.md
@@ -8,6 +8,7 @@
     - [编译MindSpore](#编译mindspore)
     - [安装MindSpore](#安装mindspore)
     - [验证安装是否成功](#验证安装是否成功)
+    - [升级MindSpore版本](#升级mindspore版本)
 
 <!-- /TOC -->
 
@@ -17,23 +18,28 @@
 
 ## 确认系统环境信息
 
-- 确认macOS版本在10.15和11.3之间，其中M1芯片当前只支持11.3。
+- 根据下表中的系统及芯片情况确定合适的Python版本，macOS版本及芯片信息可点击桌面左上角苹果标志->`关于本机`获悉：
 
-- 确认安装[Xcode](https://xcodereleases.com/) (>=12.4 and <= 13.0) ，12.4(X86)及13.0(M1) 已测试。
+    |芯片|计算架构|macOS版本|支持Python版本|
+    |-|-|-|-|
+    |M1|ARM|11.3|Python 3.9.1+（M1当前不支持3.7, 3.9支持最低版本为3.9.1）|
+    |Intel|x86_64|10.15/11.3|Python 3.7.5/Python 3.9.0|
 
-- 确认安装`Command Line Tools for Xcode`。如果未安装，使用命令`sudo xcode-select --install`安装Command Line Tools。
+- 确认安装对应的Python版本。如果未安装或者已安装其他版本的Python，可以选择下载并安装：
 
-- 确认安装Python 3.7或Python 3.9版本。如果未安装或者已安装其他版本的Python，可以选择下载并安装：
+    - Python 3.7.5 (64-bit)：[官网](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) 或者 [华为云](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg)。
+    - Python 3.9.0 (64-bit)：[官网](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) 或者 [华为云](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg)。
+    - Python 3.9.1 (64-bit)：[官网](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) 或者 [华为云](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg)。
 
-    - Python 3.7.5 (64-bit macOS 10.15)：[Python官网](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) or [华为云](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg)。
-    - Python 3.9.0 (64-bit macOS 10.15)：[Python官网](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) or [华为云](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg)。
-    - Python 3.9.1 (64-bit macOS 11.3)：[Python官网](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) or [华为云](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg)。
+- 确认安装[Xcode](https://xcodereleases.com/) (>=12.4 and <= 13.0) ，12.4(X86)及13.0(M1) 已测试。
+
+- 确认安装`Command Line Tools for Xcode`。如果未安装，使用命令`sudo xcode-select --install`安装Command Line Tools。
 
-- 确认安装[CMake 3.18.3及以上版本](https://cmake.org/download/) . 如果没有安装，可以使用`brew install cmake`进行安装。
+- 确认安装[CMake 3.18.3及以上版本](https://cmake.org/download/)。如果没有安装，可以使用`brew install cmake`进行安装。
 
-- 确认安装[patch 2.5](https://ftp.gnu.org/gnu/patch/) . 如果没有安装，可以使用`brew install patch`进行安装。
+- 确认安装[patch 2.5](https://ftp.gnu.org/gnu/patch/)。如果没有安装，可以使用`brew install patch`进行安装。
 
-- 确认安装[wheel 0.32.0及以上版本](https://pypi.org/project/wheel/). 如果没有安装，可以使用`pip install wheel` 进行安装。
+- 确认安装[wheel 0.32.0及以上版本](https://pypi.org/project/wheel/)。如果没有安装，可以使用`pip install wheel` 进行安装。
 
 ## 从代码仓下载源码
 
@@ -93,5 +99,5 @@ The result of multiplication calculation is correct, MindSpore has been installe
     在源码根目录下执行编译脚本`build.sh`成功后，在`output`目录下找到编译生成的whl安装包，然后执行命令进行升级。
 
     ```bash
-    pip install --upgrade mindspore-*.whl
+    pip install --upgrade mindspore-{version}-{python_version}-macosx_{platform_version}_{arch}.whl
     ```
\ No newline at end of file
diff --git a/install/mindspore_cpu_mac_install_source_en.md b/install/mindspore_cpu_mac_install_source_en.md
index 9df838f278deef91b35550e84fd35149993481b5..88ec059ef6fa3754afea16992b809f8b6a8978e5 100644
--- a/install/mindspore_cpu_mac_install_source_en.md
+++ b/install/mindspore_cpu_mac_install_source_en.md
@@ -8,6 +8,7 @@
     - [Compiling MindSpore](#compiling-mindspore)
     - [Installing MindSpore](#installing-mindspore)
     - [Installation Verification](#installation-verification)
+    - [Version Update](#version-update)
 
 <!-- /TOC -->
 
@@ -17,17 +18,22 @@ This document describes how to quickly install MindSpore by compiling source cod
 
 ## System Environment Information Confirmation
 
-- Ensure macOS version between 10.15 and 11.3, for M1 chip currently only support 11.3.
+- According to your Macbook configuration(click `About This Mac` to get chip/arch info),choose the right Python version based on following table:
 
-- Ensure [Xcode](https://xcodereleases.com/) (>=12.4 and <= 13.0) is installed, where 12.4(X86) and 13.0(m1) are verified.
+    |Chip|Architecture|macOS Version|Supported Python Version|
+    |-|-|-|-|
+    |M1|ARM|11.3|Python 3.9.1+(3.7.x is not supported with M1, 3.9.1 is the least supported version)|
+    |Intel|x86_64|10.15/11.3|Python 3.7.5/Python 3.9.0|
 
-- Ensure Command Line Tools for Xcode is installed. If not, use `sudo xcode-select --install` command to install it.
+- Ensure that right Python version is installed. If not installed, download and install Python from:
 
-- Ensure that Python 3.7 or 3.9 is installed. If not installed, download and install Python from:
+    - Python 3.7.5 (64-bit)：[Python official website](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg).
+    - Python 3.9.0 (64-bit)：[Python official website](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg).
+    - Python 3.9.1 (64-bit)：[Python official website](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) or [HUAWEI CLOUD](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg).
 
-    - Python 3.7.5 (64-bit for macOS 10.15)：[Python official website](https://www.python.org/ftp/python/3.7.5/python-3.7.5-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.7.5/python-3.7.5-macosx10.9.pkg).
-    - Python 3.9.0 (64-bit for macOS 10.15)：[Python official website](https://www.python.org/ftp/python/3.9.0/python-3.9.0-macosx10.9.pkg) or [HUAWEI CLOUD](https://repo.huaweicloud.com/python/3.9.0/python-3.9.0-macosx10.9.pkg).
-    - Python 3.9.1 (64-bit for macOS 11.3)：[Python official website](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg) or [HUAWEI CLOUD](https://www.python.org/ftp/python/3.9.1/python-3.9.1-macos11.0.pkg).
+- Ensure that [Xcode](https://xcodereleases.com/) (>=12.4 and <= 13.0) is installed, where 12.4(X86) and 13.0(m1) are verified.
+
+- Ensure that `Command Line Tools` for Xcode is installed. If not, use `sudo xcode-select --install` command to install it.
 
 - Ensure that [CMake](https://cmake.org/download/) > `3.18.3` is installed. Use `brew install cmake` if it's not installed.
 
diff --git a/install/mindspore_gpu_install_conda.md b/install/mindspore_gpu_install_conda.md
index 47bec9695a2416cccc1df4759ae84038bd398767..98bf1b44d3d64b5543c4de306f277c7368083466 100644
--- a/install/mindspore_gpu_install_conda.md
+++ b/install/mindspore_gpu_install_conda.md
@@ -27,7 +27,6 @@
 - 确认安装[OpenMPI 4.0.3版本](https://www.open-mpi.org/faq/?category=building#easy-build)（可选，单机多卡/多机多卡训练需要）。
 - 确认安装[OpenSSL 1.1.1及以上版本](https://github.com/openssl/openssl.git)。
     - 安装完成后设置环境变量`export OPENSSL_ROOT_DIR=“OpenSSL安装目录”`。
-- 确认安装配套CUDA 10.1的[NCCL 2.7.6版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)或者配套CUDA 11.1[NCCL 2.7.8版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)（可选，单机多卡/多机多卡训练需要）。
 - 确认安装[TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download)（可选，Serving推理需要）。
 - 确认安装[gmp 6.1.2版本](https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz)。
 - 确认安装与当前系统兼容的Conda版本。
diff --git a/install/mindspore_gpu_install_conda_en.md b/install/mindspore_gpu_install_conda_en.md
index ba4991c7ae09e29a91f47242649b78e85d099cbd..c3259f1d635fbc977f2429130c779eab1e1c87de 100644
--- a/install/mindspore_gpu_install_conda_en.md
+++ b/install/mindspore_gpu_install_conda_en.md
@@ -27,7 +27,6 @@ For details about how to install third-party dependency software when confirming
 - Ensure that [OpenMPI 4.0.3](https://www.open-mpi.org/faq/?category=building#easy-build) is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
 - Ensure that [OpenSSL 1.1.1 or later](https://github.com/openssl/openssl.git) is installed.
     - Ensure that [OpenSSL](https://github.com/openssl/openssl) is installed and set system variable `export OPENSSL_ROOT_DIR="OpenSSL installation directory"`.
-- Ensure that [NCCL 2.7.6](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 10.1 or [NCCL 2.7.8](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 11.1 is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
 - Ensure that [TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download) is installed. (optional, required for Serving inference).
 - Ensure that [gmp 6.1.2](https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz) is installed.
 - Ensure that the Conda version is compatible with the current system.
diff --git a/install/mindspore_gpu_install_nightly.md b/install/mindspore_gpu_install_nightly.md
index b56893cd5943d0c6c566bdf561a3f062a1d77825..7d615e3bfb75811a54a63ecafe80a9174605f9db 100644
--- a/install/mindspore_gpu_install_nightly.md
+++ b/install/mindspore_gpu_install_nightly.md
@@ -27,7 +27,6 @@ MindSpore Nightly是包含当前最新功能与bugfix的预览版本，但是可
 - 确认安装[OpenMPI 4.0.3版本](https://www.open-mpi.org/faq/?category=building#easy-build)（可选，单机多卡/多机多卡训练需要）。
 - 确认安装[OpenSSL 1.1.1及以上版本](https://github.com/openssl/openssl.git)。
     - 安装完成后设置环境变量`export OPENSSL_ROOT_DIR=“OpenSSL安装目录”`。
-- 确认安装配套CUDA 11.1[NCCL 2.7.8版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)（可选，单机多卡/多机多卡训练需要）。
 - 确认安装[TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download)（可选，Serving推理需要）。
 - 确认安装[gmp 6.1.2版本](https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz)。
 - 确认安装Python 3.7.5或3.9.0版本。如果未安装或者已安装其他版本的Python，可以选择下载并安装：
diff --git a/install/mindspore_gpu_install_nightly_en.md b/install/mindspore_gpu_install_nightly_en.md
index 754a26ffabbf1c7be15d86462f35f74669d55446..fc2b858bafa231ee1240e4bcf3c81f087d274648 100644
--- a/install/mindspore_gpu_install_nightly_en.md
+++ b/install/mindspore_gpu_install_nightly_en.md
@@ -25,7 +25,6 @@ This document describes how to quickly install MindSpore Nightly by pip in a Lin
 - Ensure that [OpenMPI 4.0.3](https://www.open-mpi.org/faq/?category=building#easy-build) is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
 - Ensure that [OpenSSL 1.1.1 or later](https://github.com/openssl/openssl.git) is installed.
     - Ensure that [OpenSSL](https://github.com/openssl/openssl) is installed and set system variable `export OPENSSL_ROOT_DIR="OpenSSL installation directory"`.
-- Ensure that [NCCL 2.7.6](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 10.1 or [NCCL 2.7.8](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 11.1 is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
 - Ensure that [TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download) is installed. (optional，required for Serving inference).
 - Ensure that [gmp 6.1.2](https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz) is installed.
 - Ensure that Python 3.7.5 or 3.9.0 is installed. If not installed, download and install Python from:
diff --git a/install/mindspore_gpu_install_pip.md b/install/mindspore_gpu_install_pip.md
index acf38ce6949601dbb01c1e82b421fc09a5df890d..63f8933b8a1910f76d90e48c9b75bed534ff75aa 100644
--- a/install/mindspore_gpu_install_pip.md
+++ b/install/mindspore_gpu_install_pip.md
@@ -25,7 +25,6 @@
 - 确认安装[OpenMPI 4.0.3版本](https://www.open-mpi.org/faq/?category=building#easy-build)（可选，单机多卡/多机多卡训练需要）。
 - 确认安装[OpenSSL 1.1.1及以上版本](https://github.com/openssl/openssl.git)。
     - 安装完成后设置环境变量`export OPENSSL_ROOT_DIR=“OpenSSL安装目录”`。
-- 确认安装配套CUDA 10.1的[NCCL 2.7.6版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)或者配套CUDA 11.1[NCCL 2.7.8版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)（可选，单机多卡/多机多卡训练需要）。
 - 确认安装[TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download)（可选，Serving推理需要）。
 - 确认安装[gmp 6.1.2版本](https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz)。
 - 确认安装Python 3.7.5或3.9.0版本。如果未安装或者已安装其他版本的Python，可以选择下载并安装：
diff --git a/install/mindspore_gpu_install_pip_en.md b/install/mindspore_gpu_install_pip_en.md
index e4d2e6c0ccd8548543ac02c97f969ceeeaeae452..5399618e6043abdcec2d1fe90aaebf9c326fd29f 100644
--- a/install/mindspore_gpu_install_pip_en.md
+++ b/install/mindspore_gpu_install_pip_en.md
@@ -23,7 +23,6 @@ This document describes how to quickly install MindSpore by pip in a Linux syste
 - Ensure that [OpenMPI 4.0.3](https://www.open-mpi.org/faq/?category=building#easy-build) is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
 - Ensure that [OpenSSL 1.1.1 or later](https://github.com/openssl/openssl.git) is installed.
     - Ensure that [OpenSSL](https://github.com/openssl/openssl) is installed and set system variable `export OPENSSL_ROOT_DIR="OpenSSL installation directory"`.
-- Ensure that [NCCL 2.7.6](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 10.1 or [NCCL 2.7.8](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 11.1 is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
 - Ensure that [TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download) is installed. (optional，required for Serving inference).
 - Ensure that [gmp 6.1.2](https://gmplib.org/download/gmp/gmp-6.1.2.tar.xz) is installed.
 - Ensure that Python 3.7.5 or 3.9.0 is installed. If not installed, download and install Python from:
diff --git a/install/mindspore_gpu_install_source.md b/install/mindspore_gpu_install_source.md
index 8e7a8c7c5e5f527c4cf8b590c6ac6c7071d96482..e10f551b867af82500e67c48d1eaa908142741ea 100644
--- a/install/mindspore_gpu_install_source.md
+++ b/install/mindspore_gpu_install_source.md
@@ -57,8 +57,6 @@
 - 确认安装[OpenSSL 1.1.1及以上版本](https://github.com/openssl/openssl.git)。
     - 安装完成后设置环境变量`export OPENSSL_ROOT_DIR=“OpenSSL安装目录”`。
 
-- 确认安装配套CUDA 10.1的[NCCL 2.7.6版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)或者配套CUDA 11.1[NCCL 2.7.8版本](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian)（可选，单机多卡/多机多卡训练需要）。
-
 - 确认安装[TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download)（可选，Serving推理需要）。
 
 - 确认安装[NUMA 2.0.11及以上版本](https://github.com/numactl/numactl)。
diff --git a/install/mindspore_gpu_install_source_en.md b/install/mindspore_gpu_install_source_en.md
index ab03f8d37e92679328b941084776d31d8085fc06..e013f0c7eff451cc4a6f96eb6379caccbac32cfb 100644
--- a/install/mindspore_gpu_install_source_en.md
+++ b/install/mindspore_gpu_install_source_en.md
@@ -54,8 +54,6 @@ This document describes how to quickly install MindSpore by source code in a Lin
 - Ensure that [OpenSSL 1.1.1 or later](https://github.com/openssl/openssl.git) is installed.
     - Ensure that [OpenSSL](https://github.com/openssl/openssl) is installed and set system variable `export OPENSSL_ROOT_DIR="OpenSSL installation directory"`.
 
-- Ensure that [NCCL 2.7.6](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 10.1 or [NCCL 2.7.8](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) for CUDA 11.1 is installed. (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
-
 - Ensure that [TensorRT-7.2.2](https://developer.nvidia.com/nvidia-tensorrt-download) is installed. (optional，required for Serving inference)
 
 - Ensure that [NUMA 2.0.11 or later](https://github.com/numactl/numactl) is installed.
diff --git a/resource/_static/layout.html b/resource/_static/layout.html
new file mode 100644
index 0000000000000000000000000000000000000000..b9db1fbbdc40d99d212954aad560f4221e3ab585
--- /dev/null
+++ b/resource/_static/layout.html
@@ -0,0 +1,246 @@
+{# TEMPLATE VAR SETTINGS #}
+{%- set url_root = pathto('', 1) %}
+{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
+{%- if not embedded and docstitle %}
+  {%- set titlesuffix = " &mdash; "|safe + docstitle|e %}
+{%- else %}
+  {%- set titlesuffix = "" %}
+{%- endif %}
+{%- set lang_attr = 'en' if language == None else (language | replace('_', '-')) %}
+{%- set sphinx_writer = 'writer-html5' if html5_doctype else 'writer-html4' %}
+
+<!DOCTYPE html>
+<html class="{{ sphinx_writer }}" lang="{{ lang_attr }}" >
+<head>
+  <meta charset="utf-8">
+  {{ metatags }}
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  {% block htmltitle %}
+  <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
+  {% endblock %}
+
+  {# CSS #}
+  {%- for css in css_files %}
+    {%- if css|attr("rel") %}
+  <link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
+    {%- else %}
+  <link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
+    {%- endif %}
+  {%- endfor %}
+
+  {%- for cssfile in extra_css_files %}
+    <link rel="stylesheet" href="{{ pathto(cssfile, 1) }}" type="text/css" />
+  {%- endfor %}
+   
+  <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
+  <link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" />
+  
+  {# FAVICON #}
+  {% if favicon %}
+    <link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
+  {% endif %}
+  {# CANONICAL URL #}
+  {% if theme_canonical_url %}
+    <link rel="canonical" href="{{ theme_canonical_url }}{{ pagename }}.html"/>
+  {% endif %}
+
+  {# JAVASCRIPTS #}
+  {%- block scripts %}
+  <!--[if lt IE 9]>
+    <script src="{{ pathto('_static/js/html5shiv.min.js', 1) }}"></script>
+  <![endif]-->
+  {%- if not embedded %}
+  {# XXX Sphinx 1.8.0 made this an external js-file, quick fix until we refactor the template to inherert more blocks directly from sphinx #}
+    {% if sphinx_version >= "1.8.0" %}
+      <script type="text/javascript" id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
+      {%- for scriptfile in script_files %}
+        {{ js_tag(scriptfile) if "mathjax" not in scriptfile.filename|string and "require.min.js" not in scriptfile.filename|string }}
+      {%- endfor %}
+    {% else %}
+      <script type="text/javascript">
+          var DOCUMENTATION_OPTIONS = {
+              URL_ROOT:'{{ url_root }}',
+              VERSION:'{{ release|e }}',
+              LANGUAGE:'{{ language }}',
+              COLLAPSE_INDEX:false,
+              FILE_SUFFIX:'{{ '' if no_search_suffix else file_suffix }}',
+              HAS_SOURCE:  {{ has_source|lower }},
+              SOURCELINK_SUFFIX: '{{ sourcelink_suffix }}'
+          };
+      </script>
+      {%- for scriptfile in script_files %}
+        <script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script>
+      {%- endfor %}
+    {% endif %}
+    <script type="text/javascript" src="{{ pathto('_static/js/theme.js', 1) }}"></script>
+
+    {# OPENSEARCH #}
+    {%- if use_opensearch %}
+    <link rel="search" type="application/opensearchdescription+xml"
+          title="{% trans docstitle=docstitle|e %}Search within {{ docstitle }}{% endtrans %}"
+          href="{{ pathto('_static/opensearch.xml', 1) }}"/>
+    {%- endif %}
+  {%- endif %}
+  {%- endblock %}
+
+  {%- block linktags %}
+    {%- if hasdoc('about') %}
+    <link rel="author" title="{{ _('About these documents') }}" href="{{ pathto('about') }}" />
+    {%- endif %}
+    {%- if hasdoc('genindex') %}
+    <link rel="index" title="{{ _('Index') }}" href="{{ pathto('genindex') }}" />
+    {%- endif %}
+    {%- if hasdoc('search') %}
+    <link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
+    {%- endif %}
+    {%- if hasdoc('copyright') %}
+    <link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
+    {%- endif %}
+    {%- if next %}
+    <link rel="next" title="{{ next.title|striptags|e }}" href="{{ next.link|e }}" />
+    {%- endif %}
+    {%- if prev %}
+    <link rel="prev" title="{{ prev.title|striptags|e }}" href="{{ prev.link|e }}" />
+    {%- endif %}
+  {%- endblock %}
+  {%- block extrahead %} {% endblock %}
+</head>
+
+<body class="wy-body-for-nav">
+
+  {% block extrabody %} {% endblock %}
+  <div class="wy-grid-for-nav">
+    {# SIDE NAV, TOGGLES ON MOBILE #}
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" {% if theme_style_nav_header_background %} style="background: {{theme_style_nav_header_background}}" {% endif %}>
+          {% block sidebartitle %}
+
+          {% if logo and theme_logo_only %}
+            <a href="{{ pathto(master_doc) }}">
+          {% else %}
+            <a href="{{ pathto(master_doc) }}" class="icon icon-home" alt="{{ _("Documentation Home") }}"> {{ project }}
+          {% endif %}
+
+          {% if logo %}
+            {# Not strictly valid HTML, but it's the only way to display/scale
+               it properly, without weird scripting or heaps of work
+            #}
+            <img src="{{ pathto('_static/' + logo, 1) }}" class="logo" alt="{{ _('Logo') }}"/>
+          {% endif %}
+          </a>
+
+          {% if theme_display_version %}
+            {%- set nav_version = version %}
+            {% if READTHEDOCS and current_version %}
+              {%- set nav_version = current_version %}
+            {% endif %}
+            {% if nav_version %}
+              <div class="version">
+                {{ nav_version }}
+              </div>
+            {% endif %}
+          {% endif %}
+
+          {% include "searchbox.html" %}
+
+          {% endblock %}
+        </div>
+
+        {% block navigation %}
+        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
+          {% block menu %}
+            {#
+              The singlehtml builder doesn't handle this toctree call when the
+              toctree is empty. Skip building this for now.
+            #}
+            {% if 'singlehtml' not in builder %}
+              {% set global_toc = toctree(maxdepth=theme_navigation_depth|int,
+                                          collapse=theme_collapse_navigation|tobool,
+                                          includehidden=theme_includehidden|tobool,
+                                          titles_only=theme_titles_only|tobool) %}
+            {% endif %}
+            {% if global_toc %}
+              {{ global_toc }}
+            {% else %}
+              <!-- Local TOC -->
+              <div class="local-toc">{{ toc }}</div>
+            {% endif %}
+          {% endblock %}
+        </div>
+        {% endblock %}
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+
+      {# MOBILE NAV, TRIGGLES SIDE NAV ON TOGGLE #}
+      <nav class="wy-nav-top" aria-label="top navigation">
+        {% block mobile_nav %}
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="{{ pathto(master_doc) }}">{{ project }}</a>
+        {% endblock %}
+      </nav>
+
+
+      <div class="wy-nav-content">
+      {%- block content %}
+        {% if theme_style_external_links|tobool %}
+        <div class="rst-content style-external-links">
+        {% else %}
+        <div class="rst-content">
+        {% endif %}
+          {% include "breadcrumbs.html" %}
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+          {%- block document %}
+           <div itemprop="articleBody">
+            {% block body %}{% endblock %}
+           </div>
+           {% if self.comments()|trim %}
+           <div class="articleComments">
+            {% block comments %}{% endblock %}
+           </div>
+           {% endif%}
+          </div>
+          {%- endblock %}
+          {% include "footer.html" %}
+        </div>
+      {%- endblock %}
+      </div>
+
+    </section>
+
+  </div>
+  {% include "versions.html" %}
+
+  <script type="text/javascript">
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable({{ 'true' if theme_sticky_navigation|tobool else 'false' }});
+      });
+  </script>
+
+  {# Do not conflict with RTD insertion of analytics script #}
+  {% if not READTHEDOCS %}
+    {% if theme_analytics_id %}
+    <!-- Theme Analytics -->
+    <script>
+    (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
+      (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
+      m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
+    })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
+
+    ga('create', '{{ theme_analytics_id }}', 'auto');
+    ga('send', 'pageview');
+    </script>
+
+    {% endif %}
+  {% endif %}
+
+  {%- block footer %} {% endblock %}
+    {%- for scriptfile in script_files %}
+    {%- if "mathjax" in scriptfile.filename|string %}
+	<script async="async" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
+	{%- endif %}    
+    {%- endfor %}
+</body>
+</html>
diff --git a/resource/api_updates/nn_api_updates.md b/resource/api_updates/nn_api_updates.md
index 69ff9fb194874895bd110290add5b4ead8301f53..b585881426b9694e4c7ba3e72842684bf6d92d92 100644
--- a/resource/api_updates/nn_api_updates.md
+++ b/resource/api_updates/nn_api_updates.md
@@ -4,24 +4,26 @@ Compared with the previous version, the added, deleted and supported platforms c
 
 |API|Status|Description|Support Platform|Class
 |:----|:----|:----|:----|:----
-[mindspore.nn.GraphKernel](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.GraphKernel.html#mindspore.nn.GraphKernel)|Deleted|Base class for GraphKernel cell that can be compiled into a fused kernel automatically when enable_graph_kernel in context is set to True.|Ascend/GPU/CPU|Cell
-[mindspore.nn.GraphCell](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.GraphCell.html#mindspore.nn.GraphCell)|New|Base class for running the graph loaded from a MindIR.|r1.5: Ascend/GPU/CPU|Cell
-[mindspore.nn.Jvp](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.Jvp.html#mindspore.nn.Jvp)|New|Compute the jacobian-vector-product of the given network.|r1.5: To Be Developed|Gradient
-[mindspore.nn.Vjp](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.Vjp.html#mindspore.nn.Vjp)|New|Computes the dot product between a vector  v  and the Jacobian of the given network at the point given by the inputs.|r1.5: To Be Developed|Gradient
-[mindspore.nn.SoftMarginLoss](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.SoftMarginLoss.html#mindspore.nn.SoftMarginLoss)|New|A loss class for two-class classification problems.|r1.5: Ascend|Loss Functions
-[mindspore.nn.DiGamma](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.DiGamma.html#mindspore.nn.DiGamma)|Deleted|Calculates Digamma using Lanczos’ approximation referring to “A Precision Approximation of the Gamma Function”.|Ascend/GPU|Math Functions
-[mindspore.nn.IGamma](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.IGamma.html#mindspore.nn.IGamma)|Deleted|Calculates lower regularized incomplete Gamma function.|Ascend/GPU|Math Functions
-[mindspore.nn.LBeta](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.LBeta.html#mindspore.nn.LBeta)|Deleted|This method avoids the numeric cancellation by explicitly decomposing lgamma into the Stirling approximation and an explicit log_gamma_correction, and cancelling the large terms from the Striling analytically.|Ascend/GPU|Math Functions
-[mindspore.nn.LGamma](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.LGamma.html#mindspore.nn.LGamma)|Deleted|Calculates LGamma using Lanczos’ approximation referring to “A Precision Approximation of the Gamma Function”.|Ascend/GPU|Math Functions
-[mindspore.nn.MatDet](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.MatDet.html#mindspore.nn.MatDet)|Deleted|Calculates the determinant of Positive-Definite Hermitian matrix using Cholesky decomposition.|GPU|Math Functions
-[mindspore.nn.MatInverse](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.MatInverse.html#mindspore.nn.MatInverse)|Deleted|Calculates the inverse of Positive-Definite Hermitian matrix using Cholesky decomposition.|GPU|Math Functions
-[mindspore.nn.HShrink](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.HShrink.html#mindspore.nn.HShrink)|New|Applies the hard shrinkage function element-wise, each element complies the follow function:|r1.5: Ascend|Non-linear Activations
-[mindspore.nn.SoftShrink](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.SoftShrink.html#mindspore.nn.SoftShrink)|New|Applies the soft shrinkage function elementwise.|r1.5: Ascend|Non-linear Activations
-[mindspore.nn.GRU](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.GRU.html#mindspore.nn.GRU)|New|Stacked GRU (Gated Recurrent Unit) layers.|r1.5: Ascend/GPU|Recurrent Layers
-[mindspore.nn.GRUCell](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.GRUCell.html#mindspore.nn.GRUCell)|New|A GRU(Gated Recurrent Unit) cell.|r1.5: Ascend/GPU|Recurrent Layers
-[mindspore.nn.RNN](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.RNN.html#mindspore.nn.RNN)|New|Stacked Elman RNN layers.|r1.5: Ascend/GPU|Recurrent Layers
-[mindspore.nn.RNNCell](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.RNNCell.html#mindspore.nn.RNNCell)|New|An Elman RNN cell with tanh or ReLU non-linearity.|r1.5: Ascend/GPU|Recurrent Layers
-[mindspore.nn.Conv2dThor](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.Conv2dThor.html#mindspore.nn.Conv2dThor)|Deleted|2D convolution layer and saving the information needed for THOR.|Ascend/GPU|Thor Layers
-[mindspore.nn.DenseThor](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.DenseThor.html#mindspore.nn.DenseThor)|Deleted|The dense connected layer and saving the information needed for THOR.|Ascend/GPU|Thor Layers
-[mindspore.nn.EmbeddingThor](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/nn/mindspore.nn.EmbeddingThor.html#mindspore.nn.EmbeddingThor)|Deleted|A simple lookup table that stores embeddings of a fixed dictionary and size and saving the information needed for THOR.|Ascend/GPU|Thor Layers
-[mindspore.nn.Roll](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/nn/mindspore.nn.Roll.html#mindspore.nn.Roll)|New|Rolls the elements of a tensor along an axis.|r1.5: Ascend|Utilities
+|[mindspore.nn.MicroBatchInterleaved](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.MicroBatchInterleaved.html#mindspore.nn.MicroBatchInterleaved)|New|Wrap the network with Batch Size.|r1.6: Ascend/GPU|Wrapper Functions
+|[mindspore.nn.PipelineCell](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.PipelineCell.html#mindspore.nn.PipelineCell)|Changed|Wrap the network with Micro Batch.|r1.5: To Be Developed => r1.6: Ascend/GPU|Wrapper Functions
+|[mindspore.nn.Unfold](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.Unfold.html#mindspore.nn.Unfold)|Changed|Extracts patches from images.|r1.5: Ascend => r1.6: Ascend/GPU|Utilities
+|[mindspore.nn.GRU](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.GRU.html#mindspore.nn.GRU)|Changed|Stacked GRU (Gated Recurrent Unit) layers.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|Recurrent Layers
+|[mindspore.nn.RNNCell](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.RNNCell.html#mindspore.nn.RNNCell)|Changed|An Elman RNN cell with tanh or ReLU non-linearity.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|Recurrent Layers
+|[mindspore.nn.LSTMCell](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.LSTMCell.html#mindspore.nn.LSTMCell)|Changed|A LSTM (Long Short-Term Memory) cell.|r1.5: GPU/CPU => r1.6: Ascend/GPU/CPU|Recurrent Layers
+|[mindspore.nn.RNN](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.RNN.html#mindspore.nn.RNN)|Changed|Stacked Elman RNN layers.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|Recurrent Layers
+|[mindspore.nn.GRUCell](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.GRUCell.html#mindspore.nn.GRUCell)|Changed|A GRU(Gated Recurrent Unit) cell.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|Recurrent Layers
+|[mindspore.nn.LSTM](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.LSTM.html#mindspore.nn.LSTM)|Changed|Stacked LSTM (Long Short-Term Memory) layers.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|Recurrent Layers
+|[mindspore.nn.Rprop](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.Rprop.html#mindspore.nn.Rprop)|New|Implements Resilient backpropagation.|r1.6: Ascend/GPU/CPU|Optimizer Functions
+|[mindspore.nn.ASGD](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.ASGD.html#mindspore.nn.ASGD)|New|Implements Average Stochastic Gradient Descent.|r1.6: Ascend/GPU/CPU|Optimizer Functions
+|[mindspore.nn.Lamb](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.Lamb.html#mindspore.nn.Lamb)|Changed|An optimizer that implements the Lamb(Layer-wise Adaptive Moments optimizer for Batching training) algorithm.|r1.5: Ascend/GPU/CPU => r1.6: Ascend/GPU|Optimizer Functions
+|[mindspore.nn.LARS](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.LARS.html#mindspore.nn.LARS)|Changed|Implements the LARS algorithm with LARSUpdate Operator.|r1.5: Ascend/CPU => r1.6: Ascend|Optimizer Functions
+|[mindspore.nn.FTRL](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.FTRL.html#mindspore.nn.FTRL)|Changed|Implements the FTRL algorithm with ApplyFtrl Operator.|r1.5: Ascend/GPU/CPU => r1.6: Ascend/GPU|Optimizer Functions
+|[mindspore.nn.LazyAdam](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.LazyAdam.html#mindspore.nn.LazyAdam)|Changed|r1.5: This optimizer will apply a lazy adam algorithm when gradient is sparse. => r1.6: Updates gradients by the Adaptive Moment Estimation (Adam) algorithm.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|Optimizer Functions
+|[mindspore.nn.GlobalBatchNorm](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.GlobalBatchNorm.html#mindspore.nn.GlobalBatchNorm)|Changed|The GlobalBatchNorm interface is deprecated, please use the  mindspore.nn.SyncBatchNorm  instead.|r1.5: Ascend => r1.6: deprecated|Normalization Layers
+|[mindspore.nn.CELU](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.CELU.html#mindspore.nn.CELU)|New|Continuously differentiable exponential linear units activation function.|r1.6: Ascend|Non-linear Activations
+|[mindspore.nn.LogSigmoid](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.LogSigmoid.html#mindspore.nn.LogSigmoid)|Changed|Logsigmoid activation function.|r1.5: Ascend/GPU/CPU => r1.6: Ascend/GPU|Non-linear Activations
+|[mindspore.nn.MatMul](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.MatMul.html#mindspore.nn.MatMul)|Changed|The nn.MatMul interface is deprecated, please use the  mindspore.ops.matmul  instead.|r1.5: Ascend/GPU/CPU => r1.6: deprecated|Math Functions
+|mindspore.nn.MAELoss|Deleted|MAELoss creates a criterion to measure the average absolute error between  \(x\)  and  \(y\)  element-wise, where  \(x\)  is the input and  \(y\)  is the labels.|r1.5: Ascend/GPU/CPU|Loss Functions
+|[mindspore.nn.Jvp](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.Jvp.html#mindspore.nn.Jvp)|Changed|Compute the jacobian-vector-product of the given fn.|r1.5: To Be Developed => r1.6: Ascend/GPU/CPU|Gradient
+|[mindspore.nn.Vjp](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/nn/mindspore.nn.Vjp.html#mindspore.nn.Vjp)|Changed|Computes the dot product between a vector  v  and the Jacobian of the given fn at the point given by the inputs.|r1.5: To Be Developed => r1.6: Ascend/GPU/CPU|Gradient
+|mindspore.nn.GraphKernel|Deleted|Base class for GraphKernel.|r1.5: Ascend/GPU/CPU|Cell
\ No newline at end of file
diff --git a/resource/api_updates/ops_api_updates.md b/resource/api_updates/ops_api_updates.md
index 5160bb8fb81aa49c7c579fd44ae1bdc1069a8eea..b9403968631924109be47a4e1b70364d158e1286 100644
--- a/resource/api_updates/ops_api_updates.md
+++ b/resource/api_updates/ops_api_updates.md
@@ -4,121 +4,34 @@ Compared with the previous version, the added, deleted and supported platforms c
 
 |API|Status|Description|Support Platform|Class
 |:----|:----|:----|:----|:----
-[mindspore.ops.acos](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ACos.html#mindspore.ops.ACos)|New|Refer to mindspore.ops.ACos.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.acosh](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Acosh.html#mindspore.ops.Acosh)|New|Refer to mindspore.ops.Acosh.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.add](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Add.html#mindspore.ops.Add)|New|Refer to mindspore.ops.Add.|r1.5: Ascend/GPU/CPU|functional
-mindspore.ops.array_reduce|Deleted|Reduce the dimension of the array.|  |functional
-mindspore.ops.array_to_scalar|Deleted|Convert the array to a scalar.|  |functional
-[mindspore.ops.asin](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Asin.html#mindspore.ops.Asin)|New|Refer to mindspore.ops.Asin.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.asinh](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Asinh.html#mindspore.ops.Asinh)|New|Refer to mindspore.ops.Asinh.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.atan](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Atan.html#mindspore.ops.Atan)|New|Refer to mindspore.ops.Atan.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.atan2](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Atan2.html#mindspore.ops.Atan2)|New|Refer to mindspore.ops.Atan2.|r1.5: Ascend/CPU/GPU|functional
-[mindspore.ops.atanh](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Atanh.html#mindspore.ops.Atanh)|New|Refer to mindspore.ops.Atanh.|r1.5: Ascend/CPU|functional
-[mindspore.ops.bitwise_and](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.BitwiseAnd.html#mindspore.ops.BitwiseAnd)|New|Refer to mindspore.ops.BitwiseAnd.|r1.5: Ascend|functional
-[mindspore.ops.bitwise_or](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.BitwiseOr.html#mindspore.ops.BitwiseOr)|New|Refer to mindspore.ops.BitwiseOr.|r1.5: Ascend|functional
-[mindspore.ops.bitwise_xor](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.BitwiseXor.html#mindspore.ops.BitwiseXor)|New|Refer to mindspore.ops.BitwiseXor.|r1.5: Ascend|functional
-[mindspore.ops.cos](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Cos.html#mindspore.ops.Cos)|New|Refer to mindspore.ops.Cos.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.cosh](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Cosh.html#mindspore.ops.Cosh)|New|Refer to mindspore.ops.Cosh.|r1.5: Ascend/CPU|functional
-[mindspore.ops.cumprod](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.CumProd.html#mindspore.ops.CumProd)|New|Refer to mindspore.ops.CumProd.|r1.5: Ascend/GPU|functional
-[mindspore.ops.cumsum](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.CumSum.html#mindspore.ops.CumSum)|New|Refer to mindspore.ops.CumSum.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.depend](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Depend.html#mindspore.ops.Depend)|New|Refer to mindspore.ops.Depend.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.div](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.RealDiv.html#mindspore.ops.RealDiv)|New|Refer to mindspore.ops.RealDiv.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.erf](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Erf.html#mindspore.ops.Erf)|New|Refer to mindspore.ops.Erf.|r1.5: Ascend/GPU|functional
-[mindspore.ops.erfc](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Erfc.html#mindspore.ops.Erfc)|New|Refer to mindspore.ops.Erfc.|r1.5: Ascend/GPU|functional
-[mindspore.ops.exp](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Exp.html#mindspore.ops.Exp)|New|Refer to mindspore.ops.Exp.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.eye](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Eye.html#mindspore.ops.Eye)|New|Refer to mindspore.ops.Eye.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.floor](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Floor.html#mindspore.ops.Floor)|New|Refer to mindspore.ops.Floor.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.floordiv](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.FloorDiv.html#mindspore.ops.FloorDiv)|New|Refer to mindspore.ops.FloorDiv.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.floormod](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.FloorMod.html#mindspore.ops.FloorMod)|New|Refer to mindspore.ops.FloorMod.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.gather_d](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.GatherD.html#mindspore.ops.GatherD)|New|Refer to mindspore.ops.GatherD.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.ge](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.GreaterEqual.html#mindspore.ops.GreaterEqual)|New|Refer to mindspore.ops.GreaterEqual.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.gt](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Greater.html#mindspore.ops.Greater)|New|Refer to mindspore.ops.Greater.|r1.5: Ascend/GPU/CPU|functional
-mindspore.ops.hastype|Deleted|Determine whether the object has the specified type.|  |functional
-[mindspore.ops.invert](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Invert.html#mindspore.ops.Invert)|New|Refer to mindspore.ops.Invert.|r1.5: Ascend|functional
-[mindspore.ops.isfinite](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.IsFinite.html#mindspore.ops.IsFinite)|New|Refer to mindspore.ops.IsFinite.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.isnan](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.IsNan.html#mindspore.ops.IsNan)|New|Refer to mindspore.ops.IsNan.|r1.5: GPU/CPU|functional
-[mindspore.ops.le](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.LessEqual.html#mindspore.ops.LessEqual)|New|Refer to mindspore.ops.LessEqual.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.less](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Less.html#mindspore.ops.Less)|New|Refer to mindspore.ops.Less.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.log](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Log.html#mindspore.ops.Log)|New|Refer to mindspore.ops.Log.|r1.5: Ascend/GPU/CPU|functional
-mindspore.ops.make_row_tensor|Deleted|Generate row tensor.|  |functional
-mindspore.ops.make_sparse_tensor|Deleted|Generate sparse tensor.|  |functional
-[mindspore.ops.maximum](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Maximum.html#mindspore.ops.Maximum)|New|Refer to mindspore.ops.Maximum.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.minimum](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Minimum.html#mindspore.ops.Minimum)|New|Refer to mindspore.ops.Minimum.|r1.5: Ascend/GPU/CPU|functional
-mindspore.ops.mixed_precision_cast|Deleted|A temporary ops for mixed precision will be converted to cast after the step of compiling.|  |functional
-[mindspore.ops.mul](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Mul.html#mindspore.ops.Mul)|New|Refer to mindspore.ops.Mul.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.pows](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Pow.html#mindspore.ops.Pow)|New|Refer to mindspore.ops.Pow.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.reduce_max](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ReduceMax.html#mindspore.ops.ReduceMax)|New|Refer to mindspore.ops.ReduceMax.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.reduce_mean](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ReduceMean.html#mindspore.ops.ReduceMean)|New|Refer to mindspore.ops.ReduceMean.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.reduce_min](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ReduceMin.html#mindspore.ops.ReduceMin)|New|Refer to mindspore.ops.ReduceMin.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.reduce_prod](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ReduceProd.html#mindspore.ops.ReduceProd)|New|Refer to mindspore.ops.ReduceProd.|r1.5: Ascend/GPU|functional
-[mindspore.ops.reduce_sum](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ReduceSum.html#mindspore.ops.ReduceSum)|New|Refer to mindspore.ops.ReduceSum.|r1.5: Ascend/GPU/CPU|functional
-mindspore.ops.reduced_shape|Deleted|Calculate the shape of the reduction operator.|  |functional
-mindspore.ops.row_tensor_get_dense_shape|Deleted|Get corresponding dense shape of row tensor.|  |functional
-mindspore.ops.row_tensor_get_indices|Deleted|Get indices of row tensor.|  |functional
-mindspore.ops.row_tensor_get_values|Deleted|Get values of row tensor.|  |functional
-[mindspore.ops.sin](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Sin.html#mindspore.ops.Sin)|New|Refer to mindspore.ops.Sin.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.sinh](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Sinh.html#mindspore.ops.Sinh)|New|Refer to mindspore.ops.Sinh.|r1.5: Ascend/CPU|functional
-[mindspore.ops.sort](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Sort.html#mindspore.ops.Sort)|New|Refer to mindspore.ops.Sort.|r1.5: Ascend/GPU/CPU|functional
-mindspore.ops.sparse_tensor_get_dense_shape|Deleted|Get corresponding dense shape of sparse tensor.|  |functional
-mindspore.ops.sparse_tensor_get_indices|Deleted|Get indices of sparse tensor.|  |functional
-mindspore.ops.sparse_tensor_get_values|Deleted|Get values of sparse tensor.|  |functional
-[mindspore.ops.squeeze](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Squeeze.html#mindspore.ops.Squeeze)|New|Refer to mindspore.ops.Squeeze.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.sub](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Sub.html#mindspore.ops.Sub)|New|Refer to mindspore.ops.Sub.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.tan](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Tan.html#mindspore.ops.Tan)|New|Refer to mindspore.ops.Tan.|r1.5: Ascend/CPU|functional
-[mindspore.ops.tanh](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Tanh.html#mindspore.ops.Tanh)|New|Refer to mindspore.ops.Tanh.|r1.5: GPU/  /CPU|functional
-[mindspore.ops.tensor_add](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Add.html#mindspore.ops.Add)|New|Refer to mindspore.ops.Add.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.tensor_exp](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Exp.html#mindspore.ops.Exp)|New|Refer to mindspore.ops.Exp.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.tensor_expm1](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Expm1.html#mindspore.ops.Expm1)|New|Refer to mindspore.ops.Expm1.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.tensor_scatter_add](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.TensorScatterAdd.html#mindspore.ops.TensorScatterAdd)|New|Refer to mindspore.ops.TensorScatterAdd.|r1.5: GPU|functional
-[mindspore.ops.tensor_scatter_update](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.TensorScatterUpdate.html#mindspore.ops.TensorScatterUpdate)|New|Refer to mindspore.ops.TensorScatterUpdate.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.tensor_slice](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Slice.html#mindspore.ops.Slice)|New|Refer to mindspore.ops.Slice.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.transpose](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Transpose.html#mindspore.ops.Transpose)|New|Refer to mindspore.ops.Transpose.|r1.5: Ascend/GPU/CPU|functional
-[mindspore.ops.MaskedFill](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.MaskedFill.html#mindspore.ops.MaskedFill)|New|Fills elements of self tensor with value where mask is True.|r1.5: Ascend|operations--Array Operators
-[mindspore.ops.SplitV](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.SplitV.html#mindspore.ops.SplitV)|New|Splits the input tensor into num_split tensors along the given dimension.|r1.5: Ascend|operations--Array Operators
-[mindspore.ops.TensorScatterMax](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.TensorScatterMax.html#mindspore.ops.TensorScatterMax)|New|By comparing the value at the position indicated by the index in input_x with the value in the update, the value at the index will eventually be equal to the largest one to create a new tensor.|r1.5: GPU|operations--Array Operators
-[mindspore.ops.TensorScatterMin](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.TensorScatterMin.html#mindspore.ops.TensorScatterMin)|New|By comparing the value at the position indicated by the index in input_x with the value in the  updates , the value at the index will eventually be equal to the smallest one to create a new tensor.|r1.5: GPU|operations--Array Operators
-[mindspore.ops.TensorScatterSub](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.TensorScatterSub.html#mindspore.ops.TensorScatterSub)|New|Creates a new tensor by subtracting the values from the positions in  input_x  indicicated by  indices , with values from  updates .|r1.5: GPU|operations--Array Operators
-[mindspore.ops.HookBackward](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.HookBackward.html#mindspore.ops.HookBackward)|New|This operation is used as a tag to hook gradient in intermediate variables.|r1.5: To Be Developed|operations--Debug Operators
-[mindspore.ops.Conj](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Conj.html#mindspore.ops.Conj)|New|Returns a Tensor that is the real part of the input.|r1.5: GPU|operations--Math Operators
-[mindspore.ops.Erfinv](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Erfinv.html#mindspore.ops.Erfinv)|New|Computes the inverse error function of input.|r1.5: Ascend|operations--Math Operators
-[mindspore.ops.Imag](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Imag.html#mindspore.ops.Imag)|New|Returns a new tensor containing imaginary value of the input.|r1.5: GPU|operations--Math Operators
-[mindspore.ops.Lerp](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Lerp.html#mindspore.ops.Lerp)|New|Does a linear interpolation of two tensors start and end based on a float or tensor weight.|r1.5: Ascend|operations--Math Operators
-[mindspore.ops.Real](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Real.html#mindspore.ops.Real)|New|Returns a Tensor that is the real part of the input.|r1.5: GPU|operations--Math Operators
-[mindspore.ops.AdamWeightDecay](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.AdamWeightDecay.html#mindspore.ops.AdamWeightDecay)|New|Updates gradients by the Adaptive Moment Estimation (AdamWeightDecay) algorithm with weight decay.|r1.5: GPU/CPU|operations--Neural Network Operators
-[mindspore.ops.ApplyAdagradDA](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.ApplyAdagradDA.html#mindspore.ops.ApplyAdagradDA)|New|Update  var  according to the proximal adagrad scheme.|r1.5: Ascend|operations--Neural Network Operators
-[mindspore.ops.BNTrainingReduce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BNTrainingReduce.html#mindspore.ops.BNTrainingReduce)|Deleted|For the BatchNorm operation this operator updates the moving averages for training and is used in conjunction with BNTrainingUpdate.|Ascend|operations--Neural Network Operators
-[mindspore.ops.BNTrainingUpdate](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BNTrainingUpdate.html#mindspore.ops.BNTrainingUpdate)|Deleted|For the BatchNorm operation, this operator updates the moving averages for training and is used in conjunction with BNTrainingReduce.|Ascend|operations--Neural Network Operators
-[mindspore.ops.HShrink](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.HShrink.html#mindspore.ops.HShrink)|New|Applies the hard shrinkage function element-wise, each element complies the follow function:|r1.5: Ascend|operations--Neural Network Operators
-[mindspore.ops.SoftMarginLoss](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.SoftMarginLoss.html#mindspore.ops.SoftMarginLoss)|New|SoftMarginLoss operation.|r1.5: Ascend|operations--Neural Network Operators
-[mindspore.ops.SoftShrink](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.SoftShrink.html#mindspore.ops.SoftShrink)|New|Applies the soft shrinkage function elementwise.|r1.5: Ascend|operations--Neural Network Operators
-[mindspore.ops.AngleAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.AngleAtomEnergy.html#mindspore.ops.AngleAtomEnergy)|Deleted|Add the potential energy caused by angle terms to the total potential energy of each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.AngleEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.AngleEnergy.html#mindspore.ops.AngleEnergy)|Deleted|Calculate the energy caused by 3-atoms angle term.|GPU|operations--Sponge Operators
-[mindspore.ops.AngleForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.AngleForce.html#mindspore.ops.AngleForce)|Deleted|Calculate the force exerted by angles made of 3 atoms on the corresponding atoms.|GPU|operations--Sponge Operators
-[mindspore.ops.AngleForceWithAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.AngleForceWithAtomEnergy.html#mindspore.ops.AngleForceWithAtomEnergy)|Deleted|Calculate angle force and potential energy together.|GPU|operations--Sponge Operators
-[mindspore.ops.BondAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BondAtomEnergy.html#mindspore.ops.BondAtomEnergy)|Deleted|Add the potential energy caused by simple harmonic bonds to the total potential energy of each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.BondEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BondEnergy.html#mindspore.ops.BondEnergy)|Deleted|Calculate the harmonic potential energy between each bonded atom pair.|GPU|operations--Sponge Operators
-[mindspore.ops.BondForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BondForce.html#mindspore.ops.BondForce)|Deleted|Calculate the force exerted by the simple harmonic bond on the corresponding atoms.|GPU|operations--Sponge Operators
-[mindspore.ops.BondForceWithAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BondForceWithAtomEnergy.html#mindspore.ops.BondForceWithAtomEnergy)|Deleted|Calculate bond force and harmonic potential energy together.|GPU|operations--Sponge Operators
-[mindspore.ops.BondForceWithAtomVirial](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.BondForceWithAtomVirial.html#mindspore.ops.BondForceWithAtomVirial)|Deleted|Calculate bond force and the virial coefficient caused by simple harmonic bond for each atom together.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14CFAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14CFAtomEnergy.html#mindspore.ops.Dihedral14CFAtomEnergy)|Deleted|Add the potential energy caused by Coulumb energy correction for each necessary dihedral 1,4 terms to the total potential energy of each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14CFEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14CFEnergy.html#mindspore.ops.Dihedral14CFEnergy)|Deleted|Calculate the Coulumb part of 1,4 dihedral energy correction for each necessary dihedral terms on the corresponding atoms.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14LJAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14LJAtomEnergy.html#mindspore.ops.Dihedral14LJAtomEnergy)|Deleted|Add the potential energy caused by Lennard-Jones energy correction for each necessary dihedral 1,4 terms to the total potential energy of each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14LJCFForceWithAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14LJCFForceWithAtomEnergy.html#mindspore.ops.Dihedral14LJCFForceWithAtomEnergy)|Deleted|Calculate the Lennard-Jones and Coulumb energy correction and force correction for each necessary dihedral 1,4 terms together and add them to the total force and potential energy for each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14LJEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14LJEnergy.html#mindspore.ops.Dihedral14LJEnergy)|Deleted|Calculate the Lennard-Jones part of 1,4 dihedral energy correction for each necessary dihedral terms on the corresponding atoms.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14LJForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14LJForce.html#mindspore.ops.Dihedral14LJForce)|Deleted|Calculate the Lennard-Jones part of 1,4 dihedral force correction for each necessary dihedral terms on the corresponding atoms.|GPU|operations--Sponge Operators
-[mindspore.ops.Dihedral14LJForceWithDirectCF](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.Dihedral14LJForceWithDirectCF.html#mindspore.ops.Dihedral14LJForceWithDirectCF)|Deleted|Calculate the Lennard-Jones part and the Coulomb part of force correction for each necessary dihedral 1,4 terms.|GPU|operations--Sponge Operators
-[mindspore.ops.DihedralAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.DihedralAtomEnergy.html#mindspore.ops.DihedralAtomEnergy)|Deleted|Add the potential energy caused by dihedral terms to the total potential energy of each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.DihedralEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.DihedralEnergy.html#mindspore.ops.DihedralEnergy)|Deleted|Calculate the potential energy caused by dihedral terms for each 4-atom pair.|GPU|operations--Sponge Operators
-[mindspore.ops.DihedralForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.DihedralForce.html#mindspore.ops.DihedralForce)|Deleted|Calculate the force exerted by the dihedral term which made of 4-atoms on the corresponding atoms.|GPU|operations--Sponge Operators
-[mindspore.ops.DihedralForceWithAtomEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.DihedralForceWithAtomEnergy.html#mindspore.ops.DihedralForceWithAtomEnergy)|Deleted|Calculate dihedral force and potential energy together.|GPU|operations--Sponge Operators
-[mindspore.ops.LJEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.LJEnergy.html#mindspore.ops.LJEnergy)|Deleted|Calculate the Van der Waals interaction energy described by Lennard-Jones potential for each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.LJForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.LJForce.html#mindspore.ops.LJForce)|Deleted|Calculate the Van der Waals interaction force described by Lennard-Jones potential energy for each atom.|GPU|operations--Sponge Operators
-[mindspore.ops.LJForceWithPMEDirectForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.LJForceWithPMEDirectForce.html#mindspore.ops.LJForceWithPMEDirectForce)|Deleted|Calculate the Lennard-Jones force and PME direct force together.|GPU|operations--Sponge Operators
-[mindspore.ops.MDIterationLeapFrog](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.MDIterationLeapFrog.html#mindspore.ops.MDIterationLeapFrog)|Deleted|One step of classical leap frog algorithm to solve the finite difference Hamiltonian equations of motion for certain system.|GPU|operations--Sponge Operators
-[mindspore.ops.MDIterationLeapFrogLiujian](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.MDIterationLeapFrogLiujian.html#mindspore.ops.MDIterationLeapFrogLiujian)|Deleted|One step of classical leap frog algorithm to solve the finite difference Hamiltonian equations of motion for certain system, using Langevin dynamics with Liu’s thermostat scheme.|GPU|operations--Sponge Operators
-[mindspore.ops.NeighborListUpdate](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.NeighborListUpdate.html#mindspore.ops.NeighborListUpdate)|Deleted|Update (or construct if first time) the Verlet neighbor list for the calculation of short-ranged force.|GPU|operations--Sponge Operators
-[mindspore.ops.PMEEnergy](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.PMEEnergy.html#mindspore.ops.PMEEnergy)|Deleted|Calculate the Coulumb energy of the system using PME method.|GPU|operations--Sponge Operators
-[mindspore.ops.PMEExcludedForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.PMEExcludedForce.html#mindspore.ops.PMEExcludedForce)|Deleted|Calculate the excluded  part of long-range Coulumb force using PME(Particle Meshed Ewald) method.|GPU|operations--Sponge Operators
-[mindspore.ops.PMEReciprocalForce](https://www.mindspore.cn/docs/api/zh-CN/r1.3/api_python/ops/mindspore.ops.PMEReciprocalForce.html#mindspore.ops.PMEReciprocalForce)|Deleted|Calculate the reciprocal part of long-range Coulumb force using PME(Particle Meshed Ewald) method.|GPU|operations--Sponge Operators
-[mindspore.ops.Abs](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Abs.html#mindspore.ops.Abs)|Changed|r1.5: Returns absolute value of a tensor element-wise.|r1.5: Ascend/GPU/CPU |operations--r1.3: Math Operators => r1.5: functional
-[mindspore.ops.Add](https://www.mindspore.cn/docs/api/zh-CN/r1.5/api_python/ops/mindspore.ops.Add.html#mindspore.ops.Add)|Changed|r1.5: Adds two input tensors element-wise.|r1.5: Ascend/GPU/CPU|r1.3: functional => r1.5: operations--Math Operators
+|[mindspore.ops.cummin](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.cummin.html#mindspore.ops.cummin)|New|Computation of the cumulative minimum of elements of ‘x’ in the dimension axis, and the index location of each maximum value found in the dimension ‘axis’.|r1.6: Ascend|composite
+|[mindspore.ops.Map](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Map.html#mindspore.ops.Map)|New|Map will apply the set operation on input sequences.|r1.6: Ascend/GPU/CPU|composite
+|[mindspore.ops.clip_by_global_norm](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.clip_by_global_norm.html#mindspore.ops.clip_by_global_norm)|Changed|Clips tensor values by the ratio of the sum of their norms.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|composite
+|[mindspore.ops.clip_by_value](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.clip_by_value.html#mindspore.ops.clip_by_value)|Changed|Clips tensor values to a specified min and max.|r1.5: Ascend/GPU => r1.6: Ascend/GPU/CPU|composite
+|[mindspore.ops.check_bprop](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.CheckBprop.html#mindspore.ops.CheckBprop)|New|Refer to mindspore.ops.CheckBprop.|r1.6: Ascend/GPU/CPU|functional
+|[mindspore.ops.partial](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Partial.html#mindspore.ops.Partial)|New|Refer to mindspore.ops.Partial.|r1.6: Ascend/GPU/CPU|functional
+|mindspore.ops.select|Deleted|Refer to mindspore.ops.Select.| |functional
+|[mindspore.ops.AiCPURegOp](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.AiCPURegOp.html#mindspore.ops.AiCPURegOp)|New|Class for AiCPU operator information register.| |op_info_register
+|[mindspore.ops.custom_info_register](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.custom_info_register.html#mindspore.ops.custom_info_register)|New|A decorator which is used to bind the registration information to the func parameter of mindspore.ops.Custom.| |op_info_register
+|[mindspore.ops.CustomRegOp](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.CustomRegOp.html#mindspore.ops.CustomRegOp)|New|Class used for generating the registration information for the func parameter of mindspore.ops.Custom.| |op_info_register
+|[mindspore.ops.ExtractVolumePatches](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.ExtractVolumePatches.html#mindspore.ops.ExtractVolumePatches)|New|Extract patches from input and put them in the “depth” output dimension.|r1.6: Ascend|operations--Array Operators
+|[mindspore.ops.SpaceToBatch](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.SpaceToBatch.html#mindspore.ops.SpaceToBatch)|Changed|SpaceToBatch is deprecated.|r1.5: Ascend/GPU => r1.6: Deprecated|operations--Array Operators
+|[mindspore.ops.AlltoAll](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.AlltoAll.html#mindspore.ops.AlltoAll)|New|AlltoAll is a collective operation.|r1.6: Ascend|operations--Communication Operators
+|[mindspore.ops.NeighborExchange](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.NeighborExchange.html#mindspore.ops.NeighborExchange)|New|NeighborExchange is a collective operation.|r1.6: Ascend|operations--Communication Operators
+|[mindspore.ops.NeighborExchangeV2](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.NeighborExchangeV2.html#mindspore.ops.NeighborExchangeV2)|New|NeighborExchangeV2 is a collective operation.|r1.6: Ascend|operations--Communication Operators
+|[mindspore.ops.Custom](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Custom.html#mindspore.ops.Custom)|New|Custom  primitive is used for user defined operators and is to enhance the expressive ability of built-in primitives.|r1.6: Ascend/GPU/CPU|operations--Custom Operators
+|[mindspore.ops.HookBackward](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.HookBackward.html#mindspore.ops.HookBackward)|Changed|This operation is used as a tag to hook gradient in intermediate variables.|r1.5: To Be Developed => r1.6: Ascend/GPU/CPU|operations--Debug Operators
+|[mindspore.ops.Cdist](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Cdist.html#mindspore.ops.Cdist)|New|Computes batched the p-norm distance between each pair of the two collections of row vectors.|r1.6: Ascend|operations--Math Operators
+|[mindspore.ops.Ger](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Ger.html#mindspore.ops.Ger)|New|Ger product of  x1  and  x2 .|r1.6: Ascend|operations--Math Operators
+|[mindspore.ops.LpNorm](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.LpNorm.html#mindspore.ops.LpNorm)|New|Returns the matrix norm or vector norm of a given tensor.|r1.6: Ascend|operations--Math Operators
+|[mindspore.ops.IsInf](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.IsInf.html#mindspore.ops.IsInf)|Changed|Determines which elements are inf or -inf for each position|r1.5: GPU => r1.6: GPU/CPU|operations--Math Operators
+|[mindspore.ops.MatrixInverse](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.MatrixInverse.html#mindspore.ops.MatrixInverse)|Changed|Returns the inverse of the input matrix.|r1.5: GPU => r1.6: GPU/CPU|operations--Math Operators
+|[mindspore.ops.Reciprocal](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Reciprocal.html#mindspore.ops.Reciprocal)|Changed|Returns reciprocal of a tensor element-wise.|r1.5: Ascend/GPU/CPU => r1.6: Ascend/GPU|operations--Math Operators
+|[mindspore.ops.Conv2DTranspose](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Conv2DTranspose.html#mindspore.ops.Conv2DTranspose)|New|Compute a 2D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution).|r1.6: Ascend/GPU/CPU|operations--Neural Network Operators
+|[mindspore.ops.Conv2DBackpropInput](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Conv2DBackpropInput.html#mindspore.ops.Conv2DBackpropInput)|Changed|The Conv2DBackpropInput interface is deprecated, please refer to  mindspore.ops.Conv2dTranspose  if you want to do unsampling.|r1.5: Ascend/GPU/CPU => r1.6: Deprecated|operations--Neural Network Operators
+|[mindspore.ops.DepthwiseConv2dNative](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.DepthwiseConv2dNative.html#mindspore.ops.DepthwiseConv2dNative)|Changed|DepthwiseConv2dNative will be deprecated in the future.|r1.5: Ascend => r1.6: Deprecated|operations--Neural Network Operators
+|[mindspore.ops.DropoutDoMask](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.DropoutDoMask.html#mindspore.ops.DropoutDoMask)|Changed|The DropoutDoMask interface is deprecated, please use the  mindspore.ops.Dropout  instead.|r1.5: Ascend => r1.6: Deprecated|operations--Neural Network Operators
+|[mindspore.ops.DropoutGenMask](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.DropoutGenMask.html#mindspore.ops.DropoutGenMask)|Changed|The DropoutGenMask interface is deprecated, please use the  mindspore.ops.Dropout  instead.|r1.5: Ascend => r1.6: Deprecated|operations--Neural Network Operators
+|[mindspore.ops.CheckBprop](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.CheckBprop.html#mindspore.ops.CheckBprop)|New|Checks whether the data type and the shape of corresponding elements from tuples x and y are the same.|r1.6: Ascend/GPU/CPU|operations--Other Operators
+|[mindspore.ops.Partial](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Partial.html#mindspore.ops.Partial)|New|Makes a partial function instance.|r1.6: Ascend/GPU/CPU|operations--Other Operators
+|[mindspore.ops.Abs](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/ops/mindspore.ops.Abs.html#mindspore.ops.Abs)|Changed|Returns absolute value of a tensor element-wise.|r1.6: Ascend/GPU/CPU|operations--r1.5: functional => r1.6: Math Operators
diff --git a/resource/release/release_list_en.md b/resource/release/release_list_en.md
index 442f539ed3ea80812418642414375f5adae6b25c..178f8c43e7ad9ef54eae3cf96c50c4e0c5996b2b 100644
--- a/resource/release/release_list_en.md
+++ b/resource/release/release_list_en.md
@@ -4,6 +4,7 @@
     - [Related Documents](#related-documents)
     - [Ascend Supporting Software Package](#ascend-supporting-software-package)
     - [Downloads](#downloads)
+        - [1.6.0](#160)
         - [1.5.2](#152)
         - [1.5.1](#151)
         - [1.5.0](#150)
@@ -32,39 +33,43 @@
 
 |   Category   |   Subcategory   |      Version      |
 |    ---   |    ---   |       ---      |
-| Releasenotes and API Updates | | [1.5.0](https://gitee.com/mindspore/mindspore/blob/r1.5/RELEASE.md) &nbsp;&nbsp;  [1.3.0](https://gitee.com/mindspore/mindspore/blob/r1.3/RELEASE.md) &nbsp;&nbsp; [1.2.1](https://gitee.com/mindspore/mindspore/blob/r1.2/RELEASE.md) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/mindspore/blob/r1.1/RELEASE.md) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/mindspore/blob/r1.0/RELEASE.md) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.7/RELEASE.md) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.6/RELEASE.md) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.2/RELEASE.md) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.1/RELEASE.md) |
-| Installation | | [1.5.0](https://gitee.com/mindspore/docs/tree/r1.5/install) &nbsp;&nbsp;  [1.3.0](https://gitee.com/mindspore/docs/tree/r1.3/install) &nbsp;&nbsp; [1.2.1](https://gitee.com/mindspore/docs/tree/r1.2/install) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/docs/tree/r1.1/install) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/docs/tree/r1.0/install) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/docs/tree/r0.7/install) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/docs/tree/r0.6/install) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/docs/tree/r0.5/install) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/docs/tree/r0.3/install) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/docs/tree/r0.2/install) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/docs/tree/r0.1/install) |
-| Tutorials |  | [1.5.0](https://www.mindspore.cn/tutorials/en/r1.5/index.html) &nbsp;&nbsp;  [1.3.0](https://www.mindspore.cn/tutorials/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1-Training](https://www.mindspore.cn/tutorial/training/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Inference](https://www.mindspore.cn/tutorial/inference/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Mobile Phone&IoT](https://www.mindspore.cn/tutorial/lite/en/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-Training](https://www.mindspore.cn/tutorial/training/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Inference](https://www.mindspore.cn/tutorial/inference/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Mobile Phone&IoT](https://www.mindspore.cn/tutorial/lite/en/r1.1/index.html) &nbsp;&nbsp; [1.0.1-Training](https://www.mindspore.cn/tutorial/training/en/r1.0/index.html) &nbsp;&nbsp; [1.0.1-Inference](https://www.mindspore.cn/tutorial/inference/en/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.0-Mobile Phone&IoT](https://www.mindspore.cn/tutorial/lite/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/tutorial/en/r0.7/index.html) &nbsp;&nbsp; [0.7.0-beta-Mobile Phone&IoT](https://www.mindspore.cn/lite/tutorial/en/r0.7/index.html) &nbsp;&nbsp; [0.6.0-beta](https://www.mindspore.cn/tutorial/en/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/tutorial/en/r0.5/index.html) &nbsp;&nbsp; <br> [0.3.0-alpha](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/tutorial/en/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/tutorial/en/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/tutorials/en/master/index.html) |
-| Docs |MindSpore  | [1.5.0](https://www.mindspore.cn/docs/programming_guide/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/programming_guide/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1-Programming Guide](https://www.mindspore.cn/doc/programming_guide/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Design&Specification](https://www.mindspore.cn/doc/note/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-FAQ](https://www.mindspore.cn/doc/faq/en/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-Programming Guide](https://www.mindspore.cn/doc/programming_guide/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Design&Specification](https://www.mindspore.cn/doc/note/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-FAQ](https://www.mindspore.cn/doc/faq/en/r1.1/index.html) &nbsp;&nbsp; [1.0.1-Programming Guide](https://www.mindspore.cn/doc/programming_guide/en/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.1-Design&Specification](https://www.mindspore.cn/doc/note/en/r1.0/index.html) &nbsp;&nbsp; [1.0.1-FAQ](https://www.mindspore.cn/doc/faq/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/docs/en/r0.7/index.html) &nbsp;&nbsp; [0.6.0-beta](https://www.mindspore.cn/docs/en/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/docs/en/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/docs/en/0.3.0-alpha/index.html) &nbsp;&nbsp; <br> [0.2.0-alpha](https://www.mindspore.cn/docs/en/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/docs/en/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/docs/programming_guide/en/master/index.html) |
-| |MindSpore Lite  | [1.5.0](https://www.mindspore.cn/lite/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/docs/en/r1.3/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/docs/en/r0.7/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/docs/en/master/index.html) |
-| |MindInsight  | [1.5.0](https://www.mindspore.cn/mindinsight/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindinsight/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindinsight/docs/en/master/index.html) |
-| |MindSpore Hub  | [1.5.0](https://www.mindspore.cn/hub/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/en/master/index.html) |
-| |MindArmour  | [1.5.0](https://www.mindspore.cn/mindarmour/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/en/master/index.html) |
-| |MindSpore Federated  | [1.5.0](https://www.mindspore.cn/federated/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/en/master/index.html) |
-| |MindSpore Serving  | [1.5.0](https://www.mindspore.cn/serving/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/en/master/index.html) |
-| |MindSpore Probability  | [1.5.0](https://www.mindspore.cn/probability/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/en/master/index.html) |
-| |MindQuantum  | [0.3.1](https://www.mindspore.cn/mindquantum/docs/en/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/docs/en/r0.2/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/en/master/index.html) |
+| Releasenotes and API Updates | | [1.6.0](https://gitee.com/mindspore/mindspore/blob/r1.6/RELEASE.md) &nbsp;&nbsp; [1.5.2](https://gitee.com/mindspore/mindspore/blob/r1.5/RELEASE.md) &nbsp;&nbsp; [1.3.0](https://gitee.com/mindspore/mindspore/blob/r1.3/RELEASE.md) &nbsp;&nbsp; [1.2.1](https://gitee.com/mindspore/mindspore/blob/r1.2/RELEASE.md) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/mindspore/blob/r1.1/RELEASE.md) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/mindspore/blob/r1.0/RELEASE.md) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.7/RELEASE.md) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.6/RELEASE.md) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.2/RELEASE.md) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.1/RELEASE.md) |
+| Installation | | [1.6.0](https://gitee.com/mindspore/docs/tree/r1.6/install) &nbsp;&nbsp; [1.5.2](https://gitee.com/mindspore/docs/tree/r1.5/install) &nbsp;&nbsp; [1.3.0](https://gitee.com/mindspore/docs/tree/r1.3/install) &nbsp;&nbsp; [1.2.1](https://gitee.com/mindspore/docs/tree/r1.2/install) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/docs/tree/r1.1/install) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/docs/tree/r1.0/install) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/docs/tree/r0.7/install) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/docs/tree/r0.6/install) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/docs/tree/r0.5/install) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/docs/tree/r0.3/install) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/docs/tree/r0.2/install) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/docs/tree/r0.1/install) |
+| Tutorials |  | [1.6.0](https://www.mindspore.cn/tutorials/en/r1.6/index.html) &nbsp;&nbsp; [1.5.2](https://www.mindspore.cn/tutorials/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/tutorials/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1-Training](https://www.mindspore.cn/tutorial/training/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Inference](https://www.mindspore.cn/tutorial/inference/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Mobile Phone&IoT](https://www.mindspore.cn/tutorial/lite/en/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-Training](https://www.mindspore.cn/tutorial/training/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Inference](https://www.mindspore.cn/tutorial/inference/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Mobile Phone&IoT](https://www.mindspore.cn/tutorial/lite/en/r1.1/index.html) &nbsp;&nbsp; [1.0.1-Training](https://www.mindspore.cn/tutorial/training/en/r1.0/index.html) &nbsp;&nbsp; [1.0.1-Inference](https://www.mindspore.cn/tutorial/inference/en/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.0-Mobile Phone&IoT](https://www.mindspore.cn/tutorial/lite/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/tutorial/en/r0.7/index.html) &nbsp;&nbsp; [0.7.0-beta-Mobile Phone&IoT](https://www.mindspore.cn/lite/tutorial/en/r0.7/index.html) &nbsp;&nbsp; [0.6.0-beta](https://www.mindspore.cn/tutorial/en/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/tutorial/en/r0.5/index.html) &nbsp;&nbsp; <br> [0.3.0-alpha](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/tutorial/en/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/tutorial/en/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/tutorials/en/master/index.html) |
+| Docs |MindSpore  | [1.6.0](https://www.mindspore.cn/docs/programming_guide/en/r1.6/index.html) &nbsp;&nbsp; [1.5.2](https://www.mindspore.cn/docs/programming_guide/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/programming_guide/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1-Programming Guide](https://www.mindspore.cn/doc/programming_guide/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Design&Specification](https://www.mindspore.cn/doc/note/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-FAQ](https://www.mindspore.cn/doc/faq/en/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-Programming Guide](https://www.mindspore.cn/doc/programming_guide/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Design&Specification](https://www.mindspore.cn/doc/note/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-FAQ](https://www.mindspore.cn/doc/faq/en/r1.1/index.html) &nbsp;&nbsp; [1.0.1-Programming Guide](https://www.mindspore.cn/doc/programming_guide/en/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.1-Design&Specification](https://www.mindspore.cn/doc/note/en/r1.0/index.html) &nbsp;&nbsp; [1.0.1-FAQ](https://www.mindspore.cn/doc/faq/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/docs/en/r0.7/index.html) &nbsp;&nbsp; [0.6.0-beta](https://www.mindspore.cn/docs/en/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/docs/en/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/docs/en/0.3.0-alpha/index.html) &nbsp;&nbsp; <br> [0.2.0-alpha](https://www.mindspore.cn/docs/en/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/docs/en/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/docs/programming_guide/en/master/index.html) |
+| |MindSpore Lite  | [1.6.0](https://www.mindspore.cn/lite/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/lite/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/docs/en/r1.3/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/docs/en/r0.7/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/docs/en/master/index.html) |
+| |MindInsight  | [1.6.0](https://www.mindspore.cn/mindinsight/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/mindinsight/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindinsight/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindinsight/docs/en/master/index.html) |
+| |MindSpore Hub  | [1.6.0](https://www.mindspore.cn/hub/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/hub/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/en/master/index.html) |
+| |MindArmour  | [1.6.0](https://www.mindspore.cn/mindarmour/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/mindarmour/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/en/master/index.html) |
+| |MindSpore Federated  | [1.6.0](https://www.mindspore.cn/federated/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/federated/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/en/master/index.html) |
+| |MindSpore Serving  | [1.6.0](https://www.mindspore.cn/serving/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/serving/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/en/master/index.html) |
+| |MindSpore Probability  | [1.6.0](https://www.mindspore.cn/probability/docs/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/probability/docs/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/docs/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/en/master/index.html) |
+| |MindQuantum  | [0.5.0-rc1](https://www.mindspore.cn/mindquantum/docs/en/r0.5/index.html) &nbsp;&nbsp; [0.3.1](https://www.mindspore.cn/mindquantum/docs/en/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/docs/en/r0.2/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/en/master/index.html) |
 | |MindSpore XAI  | [1.5.0](https://www.mindspore.cn/xai/docs/en/r1.5/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/xai/docs/en/master/index.html) |
 | |MindScience(MindElec and MindSPONGE)  | [0.1.0](https://www.mindspore.cn/mindscience/docs/en/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindscience/docs/en/master/index.html) |
-| |MindSpore Reinforcement | [0.1.0](https://www.mindspore.cn/reinforcement/docs/en/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/en/master/index.html) |
-| API |MindSpore  | [1.5.0](https://www.mindspore.cn/docs/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/en/r1.2/index.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/index.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/en/r0.7/index.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/en/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/api/en/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/en/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/en/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/en/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/docs/api/en/master/index.html) |
-| |MindSpore Lite  | [1.5.0](https://www.mindspore.cn/lite/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1-C++](https://www.mindspore.cn/doc/api_cpp/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Java](https://www.mindspore.cn/doc/api_java/en/r1.2/index.html) &nbsp;&nbsp; [1.1.1-C++](https://www.mindspore.cn/doc/api_cpp/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Java](https://www.mindspore.cn/doc/api_java/en/r1.1/index.html) &nbsp;&nbsp; <br> [1.0.0](https://www.mindspore.cn/doc/api_cpp/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/apic/en/r0.7/lite/namespacemembers.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/api/en/master/index.html) |
-| |MindSpore Hub  | [1.5.0](https://www.mindspore.cn/hub/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.0](https://www.mindspore.cn/doc/api_python/en/r1.2/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/en/r1.0/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/en/master/hub.html) |
-| |MindArmour  | [1.5.0](https://www.mindspore.cn/mindarmour/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/en/r1.2/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/en/r1.0/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/en/r0.7/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/en/r0.6/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.5.0-beta](https://www.mindspore.cn/api/en/r0.5/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/en/0.2.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/en/0.1.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/en/master/mindarmour.html) |
-| |MindSpore Federated  | [1.5.0](https://www.mindspore.cn/federated/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/api/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/en/master/federated_server.html) |
-| |MindSpore Serving | [1.5.0](https://www.mindspore.cn/serving/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/en/r1.2/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/en/master/server.html) |
-| |MindSpore Probability  | [1.5.0](https://www.mindspore.cn/probability/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/api/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/en/master/mindspore.nn.probability.html) |
-| |MindQuantum | [0.3.1](https://www.mindspore.cn/mindquantum/api/en/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/api/en/r0.2/index.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/doc/api_python/en/r1.2/mindquantum/mindquantum.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/en/master/mindquantum.core.html) |
+| |MindSpore Reinforcement | [0.2.0](https://www.mindspore.cn/reinforcement/docs/en/r0.2/index.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/reinforcement/docs/en/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/en/master/index.html) |
+| API |MindSpore  | [1.6.0](https://www.mindspore.cn/docs/api/en/r1.6/index.html) &nbsp;&nbsp; [1.5.2](https://www.mindspore.cn/docs/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/en/r1.2/index.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/index.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/en/r0.7/index.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/en/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/api/en/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/en/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/en/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/en/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/docs/api/en/master/index.html) |
+| |MindSpore Lite  | [1.6.0](https://www.mindspore.cn/lite/api/en/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/lite/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1-C++](https://www.mindspore.cn/doc/api_cpp/en/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Java](https://www.mindspore.cn/doc/api_java/en/r1.2/index.html) &nbsp;&nbsp; [1.1.1-C++](https://www.mindspore.cn/doc/api_cpp/en/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Java](https://www.mindspore.cn/doc/api_java/en/r1.1/index.html) &nbsp;&nbsp; <br> [1.0.0](https://www.mindspore.cn/doc/api_cpp/en/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/apic/en/r0.7/lite/namespacemembers.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/api/en/master/index.html) |
+| |MindSpore Hub  | [1.6.0](https://www.mindspore.cn/hub/docs/en/r1.6/hub.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/hub/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.0](https://www.mindspore.cn/doc/api_python/en/r1.2/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/en/r1.0/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/en/master/hub.html) |
+| |MindArmour  | [1.6.0](https://www.mindspore.cn/mindarmour/docs/en/r1.6/mindarmour.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/mindarmour/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/en/r1.2/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/en/r1.0/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/en/r0.7/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/en/r0.6/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.5.0-beta](https://www.mindspore.cn/api/en/r0.5/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/en/0.2.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/en/0.1.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/en/master/mindarmour.html) |
+| |MindSpore Federated  | [1.6.0](https://www.mindspore.cn/federated/docs/en/r1.6/federated_server.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/federated/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/api/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/en/master/federated_server.html) |
+| |MindSpore Serving | [1.6.0](https://www.mindspore.cn/serving/docs/en/r1.6/server.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/serving/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/api/en/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/en/r1.2/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/en/master/server.html) |
+| |MindSpore Probability  | [1.6.0](https://www.mindspore.cn/probability/docs/en/r1.6/mindspore.nn.probability.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/probability/api/en/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/api/en/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/en/master/mindspore.nn.probability.html) |
+| |MindQuantum | [0.5.0-rc1](https://www.mindspore.cn/mindquantum/docs/en/r0.5/mindquantum.core.html) &nbsp;&nbsp; [[0.3.1](https://www.mindspore.cn/mindquantum/api/en/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/api/en/r0.2/index.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/doc/api_python/en/r1.2/mindquantum/mindquantum.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/en/master/mindquantum.core.html) |
 | |MindSpore XAI  | [1.5.0](https://www.mindspore.cn/xai/api/en/r1.5/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/xai/docs/en/master/mindspore_xai.runner.html) |
 | |MindScience(MindElec and MindSPONGE)  | [0.1.0]((https://www.mindspore.cn/mindscience/api/en/r0.1/index.html)) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindscience/docs/en/master/mindsponge.html) |
-| |MindSpore Reinforcement | [0.1.0](https://www.mindspore.cn/reinforcement/api/en/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/en/master/reinforcement.html) |
+| |MindSpore Reinforcement | [0.2.0](https://www.mindspore.cn/reinforcement/docs/en/r0.2/reinforcement.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/reinforcement/api/en/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/en/master/reinforcement.html) |
 
 ## Ascend Supporting Software Package
 
 |    MindSpore Version    |    Hardware Platform    |    Software Package Documentation    |    Driver    |    Firmware    |    CANN    |
 |    ---    |    ---    |    ---    |    ---    |    ---    |    ---    |
-| 1.5.0 | Ascend 910 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A800-9000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434192) or [A800-9010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434160) | [A800-9000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434174) or [A800-9010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434163) | [ARM  platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) or [x86 platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
-|  | Ascend 310 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A300-3000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432511) or [A300-3010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432524) | [A300-3000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432514) or [A300-3010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432529) | [ARM  platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) or [x86 platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
+| 1.6.0 | Ascend 910 | Ascend Data Center Solution 21.0.4 | A800-9000 ARM platform or A800-9010 x86 platform | A800-9000 ARM platform or A800-9010 x86 platform | ARM platform or x86 platform |
+|  | Ascend 310 | Ascend Data Center Solution 21.0.4 | A300-3000 ARM platform or A300-3010 x86 platform | A300-3000 ARM platform or A300-3010 x86 platform | ARM platform or x86 platform |
+| 1.5.2 | Ascend 910 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A800-9000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434192) or [A800-9010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434160) | [A800-9000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434174) or [A800-9010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434163) | [ARM platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) or [x86 platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
+|  | Ascend 310 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A300-3000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432511) or [A300-3010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432524) | [A300-3000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432514) or [A300-3010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432529) | [ARM platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) or [x86 platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
+| 1.5.0 | Ascend 910 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A800-9000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434192) or [A800-9010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434160) | [A800-9000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434174) or [A800-9010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434163) | [ARM platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) or [x86 platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
+|  | Ascend 310 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A300-3000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432511) or [A300-3010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432524) | [A300-3000 ARM platform](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432514) or [A300-3010 x86 platform](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432529) | [ARM platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) or [x86 platform](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
 | 1.3.0 | Ascend 910 | [CANN 5.0.2 Software Installation Guide](https://support.huawei.com/enterprise/en/doc/EDOC1100206655) | [A800-9000 1.0.11.SCP001 ARM platform](https://support.huawei.com/enterprise/zh/ascend-computing/a800-9000-pid-250702818/software/253882155?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702818) or [A800-9010 1.0.11.SCP001 x86 platform](https://support.huawei.com/enterprise/zh/ascend-computing/a800-9010-pid-250702809/software/253882161?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702809) |  | [CANN 5.0.2.1](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373/software/253944991?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C251168373) |
 || Ascend 310 | [CANN 5.0.2 Software Installation Guide](https://support.huawei.com/enterprise/en/doc/EDOC1100206655) | [A300-3000 1.0.11 ARM platform](https://support.huawei.com/enterprise/zh/ascend-computing/a300-3000-pid-250702915/software/253276345?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702915) or [A300-3010 1.0.11 x86 platform](https://support.huawei.com/enterprise/zh/ascend-computing/a300-3010-pid-251560253/software/253276357?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C251560253) |  | [CANN 5.0.2.1](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373/software/253944991?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C251168373) |
 | 1.2.1  | Ascend 910 | [Ascend Data Center Solution 21.0.1.SPC001](https://support.huawei.com/enterprise/zh/ascend-computing/ascend-data-center-solution-pid-251167910) | [A800-9000 1.0.10 ARM platform](https://support.huawei.com/enterprise/zh/ascend-computing/a800-9000-pid-250702818/software/252727249?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702818) or [A800-9010 1.0.10 x86 platform](https://support.huawei.com/enterprise/zh/ascend-computing/a800-9010-pid-250702809/software/252727265?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702809) |  | [CANN 5.0.1.SPC102](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373/software/253348209?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C251168373) |
@@ -74,6 +79,43 @@
 
 ## Downloads
 
+### 1.6.0
+
+|   Module Name   |   Hardware Platform   |   Operating System   |      Python Version      |      Download Links      |        SHA-256     |
+| ----------------------- | ------------------------------ | --------------------------- | --------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| MindSpore               | Ascend 910<br>Ascend310        | Linux-aarch64               | Python3.7.5                 | [mindspore_ascend-1.6.0-cp37-cp37m-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/aarch64/mindspore_ascend-1.6.0-cp37-cp37m-linux_aarch64.whl) | 9db425ae9f8daa1eae7960e2b1b8efca63bbd0d8cf4fb0475dab2fe6c3963572 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_ascend-1.6.0-cp39-cp39-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/aarch64/mindspore_ascend-1.6.0-cp39-cp39-linux_aarch64.whl) | 2ce20877772744fe6a88c4828059b180bc848ff740158c0e25c13227a6b0862c |
+|                         |                                | Linux-x86_64                | Python3.7.5                 | [mindspore_ascend-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/x86_64/mindspore_ascend-1.6.0-cp37-cp37m-linux_x86_64.whl) | 966f0411b497273415ed6779f15d5743c08ae2eb62cef5d291b1995251857ebb |
+|                         |                                |                             | Python3.9.0                 | [mindspore_ascend-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/x86_64/mindspore_ascend-1.6.0-cp39-cp39-linux_x86_64.whl) | 79b4cc68de6a3c70bf1bd03a538441d347bba619189822fde58ccae8145c949c |
+|                         | Ascend 310-Binary              | Linux-aarch64               | Python3.7.5<br/>Python3.9.0 | [mindspore_ascend-1.6.0-linux_aarch64.tar.gz](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/aarch64/mindspore_ascend-1.6.0-linux_aarch64.tar.gz) | 3a15640b9986819055c98499eadbb7e365fa3b2a27e302bca8ab68659096b1dd |
+|                         |                                | Linux-x86_64                | Python3.7.5<br/>Python3.9.0 | [mindspore_ascend-1.6.0-linux_x86_64.tar.gz](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/x86_64/mindspore_ascend-1.6.0-linux_x86_64.tar.gz) | 2656bc3bf5cf5f9a65e2869ecc06f3e76b51792e5fe3b1d922160904312d29e6 |
+|                         | GPU CUDA 10.1                  | Linux-x86_64                | Python3.7.5                 | [mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-10.1/mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl) | a961ca60c44b21c9b35767ef6f4ebfeb8105d91d7e51fe44c687c98ee7971961 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-10.1/mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl) | 053f13c3b6575b111aa27dba5b58c8e258f8994bb07508982092769699c5740e |
+|                         | GPU CUDA 11.1                  | Linux-x86_64                | Python3.7.5                 | [mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-11.1/mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl) | e76bcc715a41c21f2be2604018569a0b02ca3c43b23defbeb5ff3da27260ca80 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-11.1/mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl) | 4719c951da345db6ce898dfa0ebc151cc2cb8af116dcbf0eab1202566af5aa6d |
+|                         | CPU                            | Linux-aarch64               | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/aarch64/mindspore-1.6.0-cp37-cp37m-linux_aarch64.whl) | 5dc6b9abe668d53960773d6e8ac4dc6f7016c15cce5c9480045c74a3e456e40f |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/aarch64/mindspore-1.6.0-cp39-cp39-linux_aarch64.whl) | 4a8f49587b30b6a0413edba85ebbae07600fc84f8a1fa48c42a2359402bfc852 |
+|                         |                                | Linux-x86_64                | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp37-cp37m-linux_x86_64.whl) | b4fe66629150c47397722057c32c806cd3eece5e158a93c62cac0bc03b464e3f |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp39-cp39-linux_x86_64.whl) | 87151059854e7388c6b5d6d56a7b75087f171efdcd84896c61d0e9088fcebcc0 |
+|                         |                                | Windows-x64                 | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp37-cp37m-win_amd64.whl) | bff5df6a20f98235135693dbcc2928006e0bc080d65faa1bafe0193b67c05051 |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp39-cp39-win_amd64.whl) | 51bc1079f5e5f1fe75b9b1e1022756dd2628b39d8c4c2b872bc33e6e14ecf5bf |
+|                         |                                | MacOS-aarch64               | Python3.9.1                 | [mindspore-1.6.0-cp39-cp39-macosx_11_0_arm64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/aarch64/mindspore-1.6.0-cp39-cp39-macosx_11_0_arm64.whl) | dead04b7b7d3168f0f5837d43ec18fb65c225d3834b3fce8fdc6e3e117218800 |
+|                         |                                | MacOS-x64                   | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-macosx_10_15_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp37-cp37m-macosx_10_15_x86_64.whl) | cafd82348d00a86673c72f74c3990861a0bae54080b19de29cd55767445729f7 |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-macosx_10_15_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp39-cp39-macosx_10_15_x86_64.whl) | c2c95d03deb197b7ee4bb7c1d94e2ead7d213d791c016083d23c9710749678ca |
+| MindSpore<br>Serving    | Ascend 910<br>Ascend310<br>GPU | Linux-aarch64               | Python3.7.5                 | [mindspore_serving-1.6.0-cp37-cp37m-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/aarch64/mindspore_serving-1.6.0-cp37-cp37m-linux_aarch64.whl) | 55455e5e2d0807d5d64dee5cf8d701fe0afdc3151d697509f8211607944ce74c |
+|                         |                                |                             | Python3.9.0                 | [mindspore_serving-1.6.0-cp39-cp39-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/aarch64/mindspore_serving-1.6.0-cp39-cp39-linux_aarch64.whl) | ce9868ba280901edb8c9438106cca8eab31e6289e09aaf221a3445ed67956b43 |
+|                         |                                | Linux-x86_64                | Python3.7.5                 | [mindspore_serving-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/x86_64/mindspore_serving-1.6.0-cp37-cp37m-linux_x86_64.whl) | 62cbf148a1bd5f96d92406052b5e1c45d2b1dfcdeeb73dde3c487a43aa1d6a31 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_serving-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/x86_64/mindspore_serving-1.6.0-cp39-cp39-linux_x86_64.whl) | a2be90bfc2b8800137bb5db379dfbfd3e5b17653b092f5859345195602274449 |
+| MindInsight             |                                | any                         | Python3                     | [mindinsight-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindInsight/any/mindinsight-1.6.0-py3-none-any.whl) | 8e8b002fa919e2da11cd2b1771233ae951e3eddd8fba0d346f18d8c881831ec9 |
+| MindConverter           |                                | any                         | Python3                     | [mindconverter-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindInsight/any/mindconverter-1.6.0-py3-none-any.whl) | 5b720b6efd6dfe4c15056c1195297ae07d4ffeeb383bad53decdfa84f5d98526 |
+| MindArmour              |                                | any                         | Python3                     | [mindarmour-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindArmour/any/mindarmour-1.6.0-py3-none-any.whl) | 1e26f0a4eb5ba79b3facbe9e3677bda1227cd5a642a3b697bfe99f33d3165ae8 |
+| MindSpore<br>Hub        |                                | any                         | Python3                     | [mindspore_hub-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Hub/any/mindspore_hub-1.6.0-py3-none-any.whl) | 3185a83ac660156f09b40fe6c8c8f9d57852c3d0e68ff130341106deb8020a8a |
+| MindQuantum             |                                | Linux-x86_64                | Python3.7.5                 | [mindquantum-0.5.0rc1-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindQuantum/x86_64/mindquantum-0.5.0rc1-cp37-cp37m-linux_x86_64.whl) | 2ee5346249ba7608bd67c3cbe2ab47177581389f6db783156c076d1f11251756 |
+|                         |                                | Windows-x64                 | Python3.7.5                 | [mindquantum-0.5.0rc1-cp37-cp37m-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindQuantum/x86_64/mindquantum-0.5.0rc1-cp37-cp37m-win_amd64.whl) | ffb0cc9ff182f7f4df46db110712efc151c1ea4f79e5526d609e140490db8757 |
+|                         |                                |                             | Python3.9.0                 | [mindquantum-0.5.0rc1-cp39-cp39-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindQuantum/x86_64/mindquantum-0.5.0rc1-cp39-cp39-win_amd64.whl) | 4043fbfd191c1fb96c6c709b4afa49accae48290e01e7260f5f585a23745ed80 |
+| MindSpore Reinforcement |                                | any                         | Python3                     | [mindspore_rl-0.2.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Reinforcement/any/mindspore_rl-0.2.0-py3-none-any.whl) | a1461746e27e8532a8b00bf16a9e8b63fdc2412bdeaf769c33b650a80154708a |
+| Ide-Plugin              |                                | Linux-x86_64<br>Windows-x64 | any                         | [MindSpore_Dev_ToolKit-1.6.0.zip](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/IdePlugin/any/MindSpore_Dev_ToolKit-1.6.0.zip) | 835e14f4b2c85118f54512348edc9fc3239c198dd1dbd6c9441843154b330d40 |
+
 ### 1.5.2
 
 |   Module Name   |   Hardware Platform   |   Operating System   |      Python Version      |      Download Links      |        SHA-256     |
diff --git a/resource/release/release_list_zh_cn.md b/resource/release/release_list_zh_cn.md
index 4e75dae786ab0e296c311f06592297be71f312d7..b3335c731d1edcd40fb6e418a2bb72c3b9186599 100644
--- a/resource/release/release_list_zh_cn.md
+++ b/resource/release/release_list_zh_cn.md
@@ -4,6 +4,7 @@
     - [配套资料](#配套资料)
     - [Ascend配套软件包](#ascend配套软件包)
     - [下载地址](#下载地址)
+        - [1.6.0](#160)
         - [1.5.2](#152)
         - [1.5.1](#151)
         - [1.5.0](#150)
@@ -32,37 +33,41 @@
 
 |   类别   |   子类别   |      版本      |
 |    ---   |    ---   |       ---      |
-| 版本说明和接口变更 | | [1.5.0](https://gitee.com/mindspore/mindspore/blob/r1.5/RELEASE.md) &nbsp;&nbsp; [1.3.0](https://gitee.com/mindspore/mindspore/blob/r1.3/RELEASE.md) &nbsp;&nbsp;  [1.2.1](https://gitee.com/mindspore/mindspore/blob/r1.2/RELEASE.md) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/mindspore/blob/r1.1/RELEASE.md) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/mindspore/blob/r1.0/RELEASE.md) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.7/RELEASE.md) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.6/RELEASE.md) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.2/RELEASE.md) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.1/RELEASE.md)|
-| 安装 | | [1.5.0](https://gitee.com/mindspore/docs/tree/r1.5/install) &nbsp;&nbsp;  [1.3.0](https://gitee.com/mindspore/docs/tree/r1.3/install) &nbsp;&nbsp; [1.2.1](https://gitee.com/mindspore/docs/tree/r1.2/install) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/docs/tree/r1.1/install) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/docs/tree/r1.0/install) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/docs/tree/r0.7/install) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/docs/tree/r0.6/install) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/docs/tree/r0.5/install) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/docs/tree/r0.3/install) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/docs/tree/r0.2/install) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/docs/tree/r0.1/install)|
-| 教程 |  | [1.5.0](https://www.mindspore.cn/tutorials/zh-CN/r1.5/index.html) &nbsp;&nbsp;  [1.3.0](https://www.mindspore.cn/tutorials/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1-训练](https://www.mindspore.cn/tutorial/training/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-推理](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.2/index.html) &nbsp;&nbsp;[1.2.1-手机&IoT](https://www.mindspore.cn/tutorial/lite/zh-CN/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-训练](https://www.mindspore.cn/tutorial/training/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-推理](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-手机&IoT](https://www.mindspore.cn/tutorial/lite/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.0.1-训练](https://www.mindspore.cn/tutorial/training/zh-CN/r1.0/index.html) &nbsp;&nbsp; [1.0.1-推理](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.0-手机&IoT](https://www.mindspore.cn/tutorial/lite/zh-CN/r1.0/index.html)&nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/tutorial/zh-CN/r0.7/index.html) &nbsp;&nbsp; [0.7.0-beta手机&IoT](https://www.mindspore.cn/lite/tutorial/zh-CN/r0.7/index.html) &nbsp;&nbsp; [0.6.0-beta](https://www.mindspore.cn/tutorial/zh-CN/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/tutorial/zh-CN/r0.5/index.html) &nbsp;&nbsp; <br> [0.3.0-alpha](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/tutorial/zh-CN/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/tutorial/zh-CN/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/tutorials/zh-CN/master/index.html) |
-| 文档 |MindSpore  | [1.5.0](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1-编程指南](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-设计和规格](https://www.mindspore.cn/doc/note/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-FAQ](https://www.mindspore.cn/doc/faq/zh-CN/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-编程指南](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-设计和规格](https://www.mindspore.cn/doc/note/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-FAQ](https://www.mindspore.cn/doc/faq/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.0.1-编程指南](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.0/index.html) &nbsp;&nbsp; [1.0.1-设计和规格](https://www.mindspore.cn/doc/note/zh-CN/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.1-FAQ](https://www.mindspore.cn/doc/faq/zh-CN/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/docs/zh-CN/r0.7/index.html) &nbsp;&nbsp;  [0.6.0-beta](https://www.mindspore.cn/docs/zh-CN/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/docs/zh-CN/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/docs/zh-CN/0.3.0-alpha/index.html) &nbsp;&nbsp; <br> [0.2.0-alpha](https://www.mindspore.cn/docs/zh-CN/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/docs/zh-CN/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/index.html) |
-| |MindSpore Lite  | [1.5.0](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/docs/zh-CN/r0.7/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/docs/zh-CN/master/index.html) |
-| |MindInsight  | [1.5.0](https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/index.html) |
-| |MindSpore Hub  | [1.5.0](https://www.mindspore.cn/hub/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/zh-CN/master/index.html) |
-| |MindArmour  | [1.5.0](https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/zh-CN/master/index.html) |
-| |MindSpore Federated  | [1.5.0](https://www.mindspore.cn/federated/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/zh-CN/master/index.html) |
-| |MindSpore Serving  | [1.5.0](https://www.mindspore.cn/serving/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/zh-CN/master/index.html) |
-| |MindSpore Probability  | [1.5.0](https://www.mindspore.cn/probability/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/zh-CN/master/index.html) |
-| |MindQuantum  | [0.3.1](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.2/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/index.html) |
+| 版本说明和接口变更 | | [1.6.0](https://gitee.com/mindspore/mindspore/blob/r1.6/RELEASE.md) &nbsp;&nbsp; [1.5.2](https://gitee.com/mindspore/mindspore/blob/r1.5/RELEASE.md) &nbsp;&nbsp; [1.3.0](https://gitee.com/mindspore/mindspore/blob/r1.3/RELEASE.md) &nbsp;&nbsp;  [1.2.1](https://gitee.com/mindspore/mindspore/blob/r1.2/RELEASE.md) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/mindspore/blob/r1.1/RELEASE.md) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/mindspore/blob/r1.0/RELEASE.md) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.7/RELEASE.md) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/mindspore/blob/r0.6/RELEASE.md) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.2/RELEASE.md) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/mindspore/blob/r0.1/RELEASE.md)|
+| 安装 | | [1.6.0](https://gitee.com/mindspore/docs/tree/r1.6/install) &nbsp;&nbsp; [1.5.2](https://gitee.com/mindspore/docs/tree/r1.5/install) &nbsp;&nbsp;  [1.3.0](https://gitee.com/mindspore/docs/tree/r1.3/install) &nbsp;&nbsp; [1.2.1](https://gitee.com/mindspore/docs/tree/r1.2/install) &nbsp;&nbsp; [1.1.1](https://gitee.com/mindspore/docs/tree/r1.1/install) &nbsp;&nbsp; [1.0.1](https://gitee.com/mindspore/docs/tree/r1.0/install) &nbsp;&nbsp; [0.7.0-beta](https://gitee.com/mindspore/docs/tree/r0.7/install) &nbsp;&nbsp; <br> [0.6.0-beta](https://gitee.com/mindspore/docs/tree/r0.6/install) &nbsp;&nbsp; [0.5.2-beta](https://gitee.com/mindspore/docs/tree/r0.5/install) &nbsp;&nbsp; [0.3.0-alpha](https://gitee.com/mindspore/docs/tree/r0.3/install) &nbsp;&nbsp; [0.2.0-alpha](https://gitee.com/mindspore/docs/tree/r0.2/install) &nbsp;&nbsp; [0.1.0-alpha](https://gitee.com/mindspore/docs/tree/r0.1/install)|
+| 教程 |  | [1.6.0](https://www.mindspore.cn/tutorials/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.2](https://www.mindspore.cn/tutorials/zh-CN/r1.5/index.html) &nbsp;&nbsp;  [1.3.0](https://www.mindspore.cn/tutorials/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1-训练](https://www.mindspore.cn/tutorial/training/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-推理](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.2/index.html) &nbsp;&nbsp;[1.2.1-手机&IoT](https://www.mindspore.cn/tutorial/lite/zh-CN/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-训练](https://www.mindspore.cn/tutorial/training/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-推理](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-手机&IoT](https://www.mindspore.cn/tutorial/lite/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.0.1-训练](https://www.mindspore.cn/tutorial/training/zh-CN/r1.0/index.html) &nbsp;&nbsp; [1.0.1-推理](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.0-手机&IoT](https://www.mindspore.cn/tutorial/lite/zh-CN/r1.0/index.html)&nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/tutorial/zh-CN/r0.7/index.html) &nbsp;&nbsp; [0.7.0-beta手机&IoT](https://www.mindspore.cn/lite/tutorial/zh-CN/r0.7/index.html) &nbsp;&nbsp; [0.6.0-beta](https://www.mindspore.cn/tutorial/zh-CN/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/tutorial/zh-CN/r0.5/index.html) &nbsp;&nbsp; <br> [0.3.0-alpha](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/tutorial/zh-CN/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/tutorial/zh-CN/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/tutorials/zh-CN/master/index.html) |
+| 文档 |MindSpore  | [1.6.0](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.2](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1-编程指南](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-设计和规格](https://www.mindspore.cn/doc/note/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-FAQ](https://www.mindspore.cn/doc/faq/zh-CN/r1.2/index.html) &nbsp;&nbsp; <br> [1.1.1-编程指南](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-设计和规格](https://www.mindspore.cn/doc/note/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-FAQ](https://www.mindspore.cn/doc/faq/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.0.1-编程指南](https://www.mindspore.cn/doc/programming_guide/zh-CN/r1.0/index.html) &nbsp;&nbsp; [1.0.1-设计和规格](https://www.mindspore.cn/doc/note/zh-CN/r1.0/index.html) &nbsp;&nbsp; <br> [1.0.1-FAQ](https://www.mindspore.cn/doc/faq/zh-CN/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/docs/zh-CN/r0.7/index.html) &nbsp;&nbsp;  [0.6.0-beta](https://www.mindspore.cn/docs/zh-CN/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/docs/zh-CN/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/docs/zh-CN/0.3.0-alpha/index.html) &nbsp;&nbsp; <br> [0.2.0-alpha](https://www.mindspore.cn/docs/zh-CN/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/docs/zh-CN/0.1.0-alpha/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/index.html) |
+| |MindSpore Lite  | [1.6.0](https://www.mindspore.cn/lite/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/lite/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/docs/zh-CN/r0.7/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/docs/zh-CN/master/index.html) |
+| |MindInsight  | [1.6.0](https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindinsight/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindinsight/docs/zh-CN/master/index.html) |
+| |MindSpore Hub  | [1.6.0](https://www.mindspore.cn/hub/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/hub/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/zh-CN/master/index.html) |
+| |MindArmour  | [1.6.0](https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/zh-CN/master/index.html) |
+| |MindSpore Federated  | [1.6.0](https://www.mindspore.cn/federated/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/federated/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/zh-CN/master/index.html) |
+| |MindSpore Serving  | [1.6.0](https://www.mindspore.cn/serving/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/serving/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/zh-CN/master/index.html) |
+| |MindSpore Probability  | [1.6.0](https://www.mindspore.cn/probability/docs/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/probability/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/docs/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/zh-CN/master/index.html) |
+| |MindQuantum  | [0.5.0-rc1](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.5/index.html) &nbsp;&nbsp; [0.3.1](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.2/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/index.html) |
 | |MindSpore XAI  | [1.5.0](https://www.mindspore.cn/xai/docs/zh-CN/r1.5/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/xai/docs/zh-CN/master/index.html) |
 | |MindScience(MindElec and MindSPONGE)  | [0.1.0](https://www.mindspore.cn/mindscience/docs/zh-CN/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindscience/docs/zh-CN/master/index.html) |
-| |MindSpore Reinforcement | [0.1.0](https://www.mindspore.cn/reinforcement/docs/zh-CN/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/zh-CN/master/index.html) |
-| API |MindSpore  | [1.5.0](https://www.mindspore.cn/docs/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/zh-CN/r0.7/index.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/zh-CN/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/api/zh-CN/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/zh-CN/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/zh-CN/0.1.0-alpha/index.html)&nbsp;&nbsp; [master](https://www.mindspore.cn/docs/api/zh-CN/master/index.html) |
-| |MindSpore Lite  | [1.5.0](https://www.mindspore.cn/lite/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1-C++](https://www.mindspore.cn/doc/api_cpp/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Java](https://www.mindspore.cn/doc/api_java/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.1.1-C++](https://www.mindspore.cn/doc/api_cpp/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Java](https://www.mindspore.cn/doc/api_java/zh-CN/r1.1/index.html) &nbsp;&nbsp; <br> [1.0.0](https://www.mindspore.cn/doc/api_cpp/zh-CN/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/apic/zh-CN/r0.7/lite/namespacemembers.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/api/zh-CN/master/index.html) |
-| |MindSpore Hub  | [1.5.0](https://www.mindspore.cn/hub/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.0](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.0/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/zh-CN/master/hub.html) |
-| |MindArmour  | [1.5.0](https://www.mindspore.cn/mindarmour/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.0/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/zh-CN/r0.7/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/zh-CN/r0.6/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.5.0-beta](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/zh-CN/0.2.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/zh-CN/0.1.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/zh-CN/master/mindarmour.html) |
-| |MindSpore Federated  | [1.5.0](https://www.mindspore.cn/federated/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/zh-CN/master/federated_server.html) |
-| |MindSpore Serving | [1.5.0](https://www.mindspore.cn/serving/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/zh-CN/master/server.html) |
-| |MindSpore Probability  | [1.5.0](https://www.mindspore.cn/probability/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/zh-CN/master/mindspore.nn.probability.html) |
-| |MindQuantum | [0.3.1](https://www.mindspore.cn/mindquantum/api/zh-CN/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/api/zh-CN/r0.2/index.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindquantum/mindquantum.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.html) |
+| |MindSpore Reinforcement | [0.2.0](https://www.mindspore.cn/reinforcement/docs/zh-CN/r0.2/index.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/reinforcement/docs/zh-CN/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/zh-CN/master/index.html) |
+| API |MindSpore  | [1.6.0](https://www.mindspore.cn/docs/api/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.2](https://www.mindspore.cn/docs/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/docs/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/zh-CN/r0.7/index.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/zh-CN/r0.6/index.html) &nbsp;&nbsp; [0.5.2-beta](https://www.mindspore.cn/api/zh-CN/r0.5/index.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/index.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/zh-CN/0.2.0-alpha/index.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/zh-CN/0.1.0-alpha/index.html)&nbsp;&nbsp; [master](https://www.mindspore.cn/docs/api/zh-CN/master/index.html) |
+| |MindSpore Lite  | [1.6.0](https://www.mindspore.cn/lite/api/zh-CN/r1.6/index.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/lite/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/lite/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1-C++](https://www.mindspore.cn/doc/api_cpp/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.2.1-Java](https://www.mindspore.cn/doc/api_java/zh-CN/r1.2/index.html) &nbsp;&nbsp; [1.1.1-C++](https://www.mindspore.cn/doc/api_cpp/zh-CN/r1.1/index.html) &nbsp;&nbsp; [1.1.1-Java](https://www.mindspore.cn/doc/api_java/zh-CN/r1.1/index.html) &nbsp;&nbsp; <br> [1.0.0](https://www.mindspore.cn/doc/api_cpp/zh-CN/r1.0/index.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/lite/apic/zh-CN/r0.7/lite/namespacemembers.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/lite/api/zh-CN/master/index.html) |
+| |MindSpore Hub  | [1.6.0](https://www.mindspore.cn/hub/docs/zh-CN/r1.6/hub.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/hub/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/hub/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.0](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.0/mindspore_hub/mindspore_hub.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/hub/docs/zh-CN/master/hub.html) |
+| |MindArmour  | [1.6.0](https://www.mindspore.cn/mindarmour/docs/zh-CN/r1.6/mindarmour.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/mindarmour/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/mindarmour/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindarmour/mindarmour.html) &nbsp;&nbsp; [1.0.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.0/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.7.0-beta](https://www.mindspore.cn/api/zh-CN/r0.7/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; <br> [0.6.0-beta](https://www.mindspore.cn/api/zh-CN/r0.6/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.5.0-beta](https://www.mindspore.cn/api/zh-CN/r0.5/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.3.0-alpha](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.2.0-alpha](https://www.mindspore.cn/api/zh-CN/0.2.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [0.1.0-alpha](https://www.mindspore.cn/api/zh-CN/0.1.0-alpha/api/python/mindarmour/mindarmour.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindarmour/docs/zh-CN/master/mindarmour.html) |
+| |MindSpore Federated  | [1.6.0](https://www.mindspore.cn/federated/docs/zh-CN/r1.6/federated_server.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/federated/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/federated/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/federated/docs/zh-CN/master/federated_server.html) |
+| |MindSpore Serving | [1.6.0](https://www.mindspore.cn/serving/docs/zh-CN/r1.6/server.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/serving/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/serving/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [1.2.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [1.1.1](https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindspore_serving/mindspore_serving.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/serving/docs/zh-CN/master/server.html) |
+| |MindSpore Probability  | [1.6.0](https://www.mindspore.cn/probability/docs/zh-CN/r1.6/mindspore.nn.probability.html) &nbsp;&nbsp; [1.5.0](https://www.mindspore.cn/probability/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [1.3.0](https://www.mindspore.cn/probability/api/zh-CN/r1.3/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/probability/docs/zh-CN/master/mindspore.nn.probability.html) |
+| |MindQuantum | [0.5.0-rc1](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.5/mindquantum.core.html) &nbsp;&nbsp; [0.3.1](https://www.mindspore.cn/mindquantum/api/zh-CN/r0.3/index.html) &nbsp;&nbsp; [0.2.0](https://www.mindspore.cn/mindquantum/api/zh-CN/r0.2/index.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/doc/api_python/zh-CN/r1.2/mindquantum/mindquantum.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindquantum/docs/zh-CN/master/mindquantum.core.html) |
 | |MindSpore XAI  | [1.5.0](https://www.mindspore.cn/xai/api/zh-CN/r1.5/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/xai/docs/zh-CN/master/mindspore_xai.runner.html) |
 | |MindScience(MindElec and MindSPONGE)  | [0.1.0]((https://www.mindspore.cn/mindscience/api/zh-CN/r0.1/index.html)) &nbsp;&nbsp; [master](https://www.mindspore.cn/mindscience/docs/zh-CN/master/mindsponge.html) |
-| |MindSpore Reinforcement | [0.1.0](https://www.mindspore.cn/reinforcement/api/zh-CN/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/zh-CN/master/reinforcement.html) |
+| |MindSpore Reinforcement | [0.2.0](https://www.mindspore.cn/reinforcement/docs/zh-CN/r0.2/reinforcement.html) &nbsp;&nbsp; [0.1.0](https://www.mindspore.cn/reinforcement/api/zh-CN/r0.1/index.html) &nbsp;&nbsp; [master](https://www.mindspore.cn/reinforcement/docs/zh-CN/master/reinforcement.html) |
 
 ## Ascend配套软件包
 
 |    MindSpore版本    |    硬件平台    |    软件包文档    |    驱动    |    固件    |    CANN    |
 |    ---    |    ---    |    ---    |    ---    |    ---    |    ---    |
+| 1.6.0 | Ascend 910 | Ascend Data Center Solution 21.0.4 | A800-9000 ARM平台 或 A800-9010 x86平台 | A800-9000 ARM平台 或 A800-9010 x86平台 | ARM 平台 或 x86平台 |
+|  | Ascend 310 | Ascend Data Center Solution 21.0.4 | A300-3000 ARM平台 或 A300-3010 x86平台 | A300-3000 ARM平台 或 A300-3010 x86平台 | ARM 平台 或 x86平台 |
+| 1.5.2 | Ascend 910 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A800-9000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434192) 或 [A800-9010 x86平台](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434160) | [A800-9000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434174) 或 [A800-9010 x86平台](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434163) | [ARM 平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) 或 [x86平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
+|  | Ascend 310 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A300-3000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432511) 或 [A300-3010 x86平台](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432524) | [A300-3000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432514) 或 [A300-3010 x86平台](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432529) | [ARM 平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) 或 [x86平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
 | 1.5.0 | Ascend 910 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A800-9000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434192) 或 [A800-9010 x86平台](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434160) | [A800-9000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845425-ESW2000434174) 或 [A800-9010 x86平台](https://support.huawei.com/enterprise/zh/software/253845445-ESW2000434163) | [ARM 平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) 或 [x86平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
 |  | Ascend 310 | [Ascend Data Center Solution 21.0.3](https://support.huawei.com/enterprise/zh/doc/EDOC1100226552?section=j003) | [A300-3000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432511) 或 [A300-3010 x86平台](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432524) | [A300-3000 ARM平台](https://support.huawei.com/enterprise/zh/software/253845265-ESW2000432514) 或 [A300-3010 x86平台](https://support.huawei.com/enterprise/zh/software/253845285-ESW2000432529) | [ARM 平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432564) 或 [x86平台](https://support.huawei.com/enterprise/zh/software/254503547-ESW2000432567) |
 | 1.3.0 | Ascend 910 | [CANN 5.0.2 软件安装指南](https://support.huawei.com/enterprise/zh/doc/EDOC1100206656) | [A800-9000 1.0.11.SCP001 ARM平台](https://support.huawei.com/enterprise/zh/ascend-computing/a800-9000-pid-250702818/software/253882155?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702818) 或 [A800-9010 1.0.11.SCP001 x86平台](https://support.huawei.com/enterprise/zh/ascend-computing/a800-9010-pid-250702809/software/253882161?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C250702809) |  | [CANN 5.0.2.1](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373/software/253944991?idAbsPath=fixnode01%7C23710424%7C251366513%7C22892968%7C251168373) |
@@ -74,6 +79,43 @@
 
 ## 下载地址
 
+### 1.6.0
+
+| 组件                    | 硬件平台                       | 操作系统                    | Python版本                  | 链接                                                         | SHA-256                                                      |
+| ----------------------- | ------------------------------ | --------------------------- | --------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| MindSpore               | Ascend 910<br>Ascend310        | Linux-aarch64               | Python3.7.5                 | [mindspore_ascend-1.6.0-cp37-cp37m-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/aarch64/mindspore_ascend-1.6.0-cp37-cp37m-linux_aarch64.whl) | 9db425ae9f8daa1eae7960e2b1b8efca63bbd0d8cf4fb0475dab2fe6c3963572 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_ascend-1.6.0-cp39-cp39-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/aarch64/mindspore_ascend-1.6.0-cp39-cp39-linux_aarch64.whl) | 2ce20877772744fe6a88c4828059b180bc848ff740158c0e25c13227a6b0862c |
+|                         |                                | Linux-x86_64                | Python3.7.5                 | [mindspore_ascend-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/x86_64/mindspore_ascend-1.6.0-cp37-cp37m-linux_x86_64.whl) | 966f0411b497273415ed6779f15d5743c08ae2eb62cef5d291b1995251857ebb |
+|                         |                                |                             | Python3.9.0                 | [mindspore_ascend-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/x86_64/mindspore_ascend-1.6.0-cp39-cp39-linux_x86_64.whl) | 79b4cc68de6a3c70bf1bd03a538441d347bba619189822fde58ccae8145c949c |
+|                         | Ascend 310-Binary              | Linux-aarch64               | Python3.7.5<br/>Python3.9.0 | [mindspore_ascend-1.6.0-linux_aarch64.tar.gz](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/aarch64/mindspore_ascend-1.6.0-linux_aarch64.tar.gz) | 3a15640b9986819055c98499eadbb7e365fa3b2a27e302bca8ab68659096b1dd |
+|                         |                                | Linux-x86_64                | Python3.7.5<br/>Python3.9.0 | [mindspore_ascend-1.6.0-linux_x86_64.tar.gz](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/ascend/x86_64/mindspore_ascend-1.6.0-linux_x86_64.tar.gz) | 2656bc3bf5cf5f9a65e2869ecc06f3e76b51792e5fe3b1d922160904312d29e6 |
+|                         | GPU CUDA 10.1                  | Linux-x86_64                | Python3.7.5                 | [mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-10.1/mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl) | a961ca60c44b21c9b35767ef6f4ebfeb8105d91d7e51fe44c687c98ee7971961 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-10.1/mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl) | 053f13c3b6575b111aa27dba5b58c8e258f8994bb07508982092769699c5740e |
+|                         | GPU CUDA 11.1                  | Linux-x86_64                | Python3.7.5                 | [mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-11.1/mindspore_gpu-1.6.0-cp37-cp37m-linux_x86_64.whl) | e76bcc715a41c21f2be2604018569a0b02ca3c43b23defbeb5ff3da27260ca80 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/gpu/x86_64/cuda-11.1/mindspore_gpu-1.6.0-cp39-cp39-linux_x86_64.whl) | 4719c951da345db6ce898dfa0ebc151cc2cb8af116dcbf0eab1202566af5aa6d |
+|                         | CPU                            | Linux-aarch64               | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/aarch64/mindspore-1.6.0-cp37-cp37m-linux_aarch64.whl) | 5dc6b9abe668d53960773d6e8ac4dc6f7016c15cce5c9480045c74a3e456e40f |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/aarch64/mindspore-1.6.0-cp39-cp39-linux_aarch64.whl) | 4a8f49587b30b6a0413edba85ebbae07600fc84f8a1fa48c42a2359402bfc852 |
+|                         |                                | Linux-x86_64                | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp37-cp37m-linux_x86_64.whl) | b4fe66629150c47397722057c32c806cd3eece5e158a93c62cac0bc03b464e3f |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp39-cp39-linux_x86_64.whl) | 87151059854e7388c6b5d6d56a7b75087f171efdcd84896c61d0e9088fcebcc0 |
+|                         |                                | Windows-x64                 | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp37-cp37m-win_amd64.whl) | bff5df6a20f98235135693dbcc2928006e0bc080d65faa1bafe0193b67c05051 |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp39-cp39-win_amd64.whl) | 51bc1079f5e5f1fe75b9b1e1022756dd2628b39d8c4c2b872bc33e6e14ecf5bf |
+|                         |                                | MacOS-aarch64               | Python3.9.1                 | [mindspore-1.6.0-cp39-cp39-macosx_11_0_arm64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/aarch64/mindspore-1.6.0-cp39-cp39-macosx_11_0_arm64.whl) | dead04b7b7d3168f0f5837d43ec18fb65c225d3834b3fce8fdc6e3e117218800 |
+|                         |                                | MacOS-x64                   | Python3.7.5                 | [mindspore-1.6.0-cp37-cp37m-macosx_10_15_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp37-cp37m-macosx_10_15_x86_64.whl) | cafd82348d00a86673c72f74c3990861a0bae54080b19de29cd55767445729f7 |
+|                         |                                |                             | Python3.9.0                 | [mindspore-1.6.0-cp39-cp39-macosx_10_15_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindSpore/cpu/x86_64/mindspore-1.6.0-cp39-cp39-macosx_10_15_x86_64.whl) | c2c95d03deb197b7ee4bb7c1d94e2ead7d213d791c016083d23c9710749678ca |
+| MindSpore<br>Serving    | Ascend 910<br>Ascend310<br>GPU | Linux-aarch64               | Python3.7.5                 | [mindspore_serving-1.6.0-cp37-cp37m-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/aarch64/mindspore_serving-1.6.0-cp37-cp37m-linux_aarch64.whl) | 55455e5e2d0807d5d64dee5cf8d701fe0afdc3151d697509f8211607944ce74c |
+|                         |                                |                             | Python3.9.0                 | [mindspore_serving-1.6.0-cp39-cp39-linux_aarch64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/aarch64/mindspore_serving-1.6.0-cp39-cp39-linux_aarch64.whl) | ce9868ba280901edb8c9438106cca8eab31e6289e09aaf221a3445ed67956b43 |
+|                         |                                | Linux-x86_64                | Python3.7.5                 | [mindspore_serving-1.6.0-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/x86_64/mindspore_serving-1.6.0-cp37-cp37m-linux_x86_64.whl) | 62cbf148a1bd5f96d92406052b5e1c45d2b1dfcdeeb73dde3c487a43aa1d6a31 |
+|                         |                                |                             | Python3.9.0                 | [mindspore_serving-1.6.0-cp39-cp39-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Serving/x86_64/mindspore_serving-1.6.0-cp39-cp39-linux_x86_64.whl) | a2be90bfc2b8800137bb5db379dfbfd3e5b17653b092f5859345195602274449 |
+| MindInsight             |                                | any                         | Python3                     | [mindinsight-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindInsight/any/mindinsight-1.6.0-py3-none-any.whl) | 8e8b002fa919e2da11cd2b1771233ae951e3eddd8fba0d346f18d8c881831ec9 |
+| MindConverter           |                                | any                         | Python3                     | [mindconverter-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindInsight/any/mindconverter-1.6.0-py3-none-any.whl) | 5b720b6efd6dfe4c15056c1195297ae07d4ffeeb383bad53decdfa84f5d98526 |
+| MindArmour              |                                | any                         | Python3                     | [mindarmour-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindArmour/any/mindarmour-1.6.0-py3-none-any.whl) | 1e26f0a4eb5ba79b3facbe9e3677bda1227cd5a642a3b697bfe99f33d3165ae8 |
+| MindSpore<br>Hub        |                                | any                         | Python3                     | [mindspore_hub-1.6.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Hub/any/mindspore_hub-1.6.0-py3-none-any.whl) | 3185a83ac660156f09b40fe6c8c8f9d57852c3d0e68ff130341106deb8020a8a |
+| MindQuantum             |                                | Linux-x86_64                | Python3.7.5                 | [mindquantum-0.5.0rc1-cp37-cp37m-linux_x86_64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindQuantum/x86_64/mindquantum-0.5.0rc1-cp37-cp37m-linux_x86_64.whl) | 2ee5346249ba7608bd67c3cbe2ab47177581389f6db783156c076d1f11251756 |
+|                         |                                | Windows-x64                 | Python3.7.5                 | [mindquantum-0.5.0rc1-cp37-cp37m-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindQuantum/x86_64/mindquantum-0.5.0rc1-cp37-cp37m-win_amd64.whl) | ffb0cc9ff182f7f4df46db110712efc151c1ea4f79e5526d609e140490db8757 |
+|                         |                                |                             | Python3.9.0                 | [mindquantum-0.5.0rc1-cp39-cp39-win_amd64.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/MindQuantum/x86_64/mindquantum-0.5.0rc1-cp39-cp39-win_amd64.whl) | 4043fbfd191c1fb96c6c709b4afa49accae48290e01e7260f5f585a23745ed80 |
+| MindSpore Reinforcement |                                | any                         | Python3                     | [mindspore_rl-0.2.0-py3-none-any.whl](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/Reinforcement/any/mindspore_rl-0.2.0-py3-none-any.whl) | a1461746e27e8532a8b00bf16a9e8b63fdc2412bdeaf769c33b650a80154708a |
+| Ide-Plugin              |                                | Linux-x86_64<br>Windows-x64 | any                         | [MindSpore_Dev_ToolKit-1.6.0.zip](https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.6.0/IdePlugin/any/MindSpore_Dev_ToolKit-1.6.0.zip) | 835e14f4b2c85118f54512348edc9fc3239c198dd1dbd6c9441843154b330d40 |
+
 ### 1.5.2
 
 |   组件   |   硬件平台   |   操作系统   |      Python版本      |      链接      |        SHA-256     |
diff --git a/resource/sphinx_ext/anchor_mod.py b/resource/sphinx_ext/anchor_mod.py
new file mode 100644
index 0000000000000000000000000000000000000000..23eeb66ca5dcb78a554da948a50817ddc1ff128e
--- /dev/null
+++ b/resource/sphinx_ext/anchor_mod.py
@@ -0,0 +1,49 @@
+"""Modify docutils.nodes."""
+import inspect
+from docutils import nodes as docu_node
+from docutils.nodes import document
+
+
+short_title_define = '''\
+def short_title(titlename):
+    titlename = titlename.lower()
+    cn_symbol = "。；，“”（）、？《》"
+    en_symbol = ".,=+*()<>[]{}|/"
+    spec_symbol = "_:："
+    for i in cn_symbol:
+        titlename = titlename.replace(i, '')
+    for j in en_symbol:
+        titlename = titlename.replace(j, '')
+    for k in spec_symbol:
+        titlename = titlename.replace(k, ' ')
+    titlename = titlename.replace('  ', ' ').replace(' ', '-')
+
+    return titlename
+'''
+
+dupname_rewrite = """
+def dupname(node, name):
+    node['dupnames'].append(name)
+    if node['names']:
+        node['names'].remove(name)
+    node.referenced = 1
+"""
+
+before = "            node['ids'].append(id)"
+after = """\
+        if node['names']:
+            id = node['names'][0]
+            id = short_title(id)
+            self.id_counter[id] += 1
+            if self.id_counter[id] > 1:
+                id = '{}-{}'.format(id, self.id_counter[id]-1)
+                self.id_counter[id] += 1
+        node['ids'].append(id)"""
+
+# Mod nodes for docutils.
+document_content = inspect.getsource(document)
+document_content = document_content.replace(before, after)
+#pylint: disable=exec-used
+exec(short_title_define, docu_node.__dict__)
+exec(document_content, docu_node.__dict__)
+exec(dupname_rewrite, docu_node.__dict__)
diff --git a/resource/sphinx_ext/nbsphinx_mod.py b/resource/sphinx_ext/nbsphinx_mod.py
new file mode 100644
index 0000000000000000000000000000000000000000..b39d10282f0224a7bc2f200323a831cb420a147d
--- /dev/null
+++ b/resource/sphinx_ext/nbsphinx_mod.py
@@ -0,0 +1,34 @@
+"""Modify nbsphinx."""
+import os
+import nbsphinx as nbs
+
+
+short_title_define = '''\
+def short_title(titlename):
+    titlename = titlename.lower()
+    cn_symbol = "。；，“”（）、？《》"
+    en_symbol = ".,=+*()<>[]{}|/"
+    spec_symbol = "_:："
+    for i in cn_symbol:
+        titlename = titlename.replace(i, '')
+    for j in en_symbol:
+        titlename = titlename.replace(j, '')
+    for k in spec_symbol:
+        titlename = titlename.replace(k, ' ')
+    titlename = titlename.replace('  ', ' ').replace(' ', '-')
+
+    return titlename
+'''
+
+# Remove extra outputs for nbsphinx extension.
+nbsphinx_source = r"    app.connect('html-collect-pages', html_collect_pages)\n"
+before = "link_id = title.replace(' ', '-')"
+after = "link_id = short_title(title)"
+mod_path = os.path.abspath(nbs.__file__)
+with open(mod_path, "r+", encoding="utf8") as f:
+    contents = f.read()
+    contents.replace(nbsphinx_source, '')
+    contents = contents.replace(before, after)
+    #pylint: disable=exec-used
+    exec(short_title_define, nbs.__dict__)
+    exec(contents, nbs.__dict__)
diff --git a/tools/rst_lint/run.py b/tools/rst_lint/run.py
index 62b210878e185a895782803bf24c591f8cb9bc92..8a4920a9a12f9e234508908d435bbe76048ef290 100644
--- a/tools/rst_lint/run.py
+++ b/tools/rst_lint/run.py
@@ -97,9 +97,9 @@ register_directive('toctree', Toctree)
 register_directive('autosummary', CustomDirectiveNoNested)
 register_directive('msplatformautosummary', CustomDirectiveNoNested)
 register_directive('msnoteautosummary', CustomDirectiveNoNested)
-register_directive('cnmsautosummary', CustomDirectiveNoNested)
-register_directive('cnmsplatformautosummary', CustomDirectiveNoNested)
-register_directive('cnmsnoteautosummary', CustomDirectiveNoNested)
+register_directive('mscnautosummary', CustomDirectiveNoNested)
+register_directive('mscnplatformautosummary', CustomDirectiveNoNested)
+register_directive('mscnnoteautosummary', CustomDirectiveNoNested)
 register_directive('currentmodule', CurrentModule)
 
 # Register roles.
diff --git a/tutorials/requirements.txt b/tutorials/requirements.txt
index 6d8cd70439820e16bc32c4abc93e948ba81dc01b..49a77fdec3a5c745edd40eaa223883c31500e975 100644
--- a/tutorials/requirements.txt
+++ b/tutorials/requirements.txt
@@ -1,7 +1,8 @@
 sphinx >= 2.2.1, <= 2.4.4
+docutils == 0.16
 myst_parser == 0.14.0
 sphinx-markdown-tables
-sphinx_rtd_theme
+sphinx_rtd_theme == 0.5.2
 numpy
 nbsphinx
 IPython
diff --git a/tutorials/source_en/conf.py b/tutorials/source_en/conf.py
index 6bc61feb7c22c3653744f192bc039a43ae5f4bac..1144b18eaa8d627f535a0030d826f181c349918b 100644
--- a/tutorials/source_en/conf.py
+++ b/tutorials/source_en/conf.py
@@ -11,10 +11,10 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 
 # -- Project information -----------------------------------------------------
 
@@ -62,25 +62,21 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 #
 html_theme = 'sphinx_rtd_theme'
+#modify layout.html for sphinx_rtd_theme.
+import sphinx_rtd_theme
+layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
+layout_src = '../../resource/_static/layout.html'
+if os.path.exists(layout_target):
+    os.remove(layout_target)
+shutil.copy(layout_src, layout_target)
 
 html_search_language = 'en'
 
 html_static_path = ['_static']
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../resource/search'))
diff --git a/tutorials/source_en/inference.md b/tutorials/source_en/inference.md
index 82af48885c2b619aa9219e96238acab67c269d51..088d03ff4708efdb06f03a521d42c744ca6b6b82 100644
--- a/tutorials/source_en/inference.md
+++ b/tutorials/source_en/inference.md
@@ -273,7 +273,7 @@ The following uses the [mobilenetv2.mindir](https://download.mindspore.cn/model_
 
 #### Building and Running the Windows System
 
-- Build. Renference [Building MindSpore Lite](https://mindspore.cn/lite/docs/en/master/use/build.html#id1) to get the Environment Requirements.
+- Build. Renference [Building MindSpore Lite](https://mindspore.cn/lite/docs/en/master/use/build.html#environment-requirements) to get the Environment Requirements.
 
     - Download the library: Manually download the MindSpore Lite model inference framework [mindspore-lite-{version}-win-x64.zip](https://www.mindspore.cn/lite/docs/en/master/use/downloads.html) whose hardware platform is CPU and operating system is Windows-x64. Copy the `libmindspore-lite.a` file in the decompressed `inference/lib` directory to the `mindspore/lite/examples/quick_start_cpp/lib` directory. Copy the `inference/include` directory to the `mindspore/lite/examples/quick_start_cpp/include` directory.
 
diff --git a/tutorials/source_en/save_load_model.md b/tutorials/source_en/save_load_model.md
index 5a92881b1c6df6986522c7d46cd9f75e3be5eda8..bf72874642a3d70dc718c6f5a65633d695814a52 100644
--- a/tutorials/source_en/save_load_model.md
+++ b/tutorials/source_en/save_load_model.md
@@ -171,4 +171,4 @@ export(resnet, Tensor(input), file_name='resnet50-2_32', file_format='ONNX')
 
 > - `input` specifies the input shape and data type of the exported model. If the network has multiple inputs, you need to pass them to the `export` method. Example: `export(network, Tensor(input1), Tensor(input2), file_name='network', file_format='ONNX')`
 > - If `file_name` does not contain the ".onnx" suffix, the system will automatically add the ".onnx" suffix to it.
-> - Currently, only the ONNX format export of ResNet series networks and BERT are supported.
+> - Currently, only the ONNX format export of ResNet50 and BERT[CLS] networks are supported.
diff --git a/tutorials/source_zh_cn/conf.py b/tutorials/source_zh_cn/conf.py
index 3ca84102471c22c0e031a59535a2541c459d581b..01e99f0e1ad90d016b5112f104176e9d10d1221b 100644
--- a/tutorials/source_zh_cn/conf.py
+++ b/tutorials/source_zh_cn/conf.py
@@ -11,10 +11,10 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import shutil
 import IPython
 import re
 import sys
-import nbsphinx as nbs
 
 # -- Project information -----------------------------------------------------
 
@@ -62,6 +62,13 @@ pygments_style = 'sphinx'
 # a list of builtin themes.
 #
 html_theme = 'sphinx_rtd_theme'
+#modify layout.html for sphinx_rtd_theme.
+import sphinx_rtd_theme
+layout_target = os.path.join(os.path.dirname(sphinx_rtd_theme.__file__), 'layout.html')
+layout_src = '../../resource/_static/layout.html'
+if os.path.exists(layout_target):
+    os.remove(layout_target)
+shutil.copy(layout_src, layout_target)
 
 html_search_language = 'zh'
 
@@ -69,20 +76,9 @@ html_search_options = {'dict': '../resource/jieba.txt'}
 
 html_static_path = ['_static']
 
-# Remove extra outputs for nbsphinx extension.
-nbsphinx_source_re = re.compile(r"(app\.connect\('html-collect-pages', html_collect_pages\))")
-nbsphinx_math_re = re.compile(r"(\S.*$)")
-mod_path = os.path.abspath(nbs.__file__)
-with open(mod_path, "r+", encoding="utf8") as f:
-    contents = f.readlines()
-    for num, line in enumerate(contents):
-        _content_re = nbsphinx_source_re.search(line)
-        if _content_re and "#" not in line:
-            contents[num] = nbsphinx_source_re.sub(r"# \g<1>", line)
-        if "mathjax_config = app.config" in line and "#" not in line:
-            contents[num:num+10] = [nbsphinx_math_re.sub(r"# \g<1>", i) for i in contents[num:num+10]]
-            break
-    exec("".join(contents), nbs.__dict__)
+sys.path.append(os.path.abspath('../../resource/sphinx_ext'))
+import anchor_mod
+import nbsphinx_mod
 
 
 sys.path.append(os.path.abspath('../../resource/search'))
diff --git a/tutorials/source_zh_cn/inference.md b/tutorials/source_zh_cn/inference.md
index a1e8fa9a220d7d49951472d6b983d0621fab0079..6d299857455f8582cd549245858913c5051a4d94 100644
--- a/tutorials/source_zh_cn/inference.md
+++ b/tutorials/source_zh_cn/inference.md
@@ -251,7 +251,7 @@ MindSpore Lite是端边云全场景AI框架MindSpore的端侧部分，可以在
 
 #### Linux系统构建与运行
 
-- 编译构建，环境要求请参考[编译MindSpore Lite](https://mindspore.cn/lite/docs/zh-CN/master/use/build.html#id1) 。
+- 编译构建，环境要求请参考[编译MindSpore Lite](https://mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求) 。
 
   在`mindspore/lite/examples/quick_start_cpp`目录下执行build脚本，将能够自动下载相关文件并编译Demo。
 
@@ -276,7 +276,7 @@ MindSpore Lite是端边云全场景AI框架MindSpore的端侧部分，可以在
 
 #### Windows系统构建与运行
 
-- 编译构建，环境要求请参考[编译MindSpore Lite](https://mindspore.cn/lite/docs/zh-CN/master/use/build.html#id6) 。
+- 编译构建，环境要求请参考[编译MindSpore Lite](https://mindspore.cn/lite/docs/zh-CN/master/use/build.html#环境要求-1) 。
 
     - 库下载：请手动下载硬件平台为CPU、操作系统为Windows-x64的MindSpore Lite模型推理框架[mindspore-lite-{version}-win-x64.zip](https://www.mindspore.cn/lite/docs/zh-CN/master/use/downloads.html)，将解压后`inference/lib`目录下的`libmindspore-lite.a`拷贝到`mindspore/lite/examples/quick_start_cpp/lib`目录、`inference/include`目录拷贝到`mindspore/lite/examples/quick_start_cpp/include`目录。
 
diff --git a/tutorials/source_zh_cn/intermediate/distributed_training/distributed_training_gpu.md b/tutorials/source_zh_cn/intermediate/distributed_training/distributed_training_gpu.md
index 64fad1d21267906f9a72ded93d4407b1233d30b6..30f0f30a325a2540e6cbd18665e84d36cae356ee 100644
--- a/tutorials/source_zh_cn/intermediate/distributed_training/distributed_training_gpu.md
+++ b/tutorials/source_zh_cn/intermediate/distributed_training/distributed_training_gpu.md
@@ -41,12 +41,6 @@
 
   参考OpenMPI官网教程安装：<https://www.open-mpi.org/faq/?category=building#easy-build>。
 
-- `NCCL-2.7.6`：Nvidia集合通信库。
-
-  NCCL-2.7.6下载地址：<https://developer.nvidia.com/nccl/nccl-legacy-downloads>。
-
-  参考NCCL官网教程安装：<https://docs.nvidia.com/deeplearning/nccl/install-guide/index.html#debian>。
-
 - 主机间免密登陆（涉及多机训练时需要）。若训练涉及多机，则需要配置多机间免密登陆，可参考以下步骤进行配置：
   1. 每台主机确定同一个用户作为登陆用户（不推荐root）；
   2. 执行`ssh-keygen -t rsa -P ""`生成密钥；
@@ -81,7 +75,7 @@ if __name__ == "__main__":
 
 ## 数据并行模式加载数据集
 
-在GPU硬件平台上，加载数据集的处理流程和Ascend 910 AI处理器一致。可参考[数据并行模式加载数据集](https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_ascend.html#id5)。
+在GPU硬件平台上，加载数据集的处理流程和Ascend 910 AI处理器一致。可参考[数据并行模式加载数据集](https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_ascend.html#数据并行模式加载数据集)。
 
 ## 定义网络
 
@@ -91,7 +85,7 @@ if __name__ == "__main__":
 
 ### 定义损失函数
 
-在GPU硬件平台上，损失函数的定义和Ascend 910 AI处理器一致。可参考[定义损失函数](https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_ascend.html#id8)。
+在GPU硬件平台上，损失函数的定义和Ascend 910 AI处理器一致。可参考[定义损失函数](https://www.mindspore.cn/tutorials/zh-CN/master/intermediate/distributed_training/distributed_training_ascend.html#定义损失函数)。
 
 ### 定义优化器
 
diff --git a/tutorials/source_zh_cn/quick_start.ipynb b/tutorials/source_zh_cn/quick_start.ipynb
index b831da67c0f09b1546a2c553707e37e82963214b..ad2d28d4767d2149cb920260fafedbca5edfedcf 100644
--- a/tutorials/source_zh_cn/quick_start.ipynb
+++ b/tutorials/source_zh_cn/quick_start.ipynb
@@ -428,7 +428,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> 阅读更多有关[mindspore加载模型](https://www.mindspore.cn/tutorials/zh-CN/master/save_load_model.html#id3)的信息。\n",
+    "> 阅读更多有关[mindspore加载模型](https://www.mindspore.cn/tutorials/zh-CN/master/save_load_model.html#直接保存模型)的信息。\n",
     "\n",
     "## 验证模型\n",
     "\n",
diff --git a/tutorials/source_zh_cn/save_load_model.md b/tutorials/source_zh_cn/save_load_model.md
index d11dfb73a3b96a987f91ae364342ceda7d231a55..038b87f5b453b8d93c62d5988263a22dac065fb4 100644
--- a/tutorials/source_zh_cn/save_load_model.md
+++ b/tutorials/source_zh_cn/save_load_model.md
@@ -171,4 +171,4 @@ export(resnet, Tensor(input), file_name='resnet50-2_32', file_format='ONNX')
 
 > - `input`用来指定导出模型的输入shape以及数据类型，如果网络有多个输入，需要一同传进`export`方法。 例如：`export(network, Tensor(input1), Tensor(input2), file_name='network', file_format='ONNX')`
 > - 如果`file_name`没有包含".onnx"后缀，系统会为其自动添加".onnx"后缀。
-> - 目前ONNX格式导出仅支持ResNet系列、BERT网络。
+> - 目前ONNX格式导出仅支持ResNet50、BERT[CLS]网络。