diff --git a/README.md b/README.md
index afc778fddc..86b83c599a 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ NNI (Neural Network Intelligence) is a toolkit to help users run automated machi
 The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
 
 
-### **NNI [v0.9](https://github.com/Microsoft/nni/releases) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **NNI [v1.0](https://github.com/Microsoft/nni/blob/master/docs/en_US/Release_v1.0.md) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
 
 <p align="center">
   <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
@@ -70,8 +70,8 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
         <ul>
         <li><b>Examples</b></li>
          <ul>
-           <li><a href="examples/trials/mnist-distributed-pytorch">MNIST-pytorch</li></a>
-           <li><a href="examples/trials/mnist-distributed">MNIST-tensorflow</li></a>
+           <li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
+           <li><a href="examples/trials/mnist">MNIST-tensorflow</li></a>
            <li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
            <li><a href="docs/en_US/TrialExample/GbdtExample.md">Auto-gbdt</a></li>
            <li><a href="docs/en_US/TrialExample/Cifar10Examples.md">Cifar10-pytorch</li></a>
@@ -100,7 +100,7 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
           <li><a href="docs/en_US/Tuner/BuiltinTuner.md#BOHB">BOHB</a></li>
           <li><a href="docs/en_US/Tuner/BuiltinTuner.md#GPTuner">GP Tuner</a></li>
           </ul>
-          <li><b>Tuner for <a href="docs/en_US/CommunitySharings/NasComparision.md">NAS</a></b></li>
+          <li><b>Tuner for <a href="docs/en_US/AdvancedFeature/GeneralNasInterfaces.md">NAS</a></b></li>
           <ul>
           <li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
           <li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
@@ -182,7 +182,7 @@ We encourage researchers and students leverage these projects to accelerate the
 
 **Install through pip**
 
-* We support Linux, MacOS and Windows(local, remote and pai mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
+* We support Linux, MacOS and Windows (local, remote and pai mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
 
 Linux and MacOS
 
@@ -211,7 +211,7 @@ Linux and MacOS
 * Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
 
 ```bash
-    git clone -b v0.9 https://github.com/Microsoft/nni.git
+    git clone -b v1.0 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -221,7 +221,7 @@ Windows
 * Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
 
 ```bash
-  git clone -b v0.9 https://github.com/Microsoft/nni.git
+  git clone -b v1.0 https://github.com/Microsoft/nni.git
   cd nni
   powershell -ExecutionPolicy Bypass -file install.ps1
 ```
@@ -237,7 +237,7 @@ 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 v0.9 https://github.com/Microsoft/nni.git
+    git clone -b v1.0 https://github.com/Microsoft/nni.git
 ```
 
 Linux and MacOS
@@ -345,18 +345,27 @@ Before providing your hacks, you can review the [Contributing Instruction](docs/
 * [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.md)
 
 
-## **External Repositories**
-Now we have some external usage examples run in NNI from our contributors. Thanks our lovely contributors. And welcome more and more people to join us!
-* Run [ENAS](examples/tuners/enas_nni/README.md) in NNI
-* Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) in NNI 
-* [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) in NNI
+## **External Repositories and References**
+With authors' permission, we listed a set of NNI usage examples and relevant articles.
+* ### **External Repositories** ###
+   * Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
+   * Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI 
+   * [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) with NNI 
+   * [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
+
+* ### **Relevant Articles** ###
+  
+  * [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparision.md)
+  * [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparision.md)
+  * [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
+  * [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
+  * [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
+  * **Blog (in Chinese)** - [AutoML tools (Advisor, NNI and Google Vizier) comparison](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) by [@gaocegege](https://github.com/gaocegege) - 总结与分析 section of design and implementation of kubeflow/katib
 
 ## **Feedback**
-* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI
-* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true)
+* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
 * [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-
-
+* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true).
 
 
 ## **License**
diff --git a/README_zh_CN.md b/README_zh_CN.md
index 75f1181a37..02cd6d3651 100644
--- a/README_zh_CN.md
+++ b/README_zh_CN.md
@@ -10,7 +10,7 @@
 
 NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包。 它通过多种调优的算法来搜索最好的神经网络结构和（或）超参，并支持单机、本地多机、云等不同的运行环境。
 
-### **NNI [v0.9](https://github.com/Microsoft/nni/releases) 已发布！ &nbsp;[<img width="48" src="docs/img/release_icon.png" />](#nni-released-reminder)**
+### **NNI [v1.0](https://github.com/Microsoft/nni/blob/master/docs/zh_CN/Release_v1.0.md) 已发布！ &nbsp;[<img width="48" src="docs/img/release_icon.png" />](#nni-released-reminder)**
 
 <p align="center">
   <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
@@ -19,8 +19,10 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
 <table>
   <tbody>
     <tr align="center" valign="bottom">
+    <td>
+      </td>
       <td>
-        <b>支持的框架</b>
+        <b>支持的框架和库</b>
         <img src="docs/img/bar.png"/>
       </td>
       <td>
@@ -34,26 +36,52 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
     </tr>
     </tr>
     <tr valign="top">
+    <td align="center" valign="middle">
+    <b>内置</b>
+      </td>
       <td>
+      <ul><li><b>支持的框架</b></li>
         <ul>
           <li>PyTorch</li>
-          <li>TensorFlow</li>
           <li>Keras</li>
+          <li>TensorFlow</li>
           <li>MXNet</li>
           <li>Caffe2</li>
-          <li>CNTK (Python 语言)</li>
-          <li>Chainer</li>
-          <li>Theano</li>
+          <a href="docs/zh_CN/SupportedFramework_Library.md">更多...</a><br/>
+        </ul>
+        </ul>
+      <ul>
+        <li><b>支持的库</b></li>
+          <ul>
+           <li>Scikit-learn</li>
+           <li>XGBoost</li>
+           <li>LightGBM</li>
+           <a href="docs/zh_CN/SupportedFramework_Library.md">更多...</a><br/>
+          </ul>
+      </ul>
+        <ul>
+        <li><b>示例</b></li>
+         <ul>
+           <li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
+           <li><a href="examples/trials/mnist">MNIST-tensorflow</li></a>
+           <li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
+           <li><a href="docs/zh_CN/TrialExample/GbdtExample.md">Auto-gbdt</a></li>
+           <li><a href="docs/zh_CN/TrialExample/Cifar10Examples.md">Cifar10-pytorch</li></a>
+           <li><a href="docs/zh_CN/TrialExample/SklearnExamples.md">Scikit-learn</a></li>
+              <a href="docs/zh_CN/SupportedFramework_Library.md">更多...</a><br/>
+          </ul>
         </ul>
       </td>
-      <td align="left">
+      <td align="left" >
         <a href="docs/zh_CN/Tuner/BuiltinTuner.md">Tuner（调参器）</a>
-        <br />
         <ul>
-          <b style="margin-left:-20px">通用 Tuner</b>
+          <li><b>通用 Tuner</b></li>
+          <ul>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#Random">Random Search（随机搜索）</a></li>
-          <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution（进化算法）</a></li>    
-          <b style="margin-left:-20px">超参 Tuner</b>
+          <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution（朴素进化）</a></li>
+          </ul>    
+          <li><b><a href="docs/zh_CN/CommunitySharings/HpoComparision.md">超参调优</a> Tuner</b></li>
+          <ul>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#TPE">TPE</a></li>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#Anneal">Anneal（退火算法）</a></li>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#SMAC">SMAC</a></li>
@@ -63,14 +91,19 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#BOHB">BOHB</a></li>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#GPTuner">GP Tuner</a></li>
-          <b style="margin-left:-20px">网络结构 Tuner</b>
+          </ul>
+          <li><b><a href="docs/zh_CN/AdvancedFeature/GeneralNasInterfaces.md">NAS</a> Tuner</b></li>
+          <ul>
           <li><a href="docs/zh_CN/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
-          <li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
+          <li><a href="examples/tuners/enas_nni/README_zh_CN.md">ENAS</a></li>
           </ul>
+        </ul>
           <a href="docs/zh_CN/Assessor/BuiltinAssessor.md">Assessor（评估器）</a>
           <ul>
+          <ul>
           <li><a href="docs/zh_CN/Assessor/BuiltinAssessor.md#Medianstop">Median Stop（中位数终止）</a></li>
           <li><a href="docs/zh_CN/Assessor/BuiltinAssessor.md#Curvefitting">Curve Fitting（曲线拟合）</a></li>   
+          </ul>
           </ul>  
       </td>
       <td>
@@ -85,6 +118,33 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
       </ul>
       </td>
     </tr> 
+      <tr align="center" valign="bottom">
+      </td>
+      </tr>
+      <tr valign="top">
+       <td valign="middle">
+    <b>参考</b>
+      </td>
+     <td style="border-top:#FF0000 solid 0px;">
+      <ul>
+        <li><a href="docs/zh_CN/sdk_reference.rst">Python API</a></li>
+        <li><a href="docs/zh_CN/Tutorial/AnnotationSpec.md">NNI Annotation</a></li>
+         <li><a href="docs/zh_CN/Tutorial/Installation.md">支持的操作系统</a></li>
+      </ul>
+      </td>
+       <td style="border-top:#FF0000 solid 0px;">
+      <ul>
+        <li><a href="docs/zh_CN/Tuner/CustomizeTuner.md">自定义 Tuner</a></li>
+        <li><a href="docs/zh_CN/Assessor/CustomizeAssessor.md">自定义 Assessor</a></li>
+      </ul>
+      </td>
+        <td style="border-top:#FF0000 solid 0px;">
+      <ul>
+        <li><a href="docs/zh_CN/TrainingService/SupportTrainingService.md">支持训练平台</li>
+        <li><a href="docs/zh_CN/TrainingService/HowToImplementTrainingService.md">实现训练平台</a></li>
+      </ul>
+      </td>     
+    </tr> 
   </tbody>
 </table>
 
@@ -139,7 +199,7 @@ 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 v0.9 https://github.com/Microsoft/nni.git
+    git clone -b v1.0 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -149,7 +209,7 @@ 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 v0.9 https://github.com/Microsoft/nni.git
+  git clone -b v1.0 https://github.com/Microsoft/nni.git
   cd nni
   powershell -ExecutionPolicy Bypass -file install.ps1
 ```
@@ -165,7 +225,7 @@ 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 v0.9 https://github.com/Microsoft/nni.git
+    git clone -b v1.0 https://github.com/Microsoft/nni.git
 ```
 
 Linux 和 macOS
@@ -227,64 +287,75 @@ You can use these commands to get more information about the experiment
 
 * [NNI 概述](docs/zh_CN/Overview.md)
 * [快速入门](docs/zh_CN/Tutorial/QuickStart.md)
-* [贡献](docs/zh_CN/Tutorial/Contributing.md)
-* [示例](docs/zh_CN/examples.rst)
-* [参考](docs/zh_CN/reference.rst)
 * [Web 界面教程](docs/zh_CN/Tutorial/WebUI.md)
+* [贡献](docs/zh_CN/Tutorial/Contributing.md)
 
 ## **入门**
 
 * [安装 NNI](docs/zh_CN/Tutorial/Installation.md)
 * [使用命令行工具 nnictl](docs/zh_CN/Tutorial/Nnictl.md)
-* [使用 NNIBoard](docs/zh_CN/Tutorial/WebUI.md)
-* [如何定义搜索空间](docs/zh_CN/Tutorial/SearchSpaceSpec.md)
-* [如何实现 Trial 代码](docs/zh_CN/TrialExample/Trials.md)
-* [如何选择 Tuner、搜索算法](docs/zh_CN/Tuner/BuiltinTuner.md)
+* [实现 Trial](docs/zh_CN/TrialExample/Trials.md)
 * [配置 Experiment](docs/zh_CN/Tutorial/ExperimentConfig.md)
-* [如何使用 Annotation](docs/zh_CN/TrialExample/Trials.md#nni-python-annotation)
+* [定制搜索空间](docs/zh_CN/Tutorial/SearchSpaceSpec.md)
+* [选择 Tuner、搜索算法](docs/zh_CN/Tuner/BuiltinTuner.md)
+* [使用 Annotation](docs/zh_CN/TrialExample/Trials.md#nni-python-annotation)
+* [使用 NNIBoard](docs/zh_CN/Tutorial/WebUI.md)
 
 ## **教程**
 
+* [在本机运行 Experiment (支持多 GPU 卡)](docs/zh_CN/TrainingService/LocalMode.md)
 * [在 OpenPAI 上运行 Experiment](docs/zh_CN/TrainingService/PaiMode.md)
 * [在 Kubeflow 上运行 Experiment](docs/zh_CN/TrainingService/KubeflowMode.md)
-* [在本机运行 Experiment (支持多 GPU 卡)](docs/zh_CN/TrainingService/LocalMode.md)
 * [在多机上运行 Experiment](docs/zh_CN/TrainingService/RemoteMachineMode.md)
 * [尝试不同的 Tuner](docs/zh_CN/Tuner/BuiltinTuner.md)
 * [尝试不同的 Assessor](docs/zh_CN/Assessor/BuiltinAssessor.md)
 * [实现自定义 Tuner](docs/zh_CN/Tuner/CustomizeTuner.md)
 * [实现自定义 Assessor](docs/zh_CN/Assessor/CustomizeAssessor.md)
+* [实现 NNI 训练平台](docs/zh_CN/TrainingService/HowToImplementTrainingService.md)
 * [使用进化算法为阅读理解任务找到好模型](docs/zh_CN/TrialExample/SquadEvolutionExamples.md)
+* [高级神经网络架构搜索](docs/zh_CN/AdvancedFeature/AdvancedNas.md)
 
 ## **贡献**
 
 非常欢迎通过各种方式参与此项目，例如：
 
-* 审查[源代码改动](https://github.com/microsoft/nni/pulls)
-* 审查[文档](https://github.com/microsoft/nni/tree/master/docs)中从拼写错误到新内容的任何内容，并提交拉取请求。
+* [报告 Bug](https://github.com/microsoft/nni/issues/new/choose)。
+* [请求新功能](https://github.com/microsoft/nni/issues/new/choose).
+* 建议或询问[如何调试](docs/zh_CN/Tutorial/HowToDebug.md)文档相关的问题。
 * 找到标有 ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) 或 ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) 标签的 Issue。这些都是简单的 Issue，新的贡献者可以从这些问题开始。
 
-在提交代码前，需要遵循以下的简单准则：
+在编写代码前，可以先看看[贡献指南](docs/zh_CN/Tutorial/Contributing.md)来了解更多信息。 此外，还提供了以下文档：
 
+* [NNI 开发环境安装教程](docs/zh_CN/Tutorial/SetupNniDeveloperEnvironment.md)
 * [如何调试](docs/zh_CN/Tutorial/HowToDebug.md)
-* [代码风格和命名约定](docs/zh_CN/Tutorial/Contributing.md)
-* 如何设置 [NNI 开发环境](docs/zh_CN/Tutorial/SetupNniDeveloperEnvironment.md)
-* 查看[贡献说明](docs/zh_CN/Tutorial/Contributing.md)并熟悉 NNI 的代码贡献指南
-
-## **外部代码库**
-
-下面是一些贡献者为 NNI 提供的使用示例 谢谢可爱的贡献者！ 欢迎越来越多的人加入我们！
-
-* 在 NNI 中运行 [ENAS](examples/tuners/enas_nni/README_zh_CN.md)
-* 在 NNI 中运行 [神经网络架构结构搜索](examples/trials/nas_cifar10/README_zh_CN.md) 
+* [自定义 Advisor](docs/zh_CN/Tuner/CustomizeAdvisor.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)
+
+## **其它代码库和参考**
+
+经作者许可的一些 NNI 用法示例和相关文档。
+
+* ### **外部代码库**
+   
+   * 在 NNI 中运行 [ENAS](examples/tuners/enas_nni/README_zh_CN.md)
+   * 在 NNI 中运行 [神经网络架构结构搜索](examples/trials/nas_cifar10/README_zh_CN.md) 
+   * [NNI 中的自动特征工程](examples/trials/auto-feature-engineering/README_zh_CN.md) 
+   * 使用 NNI 的 [矩阵分解超参调优](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb)
+* ### **相关文章**
+   
+   * [超参数优化的对比](docs/zh_CN/CommunitySharings/HpoComparision.md)
+   * [神经网络结构搜索的对比](docs/zh_CN/CommunitySharings/NasComparision.md)
+   * [并行化顺序算法：TPE](docs/zh_CN/CommunitySharings/ParallelizingTpeSearch.md)
+   * [使用 NNI 为 SVD 自动调参](docs/zh_CN/CommunitySharings/RecommendersSvd.md)
+   * [使用 NNI 为 SPTAG 自动调参](docs/zh_CN/CommunitySharings/SptagAutoTune.md)
+   * **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
 
 ## **反馈**
 
-* [报告 Bug](https://github.com/microsoft/nni/issues/new/choose)。  
-    
-* [请求新功能](https://github.com/microsoft/nni/issues/new/choose).
-* 在 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 中参与讨论
-* 在 [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true) 上使用 nni 的标签提问，或[在 Github 上提交 Issue](https://github.com/microsoft/nni/issues/new/choose)。
-* 我们正在实现[如何调试](docs/zh_CN/Tutorial/HowToDebug.md)的页面，欢迎提交建议和问题。
+* 在 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 中参与讨论。
+* [在 GitHub 上提交问题](https://github.com/microsoft/nni/issues/new/choose)。
+* 在 [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true) 上使用 nni 标签提问。
 
 ## **许可协议**
 
diff --git a/deployment/docker/Dockerfile b/deployment/docker/Dockerfile
index e4c9d50f28..471a5b9964 100644
--- a/deployment/docker/Dockerfile
+++ b/deployment/docker/Dockerfile
@@ -68,8 +68,8 @@ RUN python3 -m pip --no-cache-dir install Keras==2.1.6
 #
 # PyTorch
 #
-RUN python3 -m pip --no-cache-dir install torch==0.4.1
-RUN python3 -m pip install torchvision==0.2.1
+RUN python3 -m pip --no-cache-dir install torch==1.2.0
+RUN python3 -m pip install torchvision==0.4.0
 
 #
 # sklearn 0.20.0
diff --git a/deployment/pypi/setup.py b/deployment/pypi/setup.py
index 93ac59ec04..936f4dc7ff 100644
--- a/deployment/pypi/setup.py
+++ b/deployment/pypi/setup.py
@@ -40,7 +40,7 @@
     files = [path.normpath(path.join(dirpath, filename)) for filename in filenames]
     data_files.append((path.normpath(dirpath), files))
 
-with open('../../README.md', 'r') as fh:
+with open('../../README.md', 'r', encoding="utf-8") as fh:
     long_description = fh.read()
 
 setuptools.setup(
diff --git a/docs/en_US/AdvancedFeature/GeneralNasInterfaces.md b/docs/en_US/AdvancedFeature/GeneralNasInterfaces.md
index 81ff43535c..f3850e6188 100644
--- a/docs/en_US/AdvancedFeature/GeneralNasInterfaces.md
+++ b/docs/en_US/AdvancedFeature/GeneralNasInterfaces.md
@@ -1,6 +1,6 @@
-# General Programming Interface for Neural Architecture Search (experimental feature)
+# NNI Programming Interface for Neural Architecture Search (NAS)
 
-_*This is an experimental feature, currently, we only implemented the general NAS programming interface. Weight sharing will be supported in the following releases._
+_*This is an **experimental feature**. Currently, we only implemented the general NAS programming interface. Weight sharing will be supported in the following releases._
 
 Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging. However, it takes great efforts to implement those algorithms, and it is hard to reuse code base of one algorithm for implementing another.
 
diff --git a/docs/en_US/CommunitySharings/SptagAutoTune.md b/docs/en_US/CommunitySharings/SptagAutoTune.md
new file mode 100644
index 0000000000..a96f9f4991
--- /dev/null
+++ b/docs/en_US/CommunitySharings/SptagAutoTune.md
@@ -0,0 +1,8 @@
+# Automatically tuning SPTAG with NNI
+
+[SPTAG](https://github.com/microsoft/SPTAG) (Space Partition Tree And Graph) is a library for large scale vector approximate nearest neighbor search scenario released by [Microsoft Research (MSR)](https://www.msra.cn/) and [Microsoft Bing](https://www.bing.com/).
+
+This library assumes that the samples are represented as vectors and that the vectors can be compared by L2 distances or cosine distances. Vectors returned for a query vector are the vectors that have smallest L2 distance or cosine distances with the query vector.
+SPTAG provides two methods: kd-tree and relative neighborhood graph (SPTAG-KDT) and balanced k-means tree and relative neighborhood graph (SPTAG-BKT). SPTAG-KDT is advantageous in index building cost, and SPTAG-BKT is advantageous in search accuracy in very high-dimensional data.
+
+In SPTAG, there are tens of parameters that can be tuned for specified scenarios or datasets. NNI is a great tool for automatically tuning those parameters. The authors of SPTAG tried NNI for the auto tuning and found good-performing parameters easily, thus, they shared the practice of tuning SPTAG on NNI in their document [here](https://github.com/microsoft/SPTAG/blob/master/docs/Parameters.md). Please refer to it for detailed tutorial.
\ No newline at end of file
diff --git a/docs/en_US/CommunitySharings/community_sharings.rst b/docs/en_US/CommunitySharings/community_sharings.rst
index 29e3a46051..2f6edf142c 100644
--- a/docs/en_US/CommunitySharings/community_sharings.rst
+++ b/docs/en_US/CommunitySharings/community_sharings.rst
@@ -8,6 +8,7 @@ In addtion to the official tutorilas and examples, we encourage community contri
     :maxdepth: 2
 
     NNI in Recommenders <RecommendersSvd>
+    Automatically tuning SPTAG with NNI <SptagAutoTune>
     Neural Architecture Search Comparison <NasComparision>
     Hyper-parameter Tuning Algorithm Comparsion <HpoComparision>
     Parallelizing Optimization for TPE <ParallelizingTpeSearch>
diff --git a/docs/en_US/Release_v1.0.md b/docs/en_US/Release_v1.0.md
new file mode 100644
index 0000000000..e36ae943a3
--- /dev/null
+++ b/docs/en_US/Release_v1.0.md
@@ -0,0 +1,32 @@
+
+<p align="center">
+<img src=".././img/release-1-title-1.png" width="100%" />
+</p>
+
+
+From September 2018 to September 2019, We are still moving on …
+
+
+ **Great news!**&nbsp;&nbsp;With the tag of **Scalability** and **Ease of Use**, NNI v1.0 is comming. Based on the various types of [Tuning Algorithms](./Tuner/BuiltinTuner.md), NNI has supported the Hyperparameter tuning, Neural Architecture search and Auto-Feature-Engineering, which is an exciting news for algorithmic engineers; besides these, NNI v1.0 has made many improvements in the optimization of tuning algorithm, [WebUI's simplicity and intuition](./Tutorial/WebUI.md) and [Platform diversification](./TrainingService/SupportTrainingService.md). NNI has grown into a more intelligent automated machine learning (AutoML) toolkit.
+ 
+
+
+<br/>
+<br/>
+<br/>
+<p align="center">
+<img src=".././img/nni-1.png" width="80%" />
+</p>
+
+<br />
+<br />
+
+<p align="center">
+<img src=".././img/release-1-title-2.png" width="100%" />
+</p>
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Step one**: Start with the [Tutorial Doc](./Tutorial/Installation.md), and install NNI v1.0 first.<br>
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Step two**: Find a " Hello world example", follow the [Tutorial Doc](./Tutorial/QuickStart.md) and have a Quick Start. <br>
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Step three**: Get familiar with the [WebUI Tutorial](./Tutorial/WebUI.md) and let NNI better assists with your tuning tour.<br>
+
+The fully automated tool greatly improves the efficiency of the tuning process. For more detail about the 1.0 updates, you can refer to [Release 1.0](https://github.com/microsoft/nni/releases). More of our advance plan, you can refer to our [Roadmap](https://github.com/microsoft/nni/wiki/Roadmap). Besides, we also welcome more and more contributors to join us, there are many ways to participate, please refer to [How to contribute](./Tutorial/Contributing.md) for more details.
\ No newline at end of file
diff --git a/docs/en_US/TrainingService/LocalMode.md b/docs/en_US/TrainingService/LocalMode.md
index fcdf6387bf..7408e5b345 100644
--- a/docs/en_US/TrainingService/LocalMode.md
+++ b/docs/en_US/TrainingService/LocalMode.md
@@ -56,7 +56,7 @@ The hyper-parameters used in `Step 1.2 - Get predefined parameters` is defined i
     "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
 }
 ```
-Refer to [SearchSpaceSpec.md](../Tutorial/SearchSpaceSpec.md) to learn more about search space.
+Refer to [define search space](../Tutorial/SearchSpaceSpec.md) to learn more about search space.
 
 >Step 3 - Define Experiment
 
diff --git a/docs/en_US/TrainingService/PaiMode.md b/docs/en_US/TrainingService/PaiMode.md
index 78e7aa7984..3787f7165d 100644
--- a/docs/en_US/TrainingService/PaiMode.md
+++ b/docs/en_US/TrainingService/PaiMode.md
@@ -55,6 +55,32 @@ Compared with [LocalMode](LocalMode.md) and [RemoteMachineMode](RemoteMachineMod
     * Optional key. Set the shmMB configuration of OpenPAI, it set the shared memory for one task in the task role.
 * authFile
     * Optional key, Set the auth file path for private registry while using PAI mode, [Refer](https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.md#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job), you can prepare the authFile and simply provide the local path of this file, NNI will upload this file to HDFS for you.
+* portList  
+    * Optional key. Set the portList configuration of OpenPAI, it specifies a list of port used in container, [Refer](https://github.com/microsoft/pai/blob/b2324866d0280a2d22958717ea6025740f71b9f0/docs/job_tutorial.md#specification).  
+    The config schema in NNI is shown below:
+    ```
+    portList:
+      - label: test
+        beginAt: 8080
+        portNumber: 2
+    ``` 
+    Let's say you want to launch a tensorboard in the mnist example using the port. So the first step is to write a wrapper script `launch_pai.sh` of `mnist.py`.
+
+    ```bash
+    export TENSORBOARD_PORT=PAI_PORT_LIST_${PAI_CURRENT_TASK_ROLE_NAME}_0_tensorboard
+    tensorboard --logdir . --port ${!TENSORBOARD_PORT} &
+    python3 mnist.py
+    ```
+    The config file of portList should be filled as following:
+
+    ```yaml
+  trial:
+    command: bash launch_pai.sh
+    portList:
+      - label: tensorboard
+        beginAt: 0
+        portNumber: 1
+    ```
 
 Once complete to fill NNI experiment config file and save (for example, save as exp_pai.yml), then run the following command
 ```
diff --git a/docs/en_US/TrainingService/SupportTrainingService.md b/docs/en_US/TrainingService/SupportTrainingService.md
index 50c91173e2..dfb0df3fe8 100644
--- a/docs/en_US/TrainingService/SupportTrainingService.md
+++ b/docs/en_US/TrainingService/SupportTrainingService.md
@@ -33,4 +33,4 @@ abstract class TrainingService {
 }
 ```
 The parent class of TrainingService has a few abstract functions, users need to inherit the parent class and implement all of these abstract functions.  
-For more information about how to write your own TrainingService, please [refer](https://github.com/SparkSnail/nni/blob/dev-trainingServiceDoc/docs/en_US/TrainingService/HowToImplementTrainingService.md).
+For more information about how to write your own TrainingService, please [refer](https://github.com/microsoft/nni/blob/master/docs/en_US/TrainingService/HowToImplementTrainingService.md).
diff --git a/docs/en_US/Tuner/GridsearchTuner.md b/docs/en_US/Tuner/GridsearchTuner.md
index 5825abd04b..b8b1a9230e 100644
--- a/docs/en_US/Tuner/GridsearchTuner.md
+++ b/docs/en_US/Tuner/GridsearchTuner.md
@@ -3,4 +3,6 @@ Grid Search on NNI
 
 ## Grid Search
 
-Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../Tutorial/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
\ No newline at end of file
+Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. 
+
+Note that the only acceptable types of search space are `choice`, `quniform`, `randint`. 
\ No newline at end of file
diff --git a/docs/en_US/Tutorial/ExperimentConfig.md b/docs/en_US/Tutorial/ExperimentConfig.md
index 97effd2b83..cb5dfa4bc6 100644
--- a/docs/en_US/Tutorial/ExperimentConfig.md
+++ b/docs/en_US/Tutorial/ExperimentConfig.md
@@ -1,8 +1,8 @@
 # Experiment config reference
 
-A config file is needed when create an experiment, the path of the config file is provide to nnictl.
-The config file is written in YAML format, and need to be written correctly.
-This document describes the rule to write config file, and will provide some examples and templates.
+A config file is needed when creating an experiment. The path of the config file is provided to `nnictl`.
+The config file is in YAML format.
+This document describes the rules to write the config file, and provides some examples and templates.
 
 - [Experiment config reference](#Experiment-config-reference)
   - [Template](#Template)
@@ -519,6 +519,10 @@ machineList:
 
       __azureShare__ is the share of the azure file storage.
 
+  * __uploadRetryCount__
+
+    If upload files to azure storage failed, NNI will retry the process of uploading, this field will specify the number of attempts to re-upload files.
+
 * __paiConfig__
 
   * __userName__
diff --git a/docs/en_US/Tutorial/SearchSpaceSpec.md b/docs/en_US/Tutorial/SearchSpaceSpec.md
index b892ea49c9..ea0d6bdf9e 100644
--- a/docs/en_US/Tutorial/SearchSpaceSpec.md
+++ b/docs/en_US/Tutorial/SearchSpaceSpec.md
@@ -27,9 +27,8 @@ All types of sampling strategies and their parameter are listed here:
 
 * `{"_type": "choice", "_value": options}`
 
-  * Which means the variable's value is one of the options. Here 'options' should be a list. Each element of options is a number of string. It could also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space could be seen as conditional variables.
-
-  * An simple [example](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json) of [nested] search space definition. If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a key `_name` in this dict, which helps you to identify which element is chosen. Accordingly, here is a [sample](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json) which users can get from nni with nested search space definition. Tuners which support nested search space is as follows:
+  * Which means the variable's value is one of the options. Here `options` should be a list of numbers or a list of strings. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.
+  * `options` could also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space could be seen as conditional variables. Here is an simple [example of nested search space definition](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json). If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a key `_name` in this dict, which helps you to identify which element is chosen. Accordingly, here is a [sample](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json) which users can get from nni with nested search space definition. Tuners which support nested search space are as follows:
 
     - Random Search 
     - TPE
diff --git a/docs/img/nni-1.png b/docs/img/nni-1.png
new file mode 100644
index 0000000000..b31e66b2ee
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diff --git a/docs/img/release-1-title-1.png b/docs/img/release-1-title-1.png
new file mode 100644
index 0000000000..6bcbff9087
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diff --git a/docs/img/release-1-title-2.png b/docs/img/release-1-title-2.png
new file mode 100644
index 0000000000..cbdf2c3c27
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diff --git a/docs/zh_CN/AdvancedFeature/GeneralNasInterfaces.md b/docs/zh_CN/AdvancedFeature/GeneralNasInterfaces.md
index 1fa699cb3b..c002614c27 100644
--- a/docs/zh_CN/AdvancedFeature/GeneralNasInterfaces.md
+++ b/docs/zh_CN/AdvancedFeature/GeneralNasInterfaces.md
@@ -1,6 +1,6 @@
-# 神经网络架构搜索的通用编程接口（测试版）
+# 神经网络架构搜索的 NNI 编程接口（NAS）
 
-** 这是一个测试中的功能，目前只实现了通用的 NAS 编程接口。 在随后的版本中会支持权重共享。*
+** 这是**实验性的功能**。 目前，仅实现了通用的 NAS 编程接口。 在随后的版本中会支持权重共享。*
 
 自动化的神经网络架构（NAS）搜索在寻找更好的模型方面发挥着越来越重要的作用。 最近的研究工作证明了自动化 NAS 的可行性，并发现了一些超越手动设计和调整的模型。 代表算法有 [NASNet](https://arxiv.org/abs/1707.07012)，[ENAS](https://arxiv.org/abs/1802.03268)，[DARTS](https://arxiv.org/abs/1806.09055)，[Network Morphism](https://arxiv.org/abs/1806.10282)，以及 [Evolution](https://arxiv.org/abs/1703.01041) 等。 新的算法还在不断涌现。 然而，实现这些算法需要很大的工作量，且很难重用其它算法的代码库来实现。
 
@@ -125,7 +125,7 @@ for _ in range(num):
 
 ***oneshot_mode***: 遵循[论文](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf)中的训练方法。 与 enas_mode 通过训练大量子图来训练全图有所不同，oneshot_mode 中构建了全图，并将 dropout 添加到候选的输入以及候选的输出操作中。 然后像其它深度学习模型一样进行训练。 [详细说明](#OneshotMode)。 （当前仅支持 TensorFlow）。
 
-要使用 oneshot_mode，需要在配置的 `trial` 部分增加如下字段。 此模式不需要 Tuner，因此不用在配置文件中指定 Tuner。 （注意，当前仍然需要在配置文件中指定任一一个 Tuner。）此模式下也不需要添加 `nni.training_update`，因为在训练过程中不需要特别的更新过程。
+要使用 oneshot_mode，需要在配置的 `trial` 部分增加如下字段。 在此模式中，不需要使用 Tuner，只需要在配置文件中添加任意一个Tuner。 此外，也不需要增加 `nni.training_update`，因为在训练过程中不需要更新。
 
 ```diff
 trial:
@@ -139,7 +139,7 @@ trial:
 
 ***darts_mode***: 参考 [论文](https://arxiv.org/abs/1806.09055)中的训练方法。 与 oneshot_mode 类似。 有两个不同之处，首先 darts_mode 只将架构权重添加到候选操作的输出中，另外是交错的来训练模型权重和架构权重。 [详细说明](#DartsMode)。
 
-要使用 darts_mode，需要在配置的 `trial` 部分增加如下字段。 此模式不需要 Tuner，因此不用在配置文件中指定 Tuner。 （注意，当前仍需要在配置文件中指定任意一个 Tuner。）
+要使用 darts_mode，需要在配置的 `trial` 部分增加如下字段。 在此模式中，不需要使用 Tuner，只需要在配置文件中添加任意一个Tuner。
 
 ```diff
 trial:
@@ -166,9 +166,9 @@ for _ in range(num):
 
 ### enas_mode
 
-在 enas_mode 中，编译后的 Trial 代码会构建完整的图形（而不是子图），会接收所选择的架构，并在完整的图形上对此体系结构进行小型的批处理训练，然后再请求另一个架构。 它通过 [NNI 多阶段 Experiment](./multiPhase.md) 来支持。
+在 enas_mode 中，编译后的 Trial 代码会构建完整的图形（而不是子图），会接收所选择的架构，并在完整的图形上对此体系结构进行小型的批处理训练，然后再请求另一个架构。 通过 [NNI 多阶段 Experiment](./MultiPhase.md) 来支持。
 
-具体来说，使用 TensorFlow 的 Trial，通过 TensorFlow 变量来作为信号，并使用 TensorFlow 的条件函数来控制搜索空间（全图）来提高灵活性。这意味着根据这些信号，可以变为不同的多个子图。 [这是 enas_mode]() 的示例。
+具体来说，使用 TensorFlow 的 Trial，通过 TensorFlow 变量来作为信号，并使用 TensorFlow 的条件函数来控制搜索空间（全图）来提高灵活性。这意味着根据这些信号，可以变为不同的多个子图。 [这里](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nas/enas_mode)是 enas_mode 的示例。
 
 <a name="OneshotMode"></a>
 
@@ -178,7 +178,7 @@ for _ in range(num):
 
 ![](../../img/oneshot_mode.png)
 
-如[论文](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf)中的建议，应该为每层的输入实现 Dropout 方法。 当 0 < r < 1 是模型超参的取值范围（默认值为 0.01），k 是某层可选超参的数量，Dropout 比率设为 r^(1/k)。 fan-in 越高，每个输入被丢弃的可能性越大。 但某层丢弃所有可选输入的概率是常数，与 fan-in 无关。 假设 r = 0.05。 如果某层有 k = 2 个可选的输入，每个输入都会以独立的 0.051/2 ≈ 0.22 的概率被丢弃，也就是说有 0.78 的概率被保留。 如果某层有 k = 7 个可选的输入，每个输入都会以独立的 0.051/7 ≈ 0.65 的概率被丢弃，也就是说有 0.35 的概率被保留。 在这两种情况下，丢弃所有可选输入的概率是 5%。 候选操作的输出会通过同样的方法被丢弃。 [这里]()是 oneshot_mode 的示例。
+如[论文](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf)中的建议，应该为每层的输入实现 Dropout 方法。 当 0 < r < 1 是模型超参的取值范围（默认值为 0.01），k 是某层可选超参的数量，Dropout 比率设为 r^(1/k)。 fan-in 越高，每个输入被丢弃的可能性越大。 但某层丢弃所有可选输入的概率是常数，与 fan-in 无关。 假设 r = 0.05。 如果某层有 k = 2 个可选的输入，每个输入都会以独立的 0.051/2 ≈ 0.22 的概率被丢弃，也就是说有 0.78 的概率被保留。 如果某层有 k = 7 个可选的输入，每个输入都会以独立的 0.051/7 ≈ 0.65 的概率被丢弃，也就是说有 0.35 的概率被保留。 在这两种情况下，丢弃所有可选输入的概率是 5%。 候选操作的输出会通过同样的方法被丢弃。 [这里](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nas/oneshot_mode)是 oneshot_mode 的示例。
 
 <a name="DartsMode"></a>
 
@@ -188,7 +188,7 @@ for _ in range(num):
 
 ![](../../img/darts_mode.png)
 
-在 `nni.training_update` 中，TensorFlow 的 MomentumOptimizer 通过传递的 `loss` 和 `feed_dict` 来训练架构权重。 [这是 darts_mode]() 的示例。
+在 `nni.training_update` 中，TensorFlow 的 MomentumOptimizer 通过传递的 `loss` 和 `feed_dict` 来训练架构权重。 [这里](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nas/darts_mode)是 darts_mode 的示例。
 
 ### [**待实现**] One-Shot NAS 的多 Trial 任务。
 
diff --git a/docs/zh_CN/CommunitySharings/HpoComparision.md b/docs/zh_CN/CommunitySharings/HpoComparision.md
index 428d6264ea..bbe0895131 100644
--- a/docs/zh_CN/CommunitySharings/HpoComparision.md
+++ b/docs/zh_CN/CommunitySharings/HpoComparision.md
@@ -2,7 +2,7 @@
 
 *匿名作者*
 
-超参优化算法在几个问题上的对比。
+超参优化算法（HPO）在几个问题上的对比。
 
 超参数优化算法如下：
 
diff --git a/docs/zh_CN/CommunitySharings/SptagAutoTune.md b/docs/zh_CN/CommunitySharings/SptagAutoTune.md
new file mode 100644
index 0000000000..456d47b621
--- /dev/null
+++ b/docs/zh_CN/CommunitySharings/SptagAutoTune.md
@@ -0,0 +1,7 @@
+# 使用 NNI 为 SPTAG 自动调参
+
+[SPTAG](https://github.com/microsoft/SPTAG) (Space Partition Tree And Graph) 是大规模向量的最近邻搜索的工具，由[微软研究院（MSR）](https://www.msra.cn/)和[微软必应团队](https://www.bing.com/)联合发布。
+
+此工具假设样本可以表示为向量，并且能通过 L2 或余弦算法来比较距离。 输入一个查询向量，会返回与其 L2 或余弦距离最小的一组向量。 SPTAG 提供了两种方法：kd-tree 与其的相关近邻图 (SPTAG-KDT)，以及平衡 k-means 树与其的相关近邻图 （SPTAG-BKT）。 SPTAG-KDT 在索引构建效率上较好，而 SPTAG-BKT 在搜索高维度数据的精度上较好。
+
+在 SPTAG中，有几十个参数可以根据特定的场景或数据集进行调优。 NNI 是用来自动化调优这些参数的绝佳工具。 SPTAG 的作者尝试了使用 NNI 来进行自动调优，并轻松找到了性能较好的参数组合，并在 SPTAG [文档](https://github.com/microsoft/SPTAG/blob/master/docs/Parameters.md)中进行了分享。 参考此文档了解详细教程。
\ No newline at end of file
diff --git a/docs/zh_CN/CommunitySharings/community_sharings.rst b/docs/zh_CN/CommunitySharings/community_sharings.rst
index 98e6633473..509b68aa29 100644
--- a/docs/zh_CN/CommunitySharings/community_sharings.rst
+++ b/docs/zh_CN/CommunitySharings/community_sharings.rst
@@ -8,6 +8,7 @@
     :maxdepth: 2
 
     Recommenders 中使用 NNI<RecommendersSvd>
+    使用 NNI 为 SPTAG 自动调参 <SptagAutoTune>
     神经网络结构搜索（NAS）的对比<NasComparision>
     超参调优算法的对比<HpoComparision>
     TPE 的并行优化<ParallelizingTpeSearch>
diff --git a/docs/zh_CN/Release_v1.0.md b/docs/zh_CN/Release_v1.0.md
new file mode 100644
index 0000000000..6bec474194
--- /dev/null
+++ b/docs/zh_CN/Release_v1.0.md
@@ -0,0 +1,29 @@
+<p align="center">
+<img src=".././img/release-1-title-1.png" width="100%" />
+</p>
+
+从 2018 年 9 月到 2019 年 9 月，我们一直在前进 ...
+
+**好消息！**&nbsp;&nbsp;NNI v1.0 带来了更强的**伸缩性**和**易用性**。 基于各种[调参算法](./Tuner/BuiltinTuner.md)，NNI 已经支持了超参调优，神经网络结构搜索，自动特侦工程等，对算法工程师非常有用的功能。除此之外，NNI v1.0 带来了大量的改进，包括调优算法的优化，[Web 界面简化并更加直观](./Tutorial/WebUI.md)，以及[更多样的平台](./TrainingService/SupportTrainingService.md)。 NNI 已经成为了一个更加智能的自动机器学习（AutoML）工具包。
+
+  
+  
+  
+
+<p align="center">
+<img src=".././img/nni-1.png" width="80%" />
+</p>
+
+  
+  
+
+<p align="center">
+<img src=".././img/release-1-title-2.png" width="100%" />
+</p>
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**第一步**： 根据[教程](./Tutorial/Installation.md)安装 NNI v1.0。  
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**第二步**：找到 "Hello world" 示例，按照[教程](./Tutorial/QuickStart.md)入门。   
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**第三步**：熟悉 [Web 界面](./Tutorial/WebUI.md)，用 NNI 进行自动机器学习！  
+
+
+全自动工具极大地提高了调优过程的效率。 关于 v1.0 的更多细节，可参考 [Release 1.0](https://github.com/microsoft/nni/releases)。 关于进一步计划，可参考[路线图](https://github.com/microsoft/nni/wiki/Roadmap)。 此外，欢迎更多的参与者加入我们。可参考[如何贡献](./Tutorial/Contributing.md)，来了解多种参与方法。
\ No newline at end of file
diff --git a/docs/zh_CN/SupportedFramework_Library.md b/docs/zh_CN/SupportedFramework_Library.md
new file mode 100644
index 0000000000..481a921a4b
--- /dev/null
+++ b/docs/zh_CN/SupportedFramework_Library.md
@@ -0,0 +1,60 @@
+# 框架和库的支持
+
+通过内置的 Python API，NNI 天然支持所有 Python (` 版本 >= 3.5`) 语言的 AI 框架，可使用所有超参调优和神经网络搜索算法。 NNI 还为常见场景提供了一些示例和教程，使上手更容易。
+
+## 支持的 AI 框架
+
+* **[PyTorch]** https://github.com/pytorch/pytorch
+
+* [MNIST-pytorch](../../examples/trials/mnist-distributed-pytorch)  
+    
+* [CIFAR-10](TrialExample/Cifar10Examples.md)  
+    
+* [TGS salt identification chanllenge](../../examples/trials/kaggle-tgs-salt/README_zh_CN.md)  
+    
+* [Network morphism](../../examples/trials/network_morphism/README_zh_CN.md)  
+    
+
+* **[TensorFlow]** https://github.com/tensorflow/tensorflow
+
+* [MNIST-tensorflow](../../examples/trials/mnist-distributed)  
+    
+* [Squad](../../examples/trials/ga_squad/README_zh_CN.md)  
+    
+
+* **[Keras]** https://github.com/keras-team/keras
+
+* [MNIST-keras](../../examples/trials/mnist-keras)  
+    
+* [Network morphism](../../examples/trials/network_morphism/README_zh_CN.md)  
+    
+
+* **[MXNet]** https://github.com/apache/incubator-mxnet
+* **[Caffe2]** https://github.com/BVLC/caffe
+* **[CNTK (Python 语言)]** https://github.com/microsoft/CNTK
+* **[Spark MLlib]** http://spark.apache.org/mllib/
+* **[Chainer]** https://chainer.org/
+* **[Theano]** https://pypi.org/project/Theano/   
+    
+
+如果能[贡献更多示例](Tutorial/Contributing.md)，会对其他 NNI 用户有很大的帮助。
+
+## 支持的库
+
+NNI 也支持其它 Python 库，包括一些基于 GBDT 的算法：XGBoost, CatBoost 以及 lightGBM。
+
+* **[Scikit-learn]** https://scikit-learn.org/stable/
+
+* [Scikit-learn](TrialExample/SklearnExamples.md)  
+    
+
+* **[XGBoost]** https://xgboost.readthedocs.io/en/latest/
+* **[CatBoost]** https://catboost.ai/
+* **[LightGBM]** https://lightgbm.readthedocs.io/en/latest/
+
+* [Auto-gbdt](TrialExample/GbdtExample.md)  
+    
+
+这只是 NNI 支持的一小部分库。 如果对 NNI 感兴趣，可参考[教程](TrialExample/Trials.md)来继续学习。
+
+除了这些案例，也欢迎更多的用户将 NNI 应用到自己的工作中，如果有任何疑问，请参考[实现 Trial](TrialExample/Trials.md)。 如果想成为 NNI 的贡献者，无论是分享示例，还是实现 Tuner 或其它内容，我们都非常期待您的参与。更多信息请[参考这里](Tutorial/Contributing.md)。
\ No newline at end of file
diff --git a/docs/zh_CN/TrainingService/LocalMode.md b/docs/zh_CN/TrainingService/LocalMode.md
index 3156e3a45c..1aec4e3e9b 100644
--- a/docs/zh_CN/TrainingService/LocalMode.md
+++ b/docs/zh_CN/TrainingService/LocalMode.md
@@ -57,7 +57,7 @@
     }
     
 
-参考 [SearchSpaceSpec.md](../Tutorial/SearchSpaceSpec.md) 进一步了解搜索空间。
+参考[定义搜索空间](../Tutorial/SearchSpaceSpec.md)进一步了解。
 
 > 第三步：定义 Experiment
 > 
diff --git a/docs/zh_CN/TrainingService/PaiMode.md b/docs/zh_CN/TrainingService/PaiMode.md
index 47d6b1531a..ee29276fd2 100644
--- a/docs/zh_CN/TrainingService/PaiMode.md
+++ b/docs/zh_CN/TrainingService/PaiMode.md
@@ -57,7 +57,36 @@ paiConfig:
 * shmMB 
     * 可选。 设置 OpenPAI 的 shmMB，即 Docker 中的共享内存。
 * authFile 
-    * 可选，设置在使用 OpenPAI 时的私有 Docker 认证文件路径。[参考文档](https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.md#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job)。
+    * 可选。在使用 pai 模式时，为私有 Docker 仓库设置认证文件，[见参考文档](https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.md#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job)。提供 authFile 的本地路径即可， NNI 会上传此文件。
+
+* portList
+    
+    * 可选。 设置 OpenPAI 的 portList。指定了容器中使用的端口列表，[参考文档](https://github.com/microsoft/pai/blob/b2324866d0280a2d22958717ea6025740f71b9f0/docs/job_tutorial.md#specification)。  
+        示例如下：
+        portList:
+          - label: test
+            beginAt: 8080
+            portNumber: 2
+        
+    
+    假设需要在 MNIST 示例中使用端口来运行 TensorBoard。 第一步是编写 `mnist.py` 的包装脚本 `launch_pai.sh`。
+    
+    ```bash
+    export TENSORBOARD_PORT=PAI_PORT_LIST_${PAI_CURRENT_TASK_ROLE_NAME}_0_tensorboard
+    tensorboard --logdir . --port ${!TENSORBOARD_PORT} &
+    python3 mnist.py
+    ```
+    
+    portList 的配置部分如下：
+    
+    ```yaml
+    trial:
+    command: bash launch_pai.sh
+    portList:
+      - label: tensorboard
+        beginAt: 0
+        portNumber: 1
+    ```
 
 完成并保存 NNI Experiment 配置文件后（例如可保存为：exp_pai.yml），运行以下命令：
 
diff --git a/docs/zh_CN/TrainingService/SupportTrainingService.md b/docs/zh_CN/TrainingService/SupportTrainingService.md
new file mode 100644
index 0000000000..fbf6f6a2cd
--- /dev/null
+++ b/docs/zh_CN/TrainingService/SupportTrainingService.md
@@ -0,0 +1,39 @@
+# 训练平台
+
+NNI 为 Trial 任务实现了训练平台。 NNI 支持[本机](./LocalMode.md), [远程](./RemoteMachineMode.md), [OpenPAI](./PaiMode.md), [Kubeflow](./KubeflowMode.md) 和 [FrameworkController](./FrameworkControllerMode.md) 这些内置的训练平台。  
+NNI 不仅提供了这些内置的训练平台，还提供了轻松连接自己训练平台的方法。
+
+## 内置训练平台
+
+| 训练平台                                                    | 简介                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| ------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [**本机**](./LocalMode.md)                                | NNI 支持在本机运行实验，称为 local 模式。 local 模式表示 NNI 会在运行 NNI Manager 进程计算机上运行 Trial 任务，支持 GPU 调度功能。                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| [**远程计算机**](./RemoteMachineMode.md)                     | NNI 支持通过 SSH 通道在多台计算机上运行 Experiment，称为 remote 模式。 NNI 需要这些计算机的访问权限，并假定已配置好了深度学习训练环境。 NNI 将在远程计算机上中提交 Trial 任务，并根据 GPU 资源调度 Trial 任务。                                                                                                                                                                                                                                                                                                                                                                                                   |
+| [**OpenPAI**](./PaiMode.md)                             | NNI 支持在 [OpenPAI](https://github.com/Microsoft/pai) （简称 pai）上运行 Experiment，即 pai 模式。 在使用 NNI 的 pai 模式前, 需要有 [OpenPAI](https://github.com/Microsoft/pai) 群集及其账户。 如果没有 OpenPAI，参考[这里](https://github.com/Microsoft/pai#how-to-deploy)来进行部署。 在 pai 模式中，会在 Docker 创建的容器中运行 Trial 程序。                                                                                                                                                                                                                                                       |
+| [**Kubeflow**](./KubeflowMode.md)                       | NNI 支持在 [Kubeflow](https://github.com/kubeflow/kubeflow)上运行，称为 kubeflow 模式。 在开始使用 NNI 的 Kubeflow 模式前，需要有一个 Kubernetes 集群，可以是私有部署的，或者是 [Azure Kubernetes Service(AKS)](https://azure.microsoft.com/zh-cn/services/kubernetes-service/)，并需要一台配置好 [kubeconfig](https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/) 的 Ubuntu 计算机连接到此 Kubernetes 集群。 如果不熟悉 Kubernetes，可先浏览[这里](https://kubernetes.io/docs/tutorials/kubernetes-basics/)。 在 kubeflow 模式下，每个 Trial 程序会在 Kubernetes 集群中作为一个 Kubeflow 作业来运行。 |
+| [**FrameworkController**](./FrameworkControllerMode.md) | NNI 支持使用 [FrameworkController](https://github.com/Microsoft/frameworkcontroller)，来运行 Experiment，称之为 frameworkcontroller 模式。 FrameworkController 构建于 Kubernetes 上，用于编排各种应用。这样，可以不用为某个深度学习框架安装 Kubeflow 的 tf-operator 或 pytorch-operator 等。 而直接用 FrameworkController 作为 NNI Experiment 的训练平台。                                                                                                                                                                                                                                            |
+
+
+## 实现训练平台
+
+TrainingService 在设计上为了便于实现，将平台相关的公共属性抽象成类。用户只需要继承这个抽象类，并根据平台特点实现子类，便能够实现 TrainingService。  
+TrainingService 的声明如下：
+
+    abstract class TrainingService {
+        public abstract listTrialJobs(): Promise<TrialJobDetail[]>;
+        public abstract getTrialJob(trialJobId: string): Promise<TrialJobDetail>;
+        public abstract addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void;
+        public abstract removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void;
+        public abstract submitTrialJob(form: JobApplicationForm): Promise<TrialJobDetail>;
+        public abstract updateTrialJob(trialJobId: string, form: JobApplicationForm): Promise<TrialJobDetail>;
+        public abstract get isMultiPhaseJobSupported(): boolean;
+        public abstract cancelTrialJob(trialJobId: string, isEarlyStopped?: boolean): Promise<void>;
+        public abstract setClusterMetadata(key: string, value: string): Promise<void>;
+        public abstract getClusterMetadata(key: string): Promise<string>;
+        public abstract cleanUp(): Promise<void>;
+        public abstract run(): Promise<void>;
+    }
+    
+
+TrainingService 的父类有一些抽象函数，用户需要继承父类并实现所有这些抽象函数。  
+有关如何实现 TrainingService 的更多信息，[参考这里](https://github.com/microsoft/nni/blob/master/docs/zh_CN/TrainingService/HowToImplementTrainingService.md)。
\ No newline at end of file
diff --git a/docs/zh_CN/Tuner/BuiltinTuner.md b/docs/zh_CN/Tuner/BuiltinTuner.md
index 6fe36f2dd0..132e5af66c 100644
--- a/docs/zh_CN/Tuner/BuiltinTuner.md
+++ b/docs/zh_CN/Tuner/BuiltinTuner.md
@@ -115,6 +115,12 @@ tuner:
 
 此算法对计算资源的需求相对较高。 需要非常大的初始种群，以免落入局部最优中。 如果 Trial 时间很短，或者使用了 Assessor，就非常适合此算法。 如果 Trial 代码支持权重迁移，即每次 Trial 会从上一轮继承已经收敛的权重，建议使用此算法。 这会大大提高训练速度。 [详细说明](./EvolutionTuner.md)
 
+**参数**
+
+* **optimize_mode** (*maximize 或 minimize, 可选项, 默认值为 maximize*) - 如果为 'maximize'，表示 Tuner 的目标是将指标最大化。 如果为 'minimize'，表示 Tuner 的目标是将指标最小化。
+
+* **population_size** (*int 类型(大于 0), 可选项, 默认值为 20*) - 表示遗传 Tuner 中的种群（Trial 数量）。
+
 **示例**
 
 ```yaml
@@ -123,6 +129,7 @@ tuner:
   builtinTunerName: Evolution
   classArgs:
     optimize_mode: maximize
+    population_size: 100
 ```
 
 <br />
diff --git a/docs/zh_CN/Tuner/GridsearchTuner.md b/docs/zh_CN/Tuner/GridsearchTuner.md
index a1003d19a5..90b4279c39 100644
--- a/docs/zh_CN/Tuner/GridsearchTuner.md
+++ b/docs/zh_CN/Tuner/GridsearchTuner.md
@@ -2,4 +2,6 @@
 
 ## Grid Search（遍历搜索）
 
-Grid Search 会穷举定义在搜索空间文件中的所有超参组合。 注意，搜索空间仅支持 `choice`, `quniform`, `qloguniform`。 `quniform` 和 `qloguniform` 中的 **数字 `q` 有不同的含义（与[搜索空间](../Tutorial/SearchSpaceSpec.md)说明不同）。 这里的意义是在 `low` 和 `high` 之间均匀取值的数量。</p>
\ No newline at end of file
+Grid Search 会穷举定义在搜索空间文件中的所有超参组合。
+
+注意，搜索空间仅支持 `choice`, `quniform`, `randint`。
\ No newline at end of file
diff --git a/docs/zh_CN/Tutorial/ExperimentConfig.md b/docs/zh_CN/Tutorial/ExperimentConfig.md
index 700f8c00d2..21adc03b76 100644
--- a/docs/zh_CN/Tutorial/ExperimentConfig.md
+++ b/docs/zh_CN/Tutorial/ExperimentConfig.md
@@ -1,6 +1,6 @@
 # Experiment（实验）配置参考
 
-创建 Experiment 时，需要给 nnictl 命令提供配置文件的路径。 配置文件是 YAML 格式，需要保证其格式正确。 本文介绍了配置文件的内容，并提供了一些示例和模板。
+创建 Experiment 所需要的配置文件。 配置文件的路径会传入 `nnictl` 命令。 配置文件的格式为 YAML。 本文介绍了配置文件的内容，并提供了一些示例和模板。
 
 - [Experiment（实验）配置参考](#Experiment-config-reference) 
   - [模板](#Template)
@@ -549,6 +549,10 @@ machineList:
     - **azureShare**
       
       **azureShare** 是 Azure 文件存储的共享参数。
+  
+  - **uploadRetryCount**
+    
+    如果上传文件至 Azure Storage 失败，NNI 会重试。此字段指定了重试的次数。
 
 - **paiConfig**
   
diff --git a/docs/zh_CN/Tutorial/Nnictl.md b/docs/zh_CN/Tutorial/Nnictl.md
index db4adeea01..a363c0f5c6 100644
--- a/docs/zh_CN/Tutorial/Nnictl.md
+++ b/docs/zh_CN/Tutorial/Nnictl.md
@@ -123,6 +123,7 @@ nnictl 支持的命令：
   | ---------- | ----- | --- | -------------------------------- |
   | id         | False |     | 要停止的 Experiment 标识               |
   | --port, -p | False |     | 要停止的 Experiment 使用的 RESTful 服务端口 |
+  | --all, -a  | False |     | 停止所有 Experiment                  |
 
 * 详细信息及样例
   
@@ -147,10 +148,10 @@ nnictl 支持的命令：
         ```
         
   
-  4. 可使用 'nnictl stop all' 来停止所有的 Experiment。
+  4. 可使用 'nnictl stop --all' 来停止所有的 Experiment。
     
         ```bash
-        nnictl stop all
+        nnictl stop --all
         ```
         
   
diff --git a/docs/zh_CN/Tutorial/SearchSpaceSpec.md b/docs/zh_CN/Tutorial/SearchSpaceSpec.md
index 7e2f6777f5..238d640e01 100644
--- a/docs/zh_CN/Tutorial/SearchSpaceSpec.md
+++ b/docs/zh_CN/Tutorial/SearchSpaceSpec.md
@@ -10,11 +10,11 @@
 
 ```yaml
 {
-    "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
-    "conv_size":{"_type":"choice","_value":[2,3,5,7]},
-    "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
-    "batch_size":{"_type":"choice","_value":[50, 250, 500]},
-    "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
+    "dropout_rate": {"_type": "uniform", "_value": [0.1, 0.5]},
+    "conv_size": {"_type": "choice", "_value": [2, 3, 5, 7]},
+    "hidden_size": {"_type": "choice", "_value": [124, 512, 1024]},
+    "batch_size": {"_type": "choice", "_value": [50, 250, 500]},
+    "learning_rate": {"_type": "uniform", "_value": [0.0001, 0.1]}
 }
 
 ```
@@ -25,61 +25,60 @@
 
 所有采样策略和参数如下：
 
-* {"_type":"choice","_value":options}
+* `{"_type": "choice", "_value": options}`
   
-  * 表示变量的值是选项之一。 这里的 'options' 是一个数组。 选项的每个元素都是字符串。 也可以是嵌套的子搜索空间。此子搜索空间仅在相应的元素选中后才起作用。 该子搜索空间中的变量可看作是条件变量。
-  
-  * [nested] 搜索空间定义的简单[示例](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json)。 如果选项列表中的元素是 dict，则它是一个子搜索空间，对于内置的 Tuner，必须在此 dict 中添加键 “_name”，这有助于标识选中的元素。 相应的，这是使用从 NNI 获得的嵌套搜索空间的[示例](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json)。 以下 Tuner 支持嵌套搜索空间：
+  * 表示变量的值是选项之一。 这里的 `options` 应该是字符串或数值的列表。 可将任意对象（如子数组，数字与字符串的混合值或者空值）存入此数组中，但可能会产生不可预料的行为。
+  * `options` 也可以是嵌套的子搜索空间。此子搜索空间仅在相应的元素选中后才起作用。 该子搜索空间中的变量可看作是条件变量。 <a [嵌套搜索空间的简单示例](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json)。 如果选项列表中的元素是 dict，则它是一个子搜索空间，对于内置的 Tuner，必须在此 dict 中添加键 `_name`，这有助于标识选中的元素。 相应的，这是使用从 NNI 获得的嵌套搜索空间的[示例](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json)。 以下 Tuner 支持嵌套搜索空间：
     
     * Random Search（随机搜索） 
     * TPE
     * Anneal（退火算法）
     * Evolution
 
-* {"_type":"randint","_value":[lower, upper]}
+* `{"_type": "randint", "_value": [lower, upper]}`
   
-  * 当前实现的是 "quniform" 的 "randint" 分布，随机变量的分布函数是 round(uniform(lower, upper))。 所选择值的类型是 float。 如果要使用整数，需要显式转换。
+  * 从 `lower` (包含) 到 `upper` (不包含) 中选择一个随机整数。
+  * 注意：不同 Tuner 可能对 `randint` 有不同的实现。 一些 Tuner（例如，TPE，GridSearch）将从低到高无序选择，而其它一些（例如，SMAC）则有顺序。 如果希望所有 Tuner 都有序，可使用 `quniform` 并设置 `q=1`。
 
-* {"_type":"uniform","_value":[low, high]}
+* `{"_type": "uniform", "_value": [low, high]}`
   
   * 变量是 low 和 high 之间均匀分布的值。
   * 当优化时，此变量值会在两侧区间内。
 
-* {"_type":"quniform","_value":[low, high, q]}
-  
-  * 变量值为 clip(round(uniform(low, high) / q) * q, low, high)，clip 操作用于约束生成值的边界。 例如，_value 为 [0, 10, 2.5]，可取的值为 [0, 2.5, 5.0, 7.5, 10.0]; _value 为 [2, 10, 5]，可取的值为 [2, 5, 10]。
+* `{"_type": "quniform", "_value": [low, high, q]}`
   
+  * 变量值为 `clip(round(uniform(low, high) / q) * q, low, high)`，clip 操作用于约束生成值的边界。 例如，`_value` 为 [0, 10, 2.5]，可取的值为 [0, 2.5, 5.0, 7.5, 10.0]; `_value` 为 [2, 10, 5]，可取的值为 [2, 5, 10]。
   * 适用于离散，同时反映了某种"平滑"的数值，但上下限都有限制。 如果需要从范围 [low, high] 中均匀选择整数，可以如下定义 `_value`：`[low, high, 1]`。
 
-* {"_type":"loguniform","_value":[low, high]}
+* `{"_type": "loguniform", "_value": [low, high]}`
   
   * 变量值在范围 [low, high] 中是 loguniform 分布，如 exp(uniform(log(low), log(high)))，因此返回值是对数均匀分布的。
   * 当优化时，此变量必须是正数。
 
-* {"_type":"qloguniform","_value":[low, high, q]}
+* `{"_type": "qloguniform", "_value": [low, high, q]}`
   
-  * 变量值为 clip(round(loguniform(low, high) / q) * q, low, high)，clip 操作用于约束生成值的边界。
+  * 变量值为 `clip(round(loguniform(low, high) / q) * q, low, high)`，clip 操作用于约束生成值的边界。
   * 适用于值是“平滑”的离散变量，但上下限均有限制。
 
-* {"_type":"normal","_value":[mu, sigma]}
+* `{"_type": "normal", "_value": [mu, sigma]}`
   
   * 变量值为实数，且为正态分布，均值为 mu，标准方差为 sigma。 优化时，此变量不受约束。
 
-* {"_type":"qnormal","_value":[mu, sigma, q]}
+* `{"_type": "qnormal", "_value": [mu, sigma, q]}`
   
-  * 这表示变量值会类似于 round(normal(mu, sigma) / q) * q
+  * 这表示变量值会类似于 `round(normal(mu, sigma) / q) * q`
   * 适用于在 mu 周围的离散变量，且没有上下限限制。
 
-* {"_type":"lognormal","_value":[mu, sigma]}
+* `{"_type": "lognormal", "_value": [mu, sigma]}`
   
-  * 变量值为 exp(normal(mu, sigma)) 分布，范围值是对数的正态分布。 当优化时，此变量必须是正数。
+  * 变量值为 `exp(normal(mu, sigma))` 分布，范围值是对数的正态分布。 当优化时，此变量必须是正数。
 
-* {"_type":"qlognormal","_value":[mu, sigma, q]}
+* `{"_type": "qlognormal", "_value": [mu, sigma, q]}`
   
-  * 这表示变量值会类似于 round(exp(normal(mu, sigma)) / q) * q
+  * 这表示变量值会类似于 `round(exp(normal(mu, sigma)) / q) * q`
   * 适用于值是“平滑”的离散变量，但某一边有界。
 
-* {"_type":"mutable_layer","_value":{mutable_layer_infomation}}
+* `{"_type": "mutable_layer", "_value": {mutable_layer_infomation}}`
   
   * [神经网络架构搜索空间](../AdvancedFeature/GeneralNasInterfaces.md)的类型。 值是字典类型，键值对表示每个 mutable_layer 的名称和搜索空间。
   * 当前，只能通过 Annotation 来使用这种类型的搜索空间。因此不需要为搜索空间定义 JSON 文件，它会通过 Trial 中的 Annotation 自动生成。
diff --git a/docs/zh_CN/Tutorial/WebUI.md b/docs/zh_CN/Tutorial/WebUI.md
index ecf9b3b306..6ad6ac4234 100644
--- a/docs/zh_CN/Tutorial/WebUI.md
+++ b/docs/zh_CN/Tutorial/WebUI.md
@@ -12,7 +12,7 @@
 
 ![](../../img/webui-img/refresh-interval.png)
 
-* 支持查看并下载 Experiment 结果，以及 NNI Manager、Dispatcher 的日志文件。
+* "View" 按钮支持查看并下载 Experiment 结果，以及 NNI Manager、Dispatcher 的日志文件。
 
 ![](../../img/webui-img/download.png)
 
@@ -53,13 +53,11 @@
 
 ![](../../img/webui-img/trials_intermeidate.png)
 
-由于训练中可能有非常多的中间结果，因此中间结果图提供了过滤功能。 如果要使用过滤按钮查看部分 Trial，则需要提供数据。
+Trial 可能在训练过程中有大量中间结果。 为了更清楚的理解一些 Trial 的趋势，可以为中间结果图设置过滤。
 
-第一个输入框应该填入什么？ 可能会发现一个 Trial 的中间结果点变得更好或者更差。 换句话说，这可能是非常重要的中间结果点。 只需将其输入第一个输入框中。
+这样可以发现 Trial 在某个中间结果上会变得更好或更差。 换句话说，这是一个重要的中间结果。 如果要仔细查看这个点，可以在 #Intermediate 中输入其横坐标。
 
-选择了中间结果序号后，要输入需要了解的该中间结果序号的指标范围。 即其最小值和最大值。 如此图中，中间结果点为 9，指标范围为 60 至 80。
-
-Trial 中指标范围在 20 至 60 的 13 个结果就被过滤掉了。
+并输入这个中间结果的指标范围。 如下图所示，选择了第 4 个中间结果， 并将指标范围设置为了 0.8 -1。
 
 ![](../../img/webui-img/filter-intermediate.png)
 
diff --git a/docs/zh_CN/conf.py b/docs/zh_CN/conf.py
index c0a30c6eb0..ce306245ad 100644
--- a/docs/zh_CN/conf.py
+++ b/docs/zh_CN/conf.py
@@ -28,7 +28,7 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = 'v0.7'
+release = 'v1.0'
 
 # -- General configuration ---------------------------------------------------
 
diff --git a/examples/trials/efficientnet/.gitignore b/examples/trials/efficientnet/.gitignore
new file mode 100644
index 0000000000..d94725b323
--- /dev/null
+++ b/examples/trials/efficientnet/.gitignore
@@ -0,0 +1 @@
+EfficientNet-PyTorch
\ No newline at end of file
diff --git a/examples/trials/efficientnet/README.md b/examples/trials/efficientnet/README.md
new file mode 100644
index 0000000000..2d8f436594
--- /dev/null
+++ b/examples/trials/efficientnet/README.md
@@ -0,0 +1,19 @@
+# EfficientNet
+
+[EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
+
+Provided here are: Search space and tuners for finding the best tuple (alpha, beta, gamma) for EfficientNet-B1 with grid search, as discussed in Section 3.3 in [paper](https://arxiv.org/abs/1905.11946).
+
+## Instructions
+
+1. Set your working directory here in this directory.
+2. Run `git clone https://github.com/ultmaster/EfficientNet-PyTorch` to clone this modified version of [EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch). The modifications were done to adhere to the original [Tensorflow version](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) as close as possible (including EMA, label smoothing and etc.); also added are the part which gets parameters from tuner and reports intermediate/final results. Clone it into `EfficientNet-PyTorch`; the files like `main.py`, `train_imagenet.sh` will appear inside, as specified in the configuration files.
+3. Run `nnictl create --config config_net.yml` to find the best EfficientNet-B1. Adjust the training service (PAI/local/remote), batch size in the config files according to the environment.
+
+For training on ImageNet, read `EfficientNet-PyTorch/train_imagenet.sh`. Download ImageNet beforehand and extract it adhering to [PyTorch format](https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet) and then replace `/mnt/data/imagenet` in with the location of the ImageNet storage. This file should also be a good example to follow for mounting ImageNet into the container on OpenPAI.
+
+## Results
+
+The follow image is a screenshot, demonstrating the relationship between acc@1 and alpha, beta, gamma.
+
+![](assets/search_result.png)
\ No newline at end of file
diff --git a/examples/trials/efficientnet/README_zh_CN.md b/examples/trials/efficientnet/README_zh_CN.md
new file mode 100644
index 0000000000..2f4ac5e65f
--- /dev/null
+++ b/examples/trials/efficientnet/README_zh_CN.md
@@ -0,0 +1,19 @@
+# EfficientNet
+
+[EfficientNet: 重新思考卷积神经网络的模型尺度](https://arxiv.org/abs/1905.11946)
+
+这里提供了：使用遍历搜索为 EfficientNet-B1 找到最佳元组（alpha，beta，gamma）的搜索空间和 Tuner。参考[论文](https://arxiv.org/abs/1905.11946) 3.3。
+
+## 说明
+
+1. 设置此目录为当前目录。
+2. 运行 `git clone https://github.com/ultmaster/EfficientNet-PyTorch` 来 clone 修改过的 [EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)。 修改尽可能接近原始的 [TensorFlow 版本](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) （包括 EMA，标记平滑度等等。）；另外添加了代码从 Tuner 获取参数并回调中间和最终结果。 将其 clone 至 `EfficientNet-PyTorch`；`main.py`，`train_imagenet.sh` 等文件会在配置文件中指定的路径。
+3. 运行 `nnictl create --config config_net.yml` 来找到最好的 EfficientNet-B1。 根据环境来调整训练平台（OpenPAI、本机、远程），batch size。
+
+在 ImageNet 上的训练，可阅读 `EfficientNet-PyTorch/train_imagenet.sh`。 下载 ImageNet，并参考 [PyTorch 格式](https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet) 来解压，然后将 `/mnt/data/imagenet` 替换为 ImageNet 的路径。 此文件也是如何将 ImageNet 挂载到 OpenPAI 容器的示例。
+
+## 结果
+
+下图展示了 acc@1 和 alpha、beta、gamma 之间的关系。
+
+![](assets/search_result.png)
\ No newline at end of file
diff --git a/examples/trials/efficientnet/assets/search_result.png b/examples/trials/efficientnet/assets/search_result.png
new file mode 100644
index 0000000000..91c4f6dac7
Binary files /dev/null and b/examples/trials/efficientnet/assets/search_result.png differ
diff --git a/examples/trials/efficientnet/config_net.yml b/examples/trials/efficientnet/config_net.yml
new file mode 100644
index 0000000000..3ae75ef46c
--- /dev/null
+++ b/examples/trials/efficientnet/config_net.yml
@@ -0,0 +1,28 @@
+authorName: unknown
+experimentName: example_efficient_net
+trialConcurrency: 8
+maxExecDuration: 48h
+maxTrialNum: 100
+trainingServicePlatform: pai
+searchSpacePath: search_net.json
+useAnnotation: false
+tuner:
+  codeDir: .
+  classFileName: tuner.py
+  className: FixedProductTuner
+  classArgs:
+    product: 2
+trial:
+  codeDir: EfficientNet-PyTorch
+  command: sh train_imagenet.sh
+  cpuNum: 4
+  memoryMB: 25000
+  shmMB: 25000
+  gpuNum: 1
+  virtualCluster: nni
+  image: msranni/nni:latest
+nniManagerIp: <nni_manager_ip>
+paiConfig:
+  userName: <username>
+  passWord: <password>
+  host: <host>
diff --git a/examples/trials/efficientnet/search_net.json b/examples/trials/efficientnet/search_net.json
new file mode 100644
index 0000000000..bf45ba918d
--- /dev/null
+++ b/examples/trials/efficientnet/search_net.json
@@ -0,0 +1,14 @@
+{
+  "alpha": {
+    "_type": "quniform",
+    "_value": [1.0, 2.0, 0.1]
+  },
+  "beta": {
+    "_type": "quniform",
+    "_value": [1.0, 1.5, 0.1]
+  },
+  "gamma": {
+    "_type": "quniform",
+    "_value": [1.0, 1.5, 0.1]
+  }
+}
diff --git a/examples/trials/efficientnet/tuner.py b/examples/trials/efficientnet/tuner.py
new file mode 100644
index 0000000000..d091d40ac0
--- /dev/null
+++ b/examples/trials/efficientnet/tuner.py
@@ -0,0 +1,29 @@
+from nni.gridsearch_tuner.gridsearch_tuner import GridSearchTuner
+
+
+class FixedProductTuner(GridSearchTuner):
+    """
+    This tuner is essentially grid search, but it guarantees all the parameters with alpha * beta^2 * gamma^2 is
+    approximately `product`.
+    """
+
+    def __init__(self, product):
+        """
+        :param product: the constant provided, should be 2 in EfficientNet-B1
+        """
+        super().__init__()
+        self.product = product
+
+    def expand_parameters(self, para):
+        """
+        Filter out all qualified parameters
+        """
+        para = super().expand_parameters(para)
+        if all([key in para[0] for key in ["alpha", "beta", "gamma"]]):  # if this is an interested set
+            ret_para = []
+            for p in para:
+                prod = p["alpha"] * (p["beta"] ** 2) * (p["gamma"] ** 2)
+                if abs(prod - self.product) < 0.1:
+                    ret_para.append(p)
+            return ret_para
+        return para
diff --git a/examples/trials/mnist-pytorch/config.yml b/examples/trials/mnist-pytorch/config.yml
new file mode 100644
index 0000000000..00a95216aa
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config.yml
@@ -0,0 +1,21 @@
+authorName: default
+experimentName: example_mnist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 mnist.py
+  codeDir: .
+  gpuNum: 0
diff --git a/examples/trials/mnist-pytorch/config_assessor.yml b/examples/trials/mnist-pytorch/config_assessor.yml
new file mode 100644
index 0000000000..282f53921f
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_assessor.yml
@@ -0,0 +1,29 @@
+authorName: default
+experimentName: example_mnist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 50
+#choice: local, remote
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+assessor:
+  #choice: Medianstop, Curvefitting
+  builtinAssessorName: Curvefitting
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+    epoch_num: 20
+    threshold: 0.9
+trial:
+  command: python3 mnist.py
+  codeDir: .
+  gpuNum: 0
diff --git a/examples/trials/mnist-pytorch/config_frameworkcontroller.yml b/examples/trials/mnist-pytorch/config_frameworkcontroller.yml
new file mode 100644
index 0000000000..e01900a8ef
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_frameworkcontroller.yml
@@ -0,0 +1,41 @@
+authorName: default
+experimentName: example_mnist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai, kubeflow
+trainingServicePlatform: frameworkcontroller
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+assessor:
+  builtinAssessorName: Medianstop
+  classArgs:
+    optimize_mode: maximize
+  gpuNum: 0
+trial:
+  codeDir: .
+  taskRoles:
+    - name: worker
+      taskNum: 1
+      command: python3 mnist.py
+      gpuNum: 1
+      cpuNum: 1
+      memoryMB: 8192
+      image: msranni/nni:latest
+      frameworkAttemptCompletionPolicy:
+        minFailedTaskCount: 1
+        minSucceededTaskCount: 1
+frameworkcontrollerConfig:
+  storage: nfs
+  nfs:
+    # Your NFS server IP, like 10.10.10.10
+    server: {your_nfs_server_ip}
+    # Your NFS server export path, like /var/nfs/nni
+    path: {your_nfs_server_export_path}
\ No newline at end of file
diff --git a/examples/trials/mnist-pytorch/config_kubeflow.yml b/examples/trials/mnist-pytorch/config_kubeflow.yml
new file mode 100644
index 0000000000..c0cef8889e
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_kubeflow.yml
@@ -0,0 +1,32 @@
+authorName: default
+experimentName: example_dist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 1
+#choice: local, remote, pai, kubeflow
+trainingServicePlatform: kubeflow
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  codeDir: .
+  worker:
+    replicas: 1
+    command: python3 mnist.py
+    gpuNum: 0
+    cpuNum: 1
+    memoryMB: 8192
+    image: msranni/nni:latest
+kubeflowConfig:
+  operator: tf-operator
+  apiVersion: v1alpha2
+  storage: nfs
+  nfs:
+    server: 10.10.10.10
+    path: /var/nfs/general
\ No newline at end of file
diff --git a/examples/trials/mnist-pytorch/config_pai.yml b/examples/trials/mnist-pytorch/config_pai.yml
new file mode 100644
index 0000000000..ac64bb4ce6
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_pai.yml
@@ -0,0 +1,32 @@
+authorName: default
+experimentName: example_mnist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: pai
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 mnist.py
+  codeDir: .
+  gpuNum: 0
+  cpuNum: 1
+  memoryMB: 8196
+  #The docker image to run nni job on pai
+  image: msranni/nni:latest
+paiConfig:
+  #The username to login pai
+  userName: username
+  #The password to login pai
+  passWord: password
+  #The host of restful server of pai
+  host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/mnist-pytorch/config_windows.yml b/examples/trials/mnist-pytorch/config_windows.yml
new file mode 100644
index 0000000000..ae27d4517b
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_windows.yml
@@ -0,0 +1,21 @@
+authorName: default
+experimentName: example_mnist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python mnist.py
+  codeDir: .
+  gpuNum: 0
diff --git a/examples/trials/mnist-pytorch/mnist.py b/examples/trials/mnist-pytorch/mnist.py
new file mode 100644
index 0000000000..91c68c12a9
--- /dev/null
+++ b/examples/trials/mnist-pytorch/mnist.py
@@ -0,0 +1,160 @@
+"""
+A deep MNIST classifier using convolutional layers.
+
+This file is a modification of the official pytorch mnist example:
+https://github.com/pytorch/examples/blob/master/mnist/main.py
+"""
+
+import argparse
+import logging
+import nni
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+
+
+logger = logging.getLogger('mnist_AutoML')
+
+
+class Net(nn.Module):
+    def __init__(self, hidden_size):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 20, 5, 1)
+        self.conv2 = nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = nn.Linear(4*4*50, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4*4*50)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args['log_interval'] == 0:
+            logger.info('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+
+def test(args, model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            # get the index of the max log-probability
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    accuracy = 100. * correct / len(test_loader.dataset)
+
+    logger.info('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset), accuracy))
+
+    return accuracy
+
+
+def main(args):
+    use_cuda = not args['no_cuda'] and torch.cuda.is_available()
+
+    torch.manual_seed(args['seed'])
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(args['data_dir'], train=True, download=True,
+                       transform=transforms.Compose([
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,))
+                       ])),
+        batch_size=args['batch_size'], shuffle=True, **kwargs)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(args['data_dir'], train=False, transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))
+        ])),
+        batch_size=1000, shuffle=True, **kwargs)
+
+    hidden_size = args['hidden_size']
+
+    model = Net(hidden_size=hidden_size).to(device)
+    optimizer = optim.SGD(model.parameters(), lr=args['lr'],
+                          momentum=args['momentum'])
+
+    for epoch in range(1, args['epochs'] + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test_acc = test(args, model, device, test_loader)
+
+        if epoch < args['epochs']:
+            # report intermediate result
+            nni.report_intermediate_result(test_acc)
+            logger.debug('test accuracy %g', test_acc)
+            logger.debug('Pipe send intermediate result done.')
+        else:
+            # report final result
+            nni.report_final_result(test_acc)
+            logger.debug('Final result is %g', test_acc)
+            logger.debug('Send final result done.')
+
+
+def get_params():
+    # Training settings
+    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument("--data_dir", type=str,
+                        default='/tmp/tensorflow/mnist/input_data', help="data directory")
+    parser.add_argument('--batch_size', type=int, default=64, metavar='N',
+                        help='input batch size for training (default: 64)')
+    parser.add_argument("--hidden_size", type=int, default=512, metavar='N',
+                        help='hidden layer size (default: 512)')
+    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
+                        help='learning rate (default: 0.01)')
+    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
+                        help='SGD momentum (default: 0.5)')
+    parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                        help='number of epochs to train (default: 10)')
+    parser.add_argument('--seed', type=int, default=1, metavar='S',
+                        help='random seed (default: 1)')
+    parser.add_argument('--no_cuda', action='store_true', default=False,
+                        help='disables CUDA training')
+    parser.add_argument('--log_interval', type=int, default=1000, metavar='N',
+                        help='how many batches to wait before logging training status')
+
+
+    args, _ = parser.parse_known_args()
+    return args
+
+
+if __name__ == '__main__':
+    try:
+        # get parameters form tuner
+        tuner_params = nni.get_next_parameter()
+        logger.debug(tuner_params)
+        params = vars(get_params())
+        params.update(tuner_params)
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise
diff --git a/examples/trials/mnist-pytorch/requirements.txt b/examples/trials/mnist-pytorch/requirements.txt
new file mode 100644
index 0000000000..01f6b72556
--- /dev/null
+++ b/examples/trials/mnist-pytorch/requirements.txt
@@ -0,0 +1,2 @@
+torch
+torchvision
diff --git a/examples/trials/mnist-pytorch/search_space.json b/examples/trials/mnist-pytorch/search_space.json
new file mode 100644
index 0000000000..c26cdce369
--- /dev/null
+++ b/examples/trials/mnist-pytorch/search_space.json
@@ -0,0 +1,6 @@
+{
+    "batch_size": {"_type":"choice", "_value": [16, 32, 64, 128]},
+    "hidden_size":{"_type":"choice","_value":[128, 256, 512, 1024]},
+    "lr":{"_type":"choice","_value":[0.0001, 0.001, 0.01, 0.1]},
+    "momentum":{"_type":"uniform","_value":[0, 1]}
+}
diff --git a/src/nni_manager/package.json b/src/nni_manager/package.json
index 4398b59e86..0840697432 100644
--- a/src/nni_manager/package.json
+++ b/src/nni_manager/package.json
@@ -60,7 +60,8 @@
     "lodash": "^4.17.13",
     "lodash.merge": "^4.6.2",
     "node.extend": "^1.1.7",
-    "hoek": "^4.2.1"
+    "hoek": "^4.2.1",
+    "js-yaml": "^3.13.1"
   },
   "engines": {
     "node": ">=10.0.0"
diff --git a/src/nni_manager/rest_server/restValidationSchemas.ts b/src/nni_manager/rest_server/restValidationSchemas.ts
index 19f88f11af..56b1b2c633 100644
--- a/src/nni_manager/rest_server/restValidationSchemas.ts
+++ b/src/nni_manager/rest_server/restValidationSchemas.ts
@@ -53,6 +53,11 @@ export namespace ValidationSchemas {
                 shmMB: joi.number(),
                 authFile: joi.string(),
                 nasMode: joi.string().valid('classic_mode', 'enas_mode', 'oneshot_mode', 'darts_mode'),
+                portList: joi.array().items(joi.object({
+                    label: joi.string().required(),
+                    beginAt: joi.number().required(),
+                    portNumber: joi.number().required(),
+                })),
                 worker: joi.object({
                     replicas: joi.number().min(1).required(),
                     image: joi.string().min(1),
@@ -120,7 +125,8 @@ export namespace ValidationSchemas {
                 azureStorage: joi.object({
                     accountName: joi.string().regex(/^([0-9]|[a-z]|[A-Z]|-){3,31}$/),
                     azureShare: joi.string().regex(/^([0-9]|[a-z]|[A-Z]|-){3,63}$/)
-                })
+                }),
+                uploadRetryCount: joi.number().min(1)
             }),
             frameworkcontroller_config: joi.object({
                 storage: joi.string().min(1),
@@ -136,7 +142,8 @@ export namespace ValidationSchemas {
                 azureStorage: joi.object({
                     accountName: joi.string().regex(/^([0-9]|[a-z]|[A-Z]|-){3,31}$/),
                     azureShare: joi.string().regex(/^([0-9]|[a-z]|[A-Z]|-){3,63}$/)
-                })
+                }),
+                uploadRetryCount: joi.number().min(1)
             }),
             nni_manager_ip: joi.object({
                 nniManagerIp: joi.string().min(1)
diff --git a/src/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts b/src/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts
index f687136fbe..00eeb3e557 100644
--- a/src/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts
+++ b/src/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts
@@ -35,15 +35,15 @@ export namespace AzureStorageClientUtility {
      * @param fileServerClient
      * @param azureShare
      */
-    export async function createShare(fileServerClient: any, azureShare: any): Promise<void> {
-        const deferred: Deferred<void> = new Deferred<void>();
+    export async function createShare(fileServerClient: any, azureShare: any): Promise<boolean> {
+        const deferred: Deferred<boolean> = new Deferred<boolean>();
         fileServerClient.createShareIfNotExists(azureShare, (error: any, result: any, response: any) => {
             if (error) {
                 getLogger()
                   .error(`Create share failed:, ${error}`);
-                deferred.reject(error);
+                deferred.resolve(false);
             } else {
-                deferred.resolve();
+                deferred.resolve(true);
             }
         });
 
@@ -56,18 +56,17 @@ export namespace AzureStorageClientUtility {
      * @param azureFoler
      * @param azureShare
      */
-    export async function createDirectory(fileServerClient: azureStorage.FileService, azureFoler: any, azureShare: any): Promise<void> {
-        const deferred: Deferred<void> = new Deferred<void>();
+    export async function createDirectory(fileServerClient: azureStorage.FileService, azureFoler: any, azureShare: any): Promise<boolean> {
+        const deferred: Deferred<boolean> = new Deferred<boolean>();
         fileServerClient.createDirectoryIfNotExists(azureShare, azureFoler, (error: any, result: any, response: any) => {
             if (error) {
                 getLogger()
                   .error(`Create directory failed:, ${error}`);
-                deferred.reject(error);
+                deferred.resolve(false);
             } else {
-                deferred.resolve();
+                deferred.resolve(true);
             }
         });
-
         return deferred.promise;
     }
 
@@ -77,16 +76,20 @@ export namespace AzureStorageClientUtility {
      * @param azureDirectory
      */
     export async function createDirectoryRecursive(fileServerClient: azureStorage.FileService, azureDirectory: string,
-                                                   azureShare: any): Promise<void> {
-        const deferred: Deferred<void> = new Deferred<void>();
+                                                   azureShare: any): Promise<boolean> {
+        const deferred: Deferred<boolean> = new Deferred<boolean>();
         const directories: string[] = azureDirectory.split('/');
         let rootDirectory: string = '';
         for (const directory of directories) {
             rootDirectory += directory;
-            await createDirectory(fileServerClient, rootDirectory, azureShare);
+            let result:boolean = await createDirectory(fileServerClient, rootDirectory, azureShare);
+            if (!result) {
+                deferred.resolve(false);
+                return deferred.promise;
+            }
             rootDirectory += '/';
         }
-        deferred.resolve();
+        deferred.resolve(true);
 
         return deferred.promise;
     }
@@ -100,16 +103,16 @@ export namespace AzureStorageClientUtility {
      * @param localFilePath
      */
     async function uploadFileToAzure(fileServerClient: any, azureDirectory: string, azureFileName: any, azureShare: any,
-                                     localFilePath: string): Promise<void> {
-        const deferred: Deferred<void> = new Deferred<void>();
+                                     localFilePath: string): Promise<boolean> {
+        const deferred: Deferred<boolean> = new Deferred<boolean>();
         await fileServerClient.createFileFromLocalFile(azureShare, azureDirectory, azureFileName, localFilePath,
                                                        (error: any, result: any, response: any) => {
             if (error) {
                 getLogger()
                   .error(`Upload file failed:, ${error}`);
-                deferred.reject(error);
+                deferred.resolve(false);
             } else {
-                deferred.resolve();
+                deferred.resolve(true);
             }
         });
 
@@ -125,17 +128,17 @@ export namespace AzureStorageClientUtility {
      * @param localFilePath
      */
     async function downloadFile(fileServerClient: any, azureDirectory: string, azureFileName: any, azureShare: any,
-                                localFilePath: string): Promise<void> {
-        const deferred: Deferred<void> = new Deferred<void>();
+                                localFilePath: string): Promise<boolean> {
+        const deferred: Deferred<boolean> = new Deferred<boolean>();
         // tslint:disable-next-line:non-literal-fs-path
         await fileServerClient.getFileToStream(azureShare, azureDirectory, azureFileName, fs.createWriteStream(localFilePath),
                                                (error: any, result: any, response: any) => {
             if (error) {
                 getLogger()
                   .error(`Download file failed:, ${error}`);
-                deferred.reject(error);
+                deferred.resolve(false);
             } else {
-                deferred.resolve();
+                deferred.resolve(true);
             }
         });
 
@@ -151,28 +154,38 @@ export namespace AzureStorageClientUtility {
      */
     // tslint:disable:non-literal-fs-path
     export async function uploadDirectory(fileServerClient: azureStorage.FileService, azureDirectory: string, azureShare: any,
-                                          localDirectory: string): Promise<void> {
-        const deferred: Deferred<void> = new Deferred<void>();
+                                          localDirectory: string): Promise<boolean> {
+        const deferred: Deferred<boolean> = new Deferred<boolean>();
         const fileNameArray: string[] = fs.readdirSync(localDirectory);
-        await createDirectoryRecursive(fileServerClient, azureDirectory, azureShare);
+        let result: boolean = await createDirectoryRecursive(fileServerClient, azureDirectory, azureShare);
+        if (!result) {
+            deferred.resolve(false);
+            return deferred.promise;
+        }
         for (const fileName of fileNameArray) {
             const fullFilePath: string = path.join(localDirectory, fileName);
             try {
+                let resultUploadFile: boolean = true;
+                let resultUploadDir: boolean = true;
                 if (fs.lstatSync(fullFilePath)
                       .isFile()) {
-                    await uploadFileToAzure(fileServerClient, azureDirectory, fileName, azureShare, fullFilePath);
+                    resultUploadFile = await uploadFileToAzure(fileServerClient, azureDirectory, fileName, azureShare, fullFilePath);
                 } else {
                     // If filePath is a directory, recuisively copy it to azure
-                    await uploadDirectory(fileServerClient, String.Format('{0}/{1}', azureDirectory, fileName), azureShare, fullFilePath);
+                    resultUploadDir = await uploadDirectory(fileServerClient, String.Format('{0}/{1}', azureDirectory, fileName), azureShare, fullFilePath);
+                }
+                if (!(resultUploadFile && resultUploadDir)) {
+                    deferred.resolve(false);
+                    return deferred.promise;
                 }
             } catch (error) {
-                deferred.reject(error);
+                deferred.resolve(false);
 
                 return deferred.promise;
             }
         }
         // All files/directories are copied successfully, resolve
-        deferred.resolve();
+        deferred.resolve(true);
 
         return deferred.promise;
     }
diff --git a/src/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts b/src/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts
index d38c656aaa..51d56d5b7c 100644
--- a/src/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts
+++ b/src/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts
@@ -25,7 +25,7 @@ import * as path from 'path';
 import * as component from '../../../common/component';
 import { getExperimentId } from '../../../common/experimentStartupInfo';
 import {
-    JobApplicationForm, NNIManagerIpConfig, TrialJobApplicationForm, TrialJobDetail
+    JobApplicationForm, NNIManagerIpConfig, TrialJobApplicationForm, TrialJobDetail, TrialJobStatus
 } from '../../../common/trainingService';
 import { delay, generateParamFileName, getExperimentRootDir, uniqueString } from '../../../common/utils';
 import { CONTAINER_INSTALL_NNI_SHELL_FORMAT } from '../../common/containerJobData';
@@ -102,10 +102,13 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
 
         //upload code files
         const trialJobOutputUrl: string = await this.uploadCodeFiles(trialJobId, trialLocalTempFolder);
-
+        let initStatus: TrialJobStatus = 'WAITING';
+        if (!trialJobOutputUrl) {
+            initStatus = 'FAILED';
+        }
         const trialJobDetail: KubernetesTrialJobDetail = new KubernetesTrialJobDetail(
             trialJobId,
-            'WAITING',
+            initStatus,
             Date.now(),
             trialWorkingFolder,
             form,
@@ -208,24 +211,10 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
         let trialJobOutputUrl: string = '';
 
         if (this.fcClusterConfig.storageType === 'azureStorage') {
-            if (this.azureStorageClient === undefined) {
-                throw new Error('azureStorageClient is not initialized');
-            }
-            try {
-                //upload local files, including scripts for running the trial and configuration (e.g., hyperparameters) for the trial, to azure storage
-                await AzureStorageClientUtility.uploadDirectory(
-                    this.azureStorageClient, `nni/${getExperimentId()}/${trialJobId}`, this.azureStorageShare, `${trialLocalTempFolder}`);
-                //upload code files to azure storage
-                await AzureStorageClientUtility.uploadDirectory(
-                    this.azureStorageClient, `nni/${getExperimentId()}/${trialJobId}`, this.azureStorageShare, `${this.fcTrialConfig.codeDir}`);
-
-                trialJobOutputUrl = `https://${this.azureStorageAccountName}.file.core.windows.net/` + 
-                                    `${this.azureStorageShare}/${path.join('nni', getExperimentId(), trialJobId, 'output')}`;
-            } catch (error) {
-                this.log.error(error);
-
-                return Promise.reject(error);
-            }
+            const azureFrameworkControllerClusterConfig: FrameworkControllerClusterConfigAzure =
+                      <FrameworkControllerClusterConfigAzure>this.fcClusterConfig;
+            trialJobOutputUrl = await this.uploadFilesToAzureStorage(trialJobId, trialLocalTempFolder, this.fcTrialConfig.codeDir,
+                 azureFrameworkControllerClusterConfig.uploadRetryCount);
         } else if (this.fcClusterConfig.storageType === 'nfs') {
             const nfsFrameworkControllerClusterConfig: FrameworkControllerClusterConfigNFS =
               <FrameworkControllerClusterConfigNFS>this.fcClusterConfig;
diff --git a/src/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts b/src/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts
index b65a548fb6..e70246176a 100644
--- a/src/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts
+++ b/src/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts
@@ -27,7 +27,7 @@ import * as component from '../../../common/component';
 
 import { getExperimentId } from '../../../common/experimentStartupInfo';
 import {
-    JobApplicationForm, NNIManagerIpConfig, TrialJobApplicationForm, TrialJobDetail
+    JobApplicationForm, NNIManagerIpConfig, TrialJobApplicationForm, TrialJobDetail, TrialJobStatus
 } from '../../../common/trainingService';
 import { delay, generateParamFileName, getExperimentRootDir, uniqueString } from '../../../common/utils';
 import { CONTAINER_INSTALL_NNI_SHELL_FORMAT } from '../../common/containerJobData';
@@ -102,9 +102,13 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
         await this.prepareRunScript(trialLocalTempFolder, trialJobId, trialWorkingFolder, curTrialSequenceId, form);
         //upload files to sotrage
         const trialJobOutputUrl: string = await this.uploadCodeFiles(trialJobId, trialLocalTempFolder);
+        let initStatus: TrialJobStatus = 'WAITING';
+        if (!trialJobOutputUrl) {
+            initStatus = 'FAILED';
+        }
         const trialJobDetail: KubernetesTrialJobDetail = new KubernetesTrialJobDetail(
             trialJobId,
-            'WAITING',
+            initStatus,
             Date.now(),
             trialWorkingFolder,
             form,
@@ -215,23 +219,8 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
             if (this.azureStorageClient === undefined) {
                 throw new Error('azureStorageClient is not initialized');
             }
-            try {
-                //upload local files, including scripts for running the trial and configuration (e.g., hyperparameters) for the trial, to azure storage
-                await AzureStorageClientUtility.uploadDirectory(this.azureStorageClient,
-                                                                `nni/${getExperimentId()}/${trialJobId}`, this.azureStorageShare,
-                                                                `${trialLocalTempFolder}`);
-                //upload code files to azure storage
-                await AzureStorageClientUtility.uploadDirectory(this.azureStorageClient,
-                                                                `nni/${getExperimentId()}/${trialJobId}`, this.azureStorageShare,
-                                                                `${this.kubeflowTrialConfig.codeDir}`);
-
-                trialJobOutputUrl = `https://${this.azureStorageAccountName}.file.core.windows.net/${this.azureStorageShare}` + 
-                                    `/${path.join('nni', getExperimentId(), trialJobId, 'output')}`;
-            } catch (error) {
-                this.log.error(error);
-
-                return Promise.reject(error);
-            }
+            const azureKubeflowClusterConfig: KubeflowClusterConfigAzure = <KubeflowClusterConfigAzure>this.kubeflowClusterConfig;
+            trialJobOutputUrl = await this.uploadFilesToAzureStorage(trialJobId, trialLocalTempFolder, this.kubeflowTrialConfig.codeDir, azureKubeflowClusterConfig.uploadRetryCount);
         } else if (this.kubeflowClusterConfig.storage === 'nfs' || this.kubeflowClusterConfig.storage === undefined) {
             const nfsKubeflowClusterConfig: KubeflowClusterConfigNFS = <KubeflowClusterConfigNFS>this.kubeflowClusterConfig;
             // Creat work dir for current trial in NFS directory
diff --git a/src/nni_manager/training_service/kubernetes/kubernetesConfig.ts b/src/nni_manager/training_service/kubernetes/kubernetesConfig.ts
index 334eb122ed..c0d6a4c6dd 100644
--- a/src/nni_manager/training_service/kubernetes/kubernetesConfig.ts
+++ b/src/nni_manager/training_service/kubernetes/kubernetesConfig.ts
@@ -75,16 +75,19 @@ export class KubernetesClusterConfigNFS extends KubernetesClusterConfig {
 export class KubernetesClusterConfigAzure extends KubernetesClusterConfig {
     public readonly keyVault: KeyVaultConfig;
     public readonly azureStorage: AzureStorage;
+    public readonly uploadRetryCount: number | undefined;
 
     constructor(
             apiVersion: string,
             keyVault: KeyVaultConfig,
             azureStorage: AzureStorage,
-            storage?: KubernetesStorageKind
+            storage?: KubernetesStorageKind,
+            uploadRetryCount?: number
         ) {
         super(apiVersion, storage);
         this.keyVault = keyVault;
         this.azureStorage = azureStorage;
+        this.uploadRetryCount = uploadRetryCount;
     }
 
     public get storageType(): KubernetesStorageKind {
@@ -98,7 +101,8 @@ export class KubernetesClusterConfigAzure extends KubernetesClusterConfig {
             kubernetesClusterConfigObjectAzure.apiVersion,
             kubernetesClusterConfigObjectAzure.keyVault,
             kubernetesClusterConfigObjectAzure.azureStorage,
-            kubernetesClusterConfigObjectAzure.storage
+            kubernetesClusterConfigObjectAzure.storage,
+            kubernetesClusterConfigObjectAzure.uploadRetryCount
         );
     }
 }
diff --git a/src/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts b/src/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
index 0327cd553f..6a1df6e0f2 100644
--- a/src/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
+++ b/src/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
@@ -31,13 +31,14 @@ import { getLogger, Logger } from '../../common/log';
 import {
     NNIManagerIpConfig, TrialJobDetail, TrialJobMetric
 } from '../../common/trainingService';
-import { getExperimentRootDir, getIPV4Address, getJobCancelStatus, getVersion, uniqueString } from '../../common/utils';
+import { delay, getExperimentRootDir, getIPV4Address, getJobCancelStatus, getVersion, uniqueString } from '../../common/utils';
 import { AzureStorageClientUtility } from './azureStorageClientUtils';
 import { GeneralK8sClient, KubernetesCRDClient } from './kubernetesApiClient';
 import { KubernetesClusterConfig } from './kubernetesConfig';
 import { kubernetesScriptFormat, KubernetesTrialJobDetail } from './kubernetesData';
 import { KubernetesJobRestServer } from './kubernetesJobRestServer';
 
+var yaml = require('js-yaml');
 var fs = require('fs');
 
 /**
@@ -357,6 +358,52 @@ abstract class KubernetesTrainingService {
         );
         return registrySecretName;
     }
+
+    protected async uploadFilesToAzureStorage(trialJobId: string, trialLocalTempFolder: String, codeDir: String, uploadRetryCount: number | undefined): Promise<string> {
+        if (this.azureStorageClient === undefined) {
+            throw new Error('azureStorageClient is not initialized');
+        }
+        let trialJobOutputUrl: string = '';
+        let retryCount: number = 1;
+        if(uploadRetryCount) {
+            retryCount = uploadRetryCount;
+        }
+        let resultUploadNNIScript: boolean = false;
+        let resultUploadCodeFile: boolean = false;
+        try {
+            do {
+                //upload local files, including scripts for running the trial and configuration (e.g., hyperparameters) for the trial, to azure storage
+                if(!resultUploadNNIScript) {
+                    resultUploadNNIScript = await AzureStorageClientUtility.uploadDirectory(this.azureStorageClient,
+                        `nni/${getExperimentId()}/${trialJobId}`, this.azureStorageShare,
+                        `${trialLocalTempFolder}`);
+                }
+                //upload code files to azure storage
+                if(!resultUploadCodeFile) {
+                    resultUploadCodeFile = await AzureStorageClientUtility.uploadDirectory(this.azureStorageClient,
+                        `nni/${getExperimentId()}/${trialJobId}`, this.azureStorageShare,
+                        `${codeDir}`);
+                }
+                if (resultUploadNNIScript && resultUploadCodeFile) {
+                    trialJobOutputUrl = `https://${this.azureStorageAccountName}.file.core.windows.net/${this.azureStorageShare}` + 
+                    `/${path.join('nni', getExperimentId(), trialJobId, 'output')}`;
+                    break;
+                } else {
+                    //wait for 5 seconds to re-upload files
+                    await delay(5000);
+                    this.log.info('Upload failed, Retry: upload files to azure-storage');
+                }
+            } while (retryCount-- >= 0)
+        } catch (error) {
+            this.log.error(error);
+            //return a empty url when got error
+            return Promise.resolve("");
+        }
+        if(!trialJobOutputUrl) {
+            this.log.info(`Retry-count is used up, upload files to azureStorage for trial ${trialJobId} failed!`);
+        }
+        return Promise.resolve(trialJobOutputUrl);
+    }
      
 }
 export { KubernetesTrainingService };
diff --git a/src/nni_manager/training_service/pai/paiConfig.ts b/src/nni_manager/training_service/pai/paiConfig.ts
index 1248368a89..43f95f7f9c 100644
--- a/src/nni_manager/training_service/pai/paiConfig.ts
+++ b/src/nni_manager/training_service/pai/paiConfig.ts
@@ -39,6 +39,8 @@ export class PAITaskRole {
     public readonly command: string;
     //Shared memory for one task in the task role
     public readonly shmMB?: number;
+    //portList to specify the port used in container
+    public portList?: portListMetaData[];
 
     /**
      * Constructor
@@ -50,7 +52,7 @@ export class PAITaskRole {
      * @param command Executable command for tasks in the task role, can not be empty
      */
     constructor(name : string, taskNumber : number, cpuNumber : number, memoryMB : number, gpuNumber : number,
-                command : string, shmMB?: number) {
+                command : string, shmMB?: number, portList?: portListMetaData[]) {
         this.name = name;
         this.taskNumber = taskNumber;
         this.cpuNumber = cpuNumber;
@@ -58,6 +60,7 @@ export class PAITaskRole {
         this.gpuNumber = gpuNumber;
         this.command = command;
         this.shmMB = shmMB;
+        this.portList = portList;
     }
 }
 
@@ -120,6 +123,16 @@ export class PAIClusterConfig {
     }
 }
 
+/**
+ * portList data structure used in PAI taskRole
+ */
+export class portListMetaData {
+    public readonly label : string = '';
+    public readonly beginAt: number = 0;
+    public readonly portNumber: number = 0;
+}
+  
+
 /**
  * PAI trial configuration
  */
@@ -134,9 +147,11 @@ export class NNIPAITrialConfig extends TrialConfig {
     public shmMB?: number;
     //authentication file used for private Docker registry 
     public authFile?: string;
+    //portList to specify the port used in container
+    public portList?: portListMetaData[];
 
     constructor(command : string, codeDir : string, gpuNum : number, cpuNum: number, memoryMB: number,
-                image: string, virtualCluster?: string, shmMB?: number, authFile?: string) {
+                image: string, virtualCluster?: string, shmMB?: number, authFile?: string, portList?: portListMetaData[]) {
         super(command, codeDir, gpuNum);
         this.cpuNum = cpuNum;
         this.memoryMB = memoryMB;
@@ -144,5 +159,6 @@ export class NNIPAITrialConfig extends TrialConfig {
         this.virtualCluster = virtualCluster;
         this.shmMB = shmMB;
         this.authFile = authFile;
+        this.portList = portList;
     }
 }
diff --git a/src/nni_manager/training_service/pai/paiTrainingService.ts b/src/nni_manager/training_service/pai/paiTrainingService.ts
index 91865d906f..ff742f0fc0 100644
--- a/src/nni_manager/training_service/pai/paiTrainingService.ts
+++ b/src/nni_manager/training_service/pai/paiTrainingService.ts
@@ -79,6 +79,7 @@ class PAITrainingService implements TrainingService {
     private logCollection: string;
     private isMultiPhase: boolean = false;
     private authFileHdfsPath: string | undefined = undefined;
+    private portList?: string | undefined;
 
     constructor() {
         this.log = getLogger();
@@ -446,6 +447,8 @@ class PAITrainingService implements TrainingService {
                 nniPaiTrialCommand,
                 // Task shared memory
                 this.paiTrialConfig.shmMB,
+                // Task portList
+                this.paiTrialConfig.portList
             )
         ];
 
diff --git a/src/nni_manager/yarn.lock b/src/nni_manager/yarn.lock
index 681636cbcf..c0c469d8f8 100644
--- a/src/nni_manager/yarn.lock
+++ b/src/nni_manager/yarn.lock
@@ -1410,14 +1410,7 @@ js-tokens@^4.0.0:
   version "4.0.0"
   resolved "https://registry.yarnpkg.com/js-tokens/-/js-tokens-4.0.0.tgz#19203fb59991df98e3a287050d4647cdeaf32499"
 
-js-yaml@^3.10.0:
-  version "3.12.0"
-  resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-3.12.0.tgz#eaed656ec8344f10f527c6bfa1b6e2244de167d1"
-  dependencies:
-    argparse "^1.0.7"
-    esprima "^4.0.0"
-
-js-yaml@^3.13.1:
+js-yaml@^3.10.0, js-yaml@^3.13.1:
   version "3.13.1"
   resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-3.13.1.tgz#aff151b30bfdfa8e49e05da22e7415e9dfa37847"
   dependencies:
diff --git a/src/sdk/pynni/nni/msg_dispatcher.py b/src/sdk/pynni/nni/msg_dispatcher.py
index 0beda3f154..3abf8066c6 100644
--- a/src/sdk/pynni/nni/msg_dispatcher.py
+++ b/src/sdk/pynni/nni/msg_dispatcher.py
@@ -171,15 +171,15 @@ def _handle_final_metric_data(self, data):
         id_ = data['parameter_id']
         value = data['value']
         if id_ in _customized_parameter_ids:
-            if multi_phase_enabled():
-                self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value, trial_job_id=data['trial_job_id'])
-            else:
-                self.tuner.receive_customized_trial_result(id_, _trial_params[id_], value)
+            if not hasattr(self.tuner, '_accept_customized'):
+                self.tuner._accept_customized = False
+            if not self.tuner._accept_customized:
+                _logger.info('Customized trial job %s ignored by tuner', id_)
+                return
+            customized = True
         else:
-            if multi_phase_enabled():
-                self.tuner.receive_trial_result(id_, _trial_params[id_], value, trial_job_id=data['trial_job_id'])
-            else:
-                self.tuner.receive_trial_result(id_, _trial_params[id_], value)
+            customized = False
+        self.tuner.receive_trial_result(id_, _trial_params[id_], value, customized=customized, trial_job_id=data.get('trial_job_id'))
 
     def _handle_intermediate_metric_data(self, data):
         """Call assessor to process intermediate results
diff --git a/src/sdk/pynni/nni/nas_utils.py b/src/sdk/pynni/nni/nas_utils.py
index e342abbc38..efc25194a4 100644
--- a/src/sdk/pynni/nni/nas_utils.py
+++ b/src/sdk/pynni/nni/nas_utils.py
@@ -32,7 +32,7 @@ def classic_mode(
     '''Execute the chosen function and inputs directly.
     In this mode, the trial code is only running the chosen subgraph (i.e., the chosen ops and inputs),
     without touching the full model graph.'''
-    if trial._params is None:
+    if trial.get_current_parameter() is None:
         trial.get_next_parameter()
     mutable_block = trial.get_current_parameter(mutable_id)
     chosen_layer = mutable_block[mutable_layer_id]["chosen_layer"]
@@ -118,7 +118,7 @@ def oneshot_mode(
     The difference is that oneshot mode does not receive subgraph.
     Instead, it uses dropout to randomly dropout inputs and ops.'''
     # NNI requires to get_next_parameter before report a result. But the parameter will not be used in this mode
-    if trial._params is None:
+    if trial.get_current_parameter() is None:
         trial.get_next_parameter()
     optional_inputs = list(optional_inputs.values())
     inputs_num = len(optional_inputs)
diff --git a/src/sdk/pynni/nni/smartparam.py b/src/sdk/pynni/nni/smartparam.py
index 4c90a8703f..1b981aacd3 100644
--- a/src/sdk/pynni/nni/smartparam.py
+++ b/src/sdk/pynni/nni/smartparam.py
@@ -189,6 +189,6 @@ def mutable_layer(
         raise RuntimeError('Unrecognized mode: %s' % mode)
 
     def _get_param(key):
-        if trial._params is None:
+        if trial.get_current_parameter() is None:
             trial.get_next_parameter()
         return trial.get_current_parameter(key)
diff --git a/src/sdk/pynni/nni/trial.py b/src/sdk/pynni/nni/trial.py
index 9a5df0b7d8..132fd96834 100644
--- a/src/sdk/pynni/nni/trial.py
+++ b/src/sdk/pynni/nni/trial.py
@@ -50,10 +50,12 @@ def get_next_parameter():
         return None
     return _params['parameters']
 
-def get_current_parameter(tag):
+def get_current_parameter(tag=None):
     global _params
     if _params is None:
         return None
+    if tag is None:
+        return _params['parameters']
     return _params['parameters'][tag]
 
 def get_experiment_id():
diff --git a/src/sdk/pynni/nni/tuner.py b/src/sdk/pynni/nni/tuner.py
index 0d995f94c5..e8c345b3f3 100644
--- a/src/sdk/pynni/nni/tuner.py
+++ b/src/sdk/pynni/nni/tuner.py
@@ -57,19 +57,22 @@ def generate_multiple_parameters(self, parameter_id_list, **kwargs):
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
         """Invoked when a trial reports its final result. Must override.
+        By default this only reports results of algorithm-generated hyper-parameters.
+        Use `accept_customized_trials()` to receive results from user-added parameters.
         parameter_id: int
         parameters: object created by 'generate_parameters()'
-        reward: object reported by trial
+        value: object reported by trial
+        customized: bool, true if the trial is created from web UI, false if generated by algorithm
+        trial_job_id: str, only available in multiphase mode.
         """
         raise NotImplementedError('Tuner: receive_trial_result not implemented')
 
-    def receive_customized_trial_result(self, parameter_id, parameters, value, **kwargs):
-        """Invoked when a trial added by WebUI reports its final result. Do nothing by default.
-        parameter_id: int
-        parameters: object created by user
-        value: object reported by trial
+    def accept_customized_trials(self, accept=True):
+        """Enable or disable receiving results of user-added hyper-parameters. 
+        By default `receive_trial_result()` will only receive results of algorithm-generated hyper-parameters.
+        If tuners want to receive those of customized parameters as well, they can call this function in `__init__()`.
         """
-        _logger.info('Customized trial job %s ignored by tuner', parameter_id)
+        self._accept_customized = accept
 
     def trial_end(self, parameter_id, success, **kwargs):
         """Invoked when a trial is completed or terminated. Do nothing by default.
diff --git a/src/sdk/pynni/tests/test_tuner.py b/src/sdk/pynni/tests/test_tuner.py
index 41e80cfa6d..c1fd3594ee 100644
--- a/src/sdk/pynni/tests/test_tuner.py
+++ b/src/sdk/pynni/tests/test_tuner.py
@@ -34,6 +34,7 @@ def __init__(self):
         self.param = 0
         self.trial_results = [ ]
         self.search_space = None
+        self.accept_customized_trials()
 
     def generate_parameters(self, parameter_id, **kwargs):
         # report Tuner's internal states to generated parameters,
@@ -45,13 +46,9 @@ def generate_parameters(self, parameter_id, **kwargs):
             'search_space': self.search_space
         }
 
-    def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+    def receive_trial_result(self, parameter_id, parameters, value, customized, **kwargs):
         reward = extract_scalar_reward(value)
-        self.trial_results.append((parameter_id, parameters['param'], reward, False))
-
-    def receive_customized_trial_result(self, parameter_id, parameters, value):
-        reward = extract_scalar_reward(value)
-        self.trial_results.append((parameter_id, parameters['param'], reward, True))
+        self.trial_results.append((parameter_id, parameters['param'], reward, customized))
 
     def update_search_space(self, search_space):
         self.search_space = search_space
diff --git a/src/webui/src/components/Modal/Compare.tsx b/src/webui/src/components/Modal/Compare.tsx
index ec0c3a9c01..40599c3e6b 100644
--- a/src/webui/src/components/Modal/Compare.tsx
+++ b/src/webui/src/components/Modal/Compare.tsx
@@ -107,6 +107,7 @@ class Compare extends React.Component<CompareProps, {}> {
     initColumn = () => {
         const { compareRows } = this.props;
         const idList: Array<string> = [];
+        const sequenceIdList: Array<number> = [];
         const durationList: Array<number> = [];
 
         const parameterList: Array<object> = [];
@@ -117,6 +118,7 @@ class Compare extends React.Component<CompareProps, {}> {
         Object.keys(compareRows).map(item => {
             const temp = compareRows[item];
             idList.push(temp.id);
+            sequenceIdList.push(temp.sequenceId);
             durationList.push(temp.duration);
             parameterList.push(temp.description.parameters);
         });
@@ -124,13 +126,21 @@ class Compare extends React.Component<CompareProps, {}> {
             <table className="compare">
                 <tbody>
                     <tr>
-                        <td />
+                        <td className="column">Id</td>
                         {Object.keys(idList).map(key => {
                             return (
                                 <td className="value idList" key={key}>{idList[key]}</td>
                             );
                         })}
                     </tr>
+                    <tr>
+                        <td className="column">Trial No.</td>
+                        {Object.keys(sequenceIdList).map(key => {
+                            return (
+                                <td className="value idList" key={key}>{sequenceIdList[key]}</td>
+                            );
+                        })}
+                    </tr>
                     <tr>
                         <td className="column">Default metric</td>
                         {Object.keys(compareRows).map(index => {
diff --git a/src/webui/src/components/SlideBar.tsx b/src/webui/src/components/SlideBar.tsx
index 5e2e0edb5f..36d80ba00d 100644
--- a/src/webui/src/components/SlideBar.tsx
+++ b/src/webui/src/components/SlideBar.tsx
@@ -223,6 +223,16 @@ class SlideBar extends React.Component<SliderProps, SliderState> {
                     <span>{DETAILTABS}</span>
                 </Col>
                 <Col span={16} className="desktop-right">
+                    <span>
+                        <Button
+                            className="fresh"
+                            type="ghost"
+                        >
+                            <a target="_blank" href="https://nni.readthedocs.io/en/latest/Tutorial/WebUI.html">
+                                <Icon type="question" /><span>Help</span>
+                            </a>
+                        </Button>
+                    </span>
                     <span>{this.select()}</span>
                     <span>
                         <Dropdown
@@ -237,10 +247,10 @@ class SlideBar extends React.Component<SliderProps, SliderState> {
                                 <span>View</span>
                                 {
                                     menuVisible
-                                    ?
-                                    <Icon type="up" className="margin-icon"/>
-                                    :
-                                    <Icon type="down" className="margin-icon"/>
+                                        ?
+                                        <Icon type="up" className="margin-icon" />
+                                        :
+                                        <Icon type="down" className="margin-icon" />
                                 }
                             </a>
                         </Dropdown>
diff --git a/src/webui/src/components/TrialsDetail.tsx b/src/webui/src/components/TrialsDetail.tsx
index c5ee9024ab..93bb03cec7 100644
--- a/src/webui/src/components/TrialsDetail.tsx
+++ b/src/webui/src/components/TrialsDetail.tsx
@@ -171,7 +171,9 @@ class TrialsDetail extends React.Component<TrialsDetailProps, TrialDetailState>
                             status: status,
                             duration: duration,
                             acc: acc,
-                            description: desc
+                            description: desc,
+                            startTime: begin,
+                            endTime: (end !== undefined) ? end : undefined
                         });
                     });
                     // update search data result
diff --git a/src/webui/src/components/trial-detail/DefaultMetricPoint.tsx b/src/webui/src/components/trial-detail/DefaultMetricPoint.tsx
index 24131da030..966df304ef 100644
--- a/src/webui/src/components/trial-detail/DefaultMetricPoint.tsx
+++ b/src/webui/src/components/trial-detail/DefaultMetricPoint.tsx
@@ -85,7 +85,9 @@ class DefaultPoint extends React.Component<DefaultPointProps, DefaultPointState>
             });
             // deal with best metric line
             const bestCurve: Array<number | object>[] = []; // best curve data source
-            bestCurve.push([lineListDefault[0][0], lineListDefault[0][1], accSource[0].searchSpace]);
+            if (lineListDefault[0] !== undefined) {
+                bestCurve.push([lineListDefault[0][0], lineListDefault[0][1], accSource[0].searchSpace]);
+            }
             if (optimize === 'maximize') {
                 for (let i = 1; i < lineListDefault.length; i++) {
                     const val = lineListDefault[i][1];
diff --git a/src/webui/src/components/trial-detail/Para.tsx b/src/webui/src/components/trial-detail/Para.tsx
index 522b691821..94d6d70883 100644
--- a/src/webui/src/components/trial-detail/Para.tsx
+++ b/src/webui/src/components/trial-detail/Para.tsx
@@ -128,13 +128,22 @@ class Para extends React.Component<ParaProps, ParaState> {
 
     hyperParaPic = (source: Array<TableObj>, searchSpace: string) => {
         // filter succeed trials [{}, {}, {}]
-        const dataSource: Array<TableObj> = source.filter(filterByStatus);
+        const origin = source.filter(filterByStatus);
+        const dataSource: Array<TableObj> = JSON.parse(JSON.stringify(origin));
         const lenOfDataSource: number = dataSource.length;
         const accPara: Array<number> = [];
         // specific value array
         const eachTrialParams: Array<string> = [];
         // experiment interface search space obj
         const searchRange = searchSpace !== undefined ? JSON.parse(searchSpace) : '';
+        // nest search space
+        let isNested: boolean = false;
+        Object.keys(searchRange).map(item => {
+            if (typeof searchRange[item]._value[0] === 'object') {
+                isNested = true;
+                return;
+            }
+        });
         const dimName = Object.keys(searchRange);
         if (this._isMounted === true) {
             this.setState(() => ({ dimName: dimName }));
@@ -143,79 +152,131 @@ class Para extends React.Component<ParaProps, ParaState> {
         const parallelAxis: Array<Dimobj> = [];
         // search space range and specific value [only number]
         let i = 0;
-        for (i; i < dimName.length; i++) {
-            const searchKey = searchRange[dimName[i]];
-            switch (searchKey._type) {
-                case 'uniform':
-                case 'quniform':
-                    parallelAxis.push({
-                        dim: i,
-                        name: dimName[i],
-                        max: searchKey._value[1],
-                        min: searchKey._value[0]
-                    });
-                    break;
-
-                case 'randint':
-                    parallelAxis.push({
-                        dim: i,
-                        name: dimName[i],
-                        min: searchKey._value[0],
-                        max: searchKey._value[1],
-                    });
-                    break;
-
-                case 'choice':
-                    const data: Array<string> = [];
-                    for (let j = 0; j < searchKey._value.length; j++) {
-                        data.push(searchKey._value[j].toString());
-                    }
-                    parallelAxis.push({
-                        dim: i,
-                        name: dimName[i],
-                        type: 'category',
-                        data: data,
-                        boundaryGap: true,
-                        axisLine: {
-                            lineStyle: {
-                                type: 'dotted', // axis type,solid，dashed，dotted
-                                width: 1
-                            }
-                        },
-                        axisTick: {
-                            show: true,
-                            interval: 0,
-                            alignWithLabel: true,
-                        },
-                        axisLabel: {
-                            show: true,
-                            interval: 0,
-                            // rotate: 30
-                        },
-                    });
-                    break;
-                // support log distribute
-                case 'loguniform':
-                    if (lenOfDataSource > 1) {
+        if (isNested === false) {
+            for (i; i < dimName.length; i++) {
+                const searchKey = searchRange[dimName[i]];
+                switch (searchKey._type) {
+                    case 'uniform':
+                    case 'quniform':
                         parallelAxis.push({
                             dim: i,
                             name: dimName[i],
-                            type: 'log',
+                            max: searchKey._value[1],
+                            min: searchKey._value[0]
                         });
-                    } else {
+                        break;
+                    case 'randint':
+                        parallelAxis.push({
+                            dim: i,
+                            name: dimName[i],
+                            min: searchKey._value[0],
+                            max: searchKey._value[1],
+                        });
+                        break;
+                    case 'choice':
+                        const data: Array<string> = [];
+                        for (let j = 0; j < searchKey._value.length; j++) {
+                            data.push(searchKey._value[j].toString());
+                        }
+                        parallelAxis.push({
+                            dim: i,
+                            name: dimName[i],
+                            type: 'category',
+                            data: data,
+                            boundaryGap: true,
+                            axisLine: {
+                                lineStyle: {
+                                    type: 'dotted', // axis type,solid，dashed，dotted
+                                    width: 1
+                                }
+                            },
+                            axisTick: {
+                                show: true,
+                                interval: 0,
+                                alignWithLabel: true,
+                            },
+                            axisLabel: {
+                                show: true,
+                                interval: 0,
+                                // rotate: 30
+                            },
+                        });
+                        break;
+                    // support log distribute
+                    case 'loguniform':
+                        if (lenOfDataSource > 1) {
+                            parallelAxis.push({
+                                dim: i,
+                                name: dimName[i],
+                                type: 'log',
+                            });
+                        } else {
+                            parallelAxis.push({
+                                dim: i,
+                                name: dimName[i]
+                            });
+                        }
+                        break;
+                    default:
                         parallelAxis.push({
                             dim: i,
                             name: dimName[i]
                         });
-                    }
-                    break;
-
-                default:
-                    parallelAxis.push({
-                        dim: i,
-                        name: dimName[i]
-                    });
-
+                }
+            }
+        } else {
+            for (i; i < dimName.length; i++) {
+                const searchKey = searchRange[dimName[i]];
+                switch (searchKey._type) {
+                    case 'choice':
+                        const data: Array<string> = [];
+                        let j = 0;
+                        for (j; j < searchKey._value.length; j++) {
+                            const item = searchKey._value[j];
+                            Object.keys(item).map(key => {
+                                if (key !== '_name' && key !== '_type') {
+                                    Object.keys(item[key]).map(index => {
+                                        if (index !== '_type') {
+                                            const realChoice = item[key][index];
+                                            Object.keys(realChoice).map(m => {
+                                                data.push(`${item._name}_${realChoice[m]}`);
+                                            });
+                                        }
+                                    });
+                                }
+                            });
+                        }
+                        data.push('null');
+                        parallelAxis.push({
+                            dim: i,
+                            name: dimName[i],
+                            type: 'category',
+                            data: data,
+                            boundaryGap: true,
+                            axisLine: {
+                                lineStyle: {
+                                    type: 'dotted', // axis type,solid dashed dotted
+                                    width: 1
+                                }
+                            },
+                            axisTick: {
+                                show: true,
+                                interval: 0,
+                                alignWithLabel: true,
+                            },
+                            axisLabel: {
+                                show: true,
+                                interval: 0,
+                                // rotate: 30
+                            },
+                        });
+                        break;
+                    default:
+                        parallelAxis.push({
+                            dim: i,
+                            name: dimName[i]
+                        });
+                }
             }
         }
         parallelAxis.push({
@@ -283,6 +344,23 @@ class Para extends React.Component<ParaProps, ParaState> {
                     }
                 }
             });
+            // nested search space, deal data
+            if (isNested !== false) {
+                eachTrialParams.forEach(element => {
+                    Object.keys(element).forEach(key => {
+                        let item = element[key];
+                        if (typeof item === 'object') {
+                            Object.keys(item).forEach(index => {
+                                if (index !== '_name') {
+                                    element[key] = `${item._name}_${item[index]}`;
+                                } else {
+                                    element[key] = 'null';
+                                }
+                            });
+                        }
+                    });
+                });
+            }
             if (this._isMounted) {
                 // if not return final result
                 const maxVal = accPara.length === 0 ? 1 : Math.max(...accPara);
diff --git a/src/webui/src/components/trial-detail/TableList.tsx b/src/webui/src/components/trial-detail/TableList.tsx
index a1af9be602..0c72de7a5e 100644
--- a/src/webui/src/components/trial-detail/TableList.tsx
+++ b/src/webui/src/components/trial-detail/TableList.tsx
@@ -182,7 +182,7 @@ class TableList extends React.Component<TableListProps, TableListState> {
 
     // checkbox for coloumn
     selectedColumn = (checkedValues: Array<string>) => {
-        // 7: because have seven common column, "Intermediate count" is not shown by default
+        // 7: because have seven common column, "Intermediate count" is hidden by default
         let count = 7;
         const want: Array<object> = [];
         const finalKeys: Array<string> = [];
@@ -191,6 +191,8 @@ class TableList extends React.Component<TableListProps, TableListState> {
             switch (checkedValues[m]) {
                 case 'Trial No.':
                 case 'ID':
+                case 'StartTime':
+                case 'EndTime':
                 case 'Duration':
                 case 'Status':
                 case 'Operation':
@@ -347,6 +349,50 @@ class TableList extends React.Component<TableListProps, TableListState> {
                         }
                     });
                     break;
+                case 'StartTime':
+                    showColumn.push({
+                        title: 'StartTime',
+                        dataIndex: 'startTime',
+                        key: 'startTime',
+                        width: 160,
+                        render: (text: string, record: TableObj) => {
+                            const start = record.startTime !== undefined ? record.startTime : -1;
+                            return (
+                                <span>
+                                    {
+                                        start !== -1
+                                            ?
+                                            new Date(start).toLocaleString('en-US')
+                                            :
+                                            '--'
+                                    }
+                                </span>
+                            );
+                        },
+                    });
+                    break;
+                case 'EndTime':
+                    showColumn.push({
+                        title: 'EndTime',
+                        dataIndex: 'endTime',
+                        key: 'endTime',
+                        width: 160,
+                        render: (text: string, record: TableObj) => {
+                            const end = record.endTime !== undefined ? record.endTime : -1;
+                            return (
+                                <span>
+                                    {
+                                        end !== -1
+                                            ?
+                                            new Date(end).toLocaleString('en-US')
+                                            :
+                                            '--'
+                                    }
+                                </span>
+                            );
+                        },
+                    });
+                    break;
                 case 'Duration':
                     showColumn.push({
                         title: 'Duration',
diff --git a/src/webui/src/static/const.ts b/src/webui/src/static/const.ts
index f2708ebbee..fcfd40bc88 100644
--- a/src/webui/src/static/const.ts
+++ b/src/webui/src/static/const.ts
@@ -34,21 +34,29 @@ const COLUMN_INDEX = [
         index: 2
     },
     {
-        name: 'Duration',
+        name: 'StartTime',
         index: 3
     },
     {
-        name: 'Status',
+        name: 'EndTime',
         index: 4
     },
     {
-        name: 'Intermediate count',
+        name: 'Duration',
         index: 5
     },
     {
-        name: 'Default',
+        name: 'Status',
         index: 6
     },
+    {
+        name: 'Intermediate count',
+        index: 7
+    },
+    {
+        name: 'Default',
+        index: 8
+    },
     {
         name: 'Operation',
         index: 10000
@@ -57,7 +65,8 @@ const COLUMN_INDEX = [
 // defatult selected column
 const COLUMN = ['Trial No.', 'ID', 'Duration', 'Status', 'Default', 'Operation'];
 // all choice column !dictory final
-const COLUMNPro = ['Trial No.', 'ID', 'Duration', 'Status', 'Intermediate count', 'Default', 'Operation'];
+const COLUMNPro = ['Trial No.', 'ID', 'StartTime', 'EndTime', 'Duration', 'Status',
+'Intermediate count', 'Default', 'Operation'];
 export {
     MANAGER_IP, DOWNLOAD_IP, trialJobStatus, COLUMNPro,
     CONTROLTYPE, MONACO, COLUMN, COLUMN_INDEX, DRAWEROPTION
diff --git a/src/webui/src/static/interface.ts b/src/webui/src/static/interface.ts
index 15349e23ab..ee7062feab 100644
--- a/src/webui/src/static/interface.ts
+++ b/src/webui/src/static/interface.ts
@@ -8,6 +8,8 @@ interface TableObj {
     acc?: FinalType; // draw accuracy graph
     description: Parameters;
     color?: string;
+    startTime?: number;
+    endTime?: number;
 }
 
 interface SearchSpace {
diff --git a/src/webui/src/static/style/slideBar.scss b/src/webui/src/static/style/slideBar.scss
index b5a3ab0d8f..a07f7e75df 100644
--- a/src/webui/src/static/style/slideBar.scss
+++ b/src/webui/src/static/style/slideBar.scss
@@ -9,10 +9,11 @@ $drowHoverBgColor: #e2e2e2;
     border: none;
     color: #fff;
     font-size: 16px;
-    padding: 0;
+    padding: 0 8px;
 }
 .fresh:hover{
     color: #fff;
+    background-color: #006cb5;
 }
 
 .dropdown{
diff --git a/src/webui/src/static/style/table.scss b/src/webui/src/static/style/table.scss
index 6185ed4859..d16bb2a8be 100644
--- a/src/webui/src/static/style/table.scss
+++ b/src/webui/src/static/style/table.scss
@@ -115,6 +115,7 @@
 }
 
 #detail-button{
+    margin: 2px 0;
     .common-style, .common-style:visited, .common-style:focus{
         height: 26px;
         border: none;
@@ -131,7 +132,7 @@
     .common-style:disabled{
         background-color: #f4f4f4;
     }
-    .special, .special:visited, .special:focus{
+    .special, .special:visited, .special:focus, .special button{
         height: 26px;
         border: none;
         border-radius: 0;
@@ -146,7 +147,7 @@
         background-color: #c8c8c8;
         outline: 0;
     }
-    .special:disabled{
+    .special:disabled, .special button:disabled{
         background-color: #f4f4f4;
         color: #d9d9d9;
     }
diff --git a/test/config_test/examples/mnist-pytorch.test.yml b/test/config_test/examples/mnist-pytorch.test.yml
new file mode 100644
index 0000000000..a5f375b3b7
--- /dev/null
+++ b/test/config_test/examples/mnist-pytorch.test.yml
@@ -0,0 +1,23 @@
+authorName: nni
+experimentName: default_test
+maxExecDuration: 15m
+maxTrialNum: 4
+trialConcurrency: 2
+searchSpacePath: ./mnist_pytorch_search_space.json
+
+tuner:
+  builtinTunerName: Random
+assessor:
+  builtinAssessorName: Medianstop
+  classArgs:
+    optimize_mode: maximize
+trial:
+  codeDir: ../../../examples/trials/mnist-pytorch
+  command: python3 mnist.py  --epochs 2
+  gpuNum: 0
+
+useAnnotation: false
+multiPhase: false
+multiThread: false
+
+trainingServicePlatform: local
diff --git a/test/config_test/examples/mnist_pytorch_search_space.json b/test/config_test/examples/mnist_pytorch_search_space.json
new file mode 100644
index 0000000000..c26cdce369
--- /dev/null
+++ b/test/config_test/examples/mnist_pytorch_search_space.json
@@ -0,0 +1,6 @@
+{
+    "batch_size": {"_type":"choice", "_value": [16, 32, 64, 128]},
+    "hidden_size":{"_type":"choice","_value":[128, 256, 512, 1024]},
+    "lr":{"_type":"choice","_value":[0.0001, 0.001, 0.01, 0.1]},
+    "momentum":{"_type":"uniform","_value":[0, 1]}
+}
diff --git a/tools/nni_cmd/config_schema.py b/tools/nni_cmd/config_schema.py
index f09786664b..1568b9f713 100644
--- a/tools/nni_cmd/config_schema.py
+++ b/tools/nni_cmd/config_schema.py
@@ -240,7 +240,12 @@ def setPathCheck(key):
     Optional('outputDir'): And(Regex(r'hdfs://(([0-9]{1,3}.){3}[0-9]{1,3})(:[0-9]{2,5})?(/.*)?'),\
                          error='ERROR: outputDir format error, outputDir format is hdfs://xxx.xxx.xxx.xxx:xxx'),
     Optional('virtualCluster'): setType('virtualCluster', str),
-    Optional('nasMode'): setChoice('nasMode', 'classic_mode', 'enas_mode', 'oneshot_mode', 'darts_mode')
+    Optional('nasMode'): setChoice('nasMode', 'classic_mode', 'enas_mode', 'oneshot_mode', 'darts_mode'),
+    Optional('portList'): [{
+        "label": setType('label', str),
+        "beginAt": setType('beginAt', int),
+        "portNumber": setType('portNumber', int)
+    }]
     }
 }
 
@@ -310,7 +315,8 @@ def setPathCheck(key):
                            error='ERROR: accountName format error, accountName support using (0-9|a-z|A-Z|-)'),
             'azureShare': And(Regex('([0-9]|[a-z]|[A-Z]|-){3,63}'),\
                           error='ERROR: azureShare format error, azureShare support using (0-9|a-z|A-Z|-)')
-        }
+        },
+        Optional('uploadRetryCount'): setNumberRange('uploadRetryCount', int, 1, 99999)
     })
 }
 
@@ -356,7 +362,8 @@ def setPathCheck(key):
                            error='ERROR: accountName format error, accountName support using (0-9|a-z|A-Z|-)'),
             'azureShare': And(Regex('([0-9]|[a-z]|[A-Z]|-){3,63}'),\
                           error='ERROR: azureShare format error, azureShare support using (0-9|a-z|A-Z|-)')
-        }
+        },
+        Optional('uploadRetryCount'): setNumberRange('uploadRetryCount', int, 1, 99999)
     })
 }
 
diff --git a/tools/nni_cmd/launcher_utils.py b/tools/nni_cmd/launcher_utils.py
index 10b4a0ccad..da6a668064 100644
--- a/tools/nni_cmd/launcher_utils.py
+++ b/tools/nni_cmd/launcher_utils.py
@@ -198,10 +198,7 @@ def validate_common_content(experiment_config):
                     Schema({**separate_schema_dict[separate_key]['customized']}).validate(experiment_config[separate_key])
     except SchemaError as error:
         print_error('Your config file is not correct, please check your config file content!')
-        if error.__str__().__contains__('Wrong key'):
-            print_error(' '.join(error.__str__().split()[:3]))
-        else:
-            print_error(error)
+        print_error(error.code)
         exit(1)
 
     #set default value