Merge pull request #64 from microsoft/master

pull code

README.Makefile_zh_CN.md

@@ -69,7 +69,7 @@ Python 包使用 setuptools 安装，所以安装路径依赖于 Python 配置
 | ------------------ | ------------------------------------------ |
 | `easy-install`     | 安装依赖项，生成，安装 NNI，并编辑 `~/.bashrc`            |
 | `dev-easy-install` | 安装依赖项，生成，将 NNI 作为符号链接来安装，并编辑 `~/.bashrc`   |
-| `install`          | 安装 Python 包，Node.js 模块，NNI 脚本和样例           |
+| `install`          | 安装 Python 包，Node.js 模块，NNI 脚本和示例           |
 | `dev-install`      | 将 Python 和 Node.js 模块作为符号链接安装，然后安装 scripts |
 | `pip-install`      | 安装依赖项，生成，安装 NNI，但不安装 Python 包              |
 

README.md

@@ -25,7 +25,7 @@ The tool manages automated machine learning (AutoML) experiments, **dispatches a
 * Researchers and data scientists who want to easily **implement and experiement new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
 * ML Platform owners who want to **support AutoML in their platform**.
 
-### **NNI v1.2 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **NNI v1.3 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
 
 ## **NNI capabilities in a glance**
 NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in stat-of-the-art AutoML algorithms and out of box support for popular training platforms. 
@@ -229,7 +229,7 @@ Linux and MacOS
 * Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
 
 ```bash
-    git clone -b v1.2 https://github.com/Microsoft/nni.git
+    git clone -b v1.3 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -239,7 +239,7 @@ Windows
 * Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
 
 ```bash
-  git clone -b v1.2 https://github.com/Microsoft/nni.git
+  git clone -b v1.3 https://github.com/Microsoft/nni.git
   cd nni
   powershell -ExecutionPolicy Bypass -file install.ps1
 ```
@@ -255,7 +255,7 @@ The following example is an experiment built on TensorFlow. Make sure you have *
 * Download the examples via clone the source code.
 
 ```bash
-    git clone -b v1.2 https://github.com/Microsoft/nni.git
+    git clone -b v1.3 https://github.com/Microsoft/nni.git
 ```
 
 Linux and MacOS

README_zh_CN.md

@@ -19,7 +19,7 @@ NNI 管理自动机器学习 (AutoML) 的 Experiment，**调度运行**由调优
 * 想要更容易**实现或试验新的自动机器学习算法**的研究员或数据科学家，包括：超参调优算法，神经网络搜索算法以及模型压缩算法。
 * 在机器学习平台中**支持自动机器学习**。
 
-### **NNI v1.2 已发布！ &nbsp;[<img width="48" src="docs/img/release_icon.png" />](#nni-released-reminder)**
+### **NNI v1.3 已发布！ &nbsp;[<img width="48" src="docs/img/release_icon.png" />](#nni-released-reminder)**
 
 ## **NNI 功能一览**
 
@@ -224,7 +224,7 @@ Linux 和 MacOS
 * 在 `python >= 3.5` 的环境中运行命令： `git` 和 `wget`，确保安装了这两个组件。
 
 ```bash
-    git clone -b v1.2 https://github.com/Microsoft/nni.git
+    git clone -b v1.3 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -234,7 +234,7 @@ Windows
 * 在 `python >=3.5` 的环境中运行命令： `git` 和 `PowerShell`，确保安装了这两个组件。
 
 ```bash
-  git clone -b v1.2 https://github.com/Microsoft/nni.git
+  git clone -b v1.3 https://github.com/Microsoft/nni.git
   cd nni
   powershell -ExecutionPolicy Bypass -file install.ps1
 ```
@@ -250,7 +250,7 @@ Windows 上参考 [Windows 上使用 NNI](docs/zh_CN/Tutorial/NniOnWindows.md)
 * 通过克隆源代码下载示例。
 
 ```bash
-    git clone -b v1.2 https://github.com/Microsoft/nni.git
+    git clone -b v1.3 https://github.com/Microsoft/nni.git
 ```
 
 Linux 和 MacOS
@@ -326,7 +326,7 @@ You can use these commands to get more information about the experiment
 * [如何调试](docs/zh_CN/Tutorial/HowToDebug.md)
 * [自定义 Tuner](docs/zh_CN/Tuner/CustomizeTuner.md)
 * [实现定制的训练平台](docs/zh_CN/TrainingService/HowToImplementTrainingService.md)
-* [在 NNI 上实现新的 NAS Trainer](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/NasInterface.md#implement-a-new-nas-trainer-on-nni)
+* [在 NNI 上实现新的 NAS Trainer](https://github.com/microsoft/nni/blob/master/docs/zh_CN/NAS/NasInterface.md#implement-a-new-nas-trainer-on-nni)
 * [自定义 Advisor](docs/zh_CN/Tuner/CustomizeAdvisor.md)
 
 ## **其它代码库和参考**
@@ -349,6 +349,7 @@ You can use these commands to get more information about the experiment
    * [使用 NNI 为 SPTAG 自动调参](docs/zh_CN/CommunitySharings/SptagAutoTune.md)
    * [使用 NNI 为 scikit-learn 查找超参](https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1)
    * **博客** - [AutoML 工具（Advisor，NNI 与 Google Vizier）的对比](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90) 作者：[@gaocegege](https://github.com/gaocegege) - kubeflow/katib 的设计与实现的总结与分析章节
+   * **Blog (中文)** - [NNI 2019 新功能汇总](https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA) by @squirrelsc
 
 ## **反馈**
 

azure-pipelines.yml

@@ -31,6 +31,7 @@ jobs:
       python3 -m pip install tensorflow==1.13.1 --user
       python3 -m pip install keras==2.1.6 --user
       python3 -m pip install gym onnx --user
+      python3 -m pip install sphinx==1.8.3 sphinx-argparse==0.2.5 sphinx-markdown-tables==0.0.9 sphinx-rtd-theme==0.4.2 sphinxcontrib-websupport==1.1.0 recommonmark==0.5.0 --user
       sudo apt-get install swig -y
       nnictl package install --name=SMAC
       nnictl package install --name=BOHB
@@ -69,6 +70,10 @@ jobs:
       cd test
       python3 cli_test.py
     displayName: 'nnicli test'
+  - script: |
+      cd docs/en_US/
+      sphinx-build -M html . _build -W
+    displayName: 'Sphinx Documentation Build check'
 
 - job: 'basic_test_pr_macOS'
   pool: