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43 changes: 26 additions & 17 deletions README.md
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Expand Up @@ -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>
Expand Down Expand Up @@ -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>
Expand Down Expand Up @@ -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>
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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
```
Expand All @@ -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
```
Expand All @@ -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
Expand Down Expand Up @@ -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**
Expand Down
159 changes: 115 additions & 44 deletions README_zh_CN.md
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Expand Up @@ -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>
Expand All @@ -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>
Expand All @@ -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>
Expand All @@ -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>
Expand All @@ -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>

Expand Down Expand Up @@ -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
```
Expand All @@ -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
```
Expand All @@ -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
Expand Down Expand Up @@ -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**

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