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Fixed broken link and adjusted the original content of READE.md (#1305) 

* Added NEWS,EXTERNAL REPO and feedback modules and adjusted the original content

* -1 Added NEWS,EXTERNAL REPO and Feedback modules and adjusted the original content

* -2 Added NEWS,EXTERNAL REPO and Feedback modules and adjusted the original content

* -4 remove news and fixed some broken link

* -4 remove news

* -4 remove news and adjust font

* -5 remove news and adjust font
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README.md
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......@@ -21,6 +21,7 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<p align="center">
<a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
</p>
<div>
<table>
<tbody>
<tr align="center" valign="bottom">
......@@ -37,7 +38,7 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<img src="docs/img/bar.png"/>
</td>
</tr>
<tr/>
</tr>
<tr valign="top">
<td>
<ul>
......@@ -51,41 +52,91 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<li>Theano</li>
</ul>
</td>
<td>
<a href="docs/en_US/BuiltinTuner.md">Tuner</a>
<td align="left">
<a href="docs/en_US/Tuner/BuiltinTuner.md">Tuner</a>
<br />
<ul>
<li><a href="docs/en_US/BuiltinTuner.md#TPE">TPE</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#Random">Random Search</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#Anneal">Anneal</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#SMAC">SMAC</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#Batch">Batch</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#GridSearch">Grid Search</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#Hyperband">Hyperband</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
<li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#BOHB">BOHB</a></li>
<li><a href="docs/en_US/BuiltinTuner.md#GPTuner">GP Tuner</a></li>
<b style="margin-left:-20px"><font size=4 color=#800000>General Tuner</font></b>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Random"><font size=2.9>Random Search</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution"><font size=2.9>Naïve Evolution</font></a></li>
<b><font size=4 color=#800000 style="margin-left:-20px">Tuner for HPO</font></b>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#TPE"><font size=2.9>TPE</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Anneal"><font size=2.9>Anneal</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#SMAC"><font size=2.9>SMAC</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Batch"><font size=2.9>Batch</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#GridSearch"><font size=2.9>Grid Search</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Hyperband"><font size=2.9>Hyperband</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#MetisTuner"><font size=2.9>Metis Tuner</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#BOHB"><font size=2.9>BOHB</font></a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#GPTuner"><font size=2.9>GP Tuner</font></a></li>
<b style="margin-left:-20px"><font size=4 color=#800000 style="margin-left:-20px">Tuner for NAS</font></b>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism"><font size=2.9>Network Morphism</font></a></li>
<li><a href="examples/tuners/enas_nni/README.md"><font size=2.9>ENAS</font></a></li>
</ul>
<a href="docs/en_US/BuiltinAssessor.md">Assessor</a>
<a href="docs/en_US/Assessor/BuiltinAssessor.md">Assessor</a>
<ul>
<li><a href="docs/en_US/BuiltinAssessor.md#Medianstop">Median Stop</a></li>
<li><a href="docs/en_US/BuiltinAssessor.md#Curvefitting">Curve Fitting</a></li>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Medianstop"><font size=2.9>Median Stop</font></a></li>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Curvefitting"><font size=2.9>Curve Fitting</font></a></li>
</ul>
</td>
<td>
<ul>
<li><a href="docs/en_US/LocalMode.md">Local Machine</a></li>
<li><a href="docs/en_US/RemoteMachineMode.md">Remote Servers</a></li>
<li><a href="docs/en_US/PaiMode.md">OpenPAI</a></li>
<li><a href="docs/en_US/KubeflowMode.md">Kubeflow</a></li>
<li><a href="docs/en_US/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a></li>
<li><a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a></li>
<li><b>Kubernetes based services</b></li>
<ul><li><a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a></li>
<li><a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a></li>
<li><a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a></li>
</ul>
</ul>
</td>
</tr>
<tr align="center" valign="bottom">
<td style="border-top:#FF0000 solid 0px;">
<b>References</b>
<img src="docs/img/bar.png"/>
</td>
<td style="border-top:#FF0000 solid 0px;">
<b>References</b>
<img src="docs/img/bar.png"/>
</td>
<td style="border-top:#FF0000 solid 0px;">
<b>References</b>
<img src="docs/img/bar.png"/>
</td>
</tr>
<tr valign="top">
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/sdk_reference.rst">Python API</a></li>
<li><a href="docs/en_US/Tutorial/AnnotationSpec.md">NNI Annotation</a></li>
<li><a href="docs/en_US/TrialExample/Trials.md#nni-python-annotation">Annotation tutorial</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/tuners.rst">Try different tuners</a></li>
<li><a href="docs/en_US/assessors.rst">Try different assessors</a></li>
<li><a href="docs/en_US/Tuner/CustomizeTuner.md">Implement a customized tuner</a></li>
<li><a href="docs/en_US/Tuner/CustomizeAdvisor.md">Implement a customized advisor</a></li>
<li><a href="docs/en_US/Assessor/CustomizeAssessor.md">Implement a customized assessor </a></li>
<li><a href="docs/en_US/CommunitySharings/HpoComparision.md">HPO Comparison</a></li>
<li><a href="docs/en_US/CommunitySharings/NasComparision.md">NAS Comparison</a></li>
<li><a href="docs/en_US/CommunitySharings/RecommendersSvd.md">Automatically tuning SVD on NNI</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.md">Implement TrainingService in NNI</a></li>
<li><a href="docs/en_US/TrainingService/LocalMode.md">Run an experiment on local</a></li>
<li><a href="docs/en_US/TrainingService/KubeflowMode.md">Run an experiment on Kubeflow</a></li>
<li><a href="docs/en_US/TrainingService/PaiMode.md">Run an experiment on OpenPAI?</a></li>
<li><a href="docs/en_US/TrainingService/RemoteMachineMode.md">Run an experiment on multiple machines?</a></li>
</ul>
</td>
</tbody>
</table>
</div>
## **Who should consider using NNI**
......@@ -127,7 +178,7 @@ Note:
* `--user` can be added if you want to install NNI in your home directory, which does not require any special privileges.
* Currently NNI on Windows support local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
* If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/FAQ.md)
* If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/Tutorial/FAQ.md)
**Install through source code**
......@@ -153,9 +204,9 @@ Windows
powershell -ExecutionPolicy Bypass -file install.ps1
```
For the system requirements of NNI, please refer to [Install NNI](docs/en_US/Installation.md)
For the system requirements of NNI, please refer to [Install NNI](docs/en_US/Tutorial/Installation.md)
For NNI on Windows, please refer to [NNI on Windows](docs/en_US/NniOnWindows.md)
For NNI on Windows, please refer to [NNI on Windows](docs/en_US/Tutorial/NniOnWindows.md)
**Verify install**
......@@ -211,7 +262,7 @@ You can use these commands to get more information about the experiment
-----------------------------------------------------------------------
```
* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/WebUI.md) are more Web UI pages.
* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/Tutorial/WebUI.md) are more Web UI pages.
<table style="border: none">
<th><img src="./docs/img/webui_overview_page.png" alt="drawing" width="395"/></th>
......@@ -219,44 +270,63 @@ You can use these commands to get more information about the experiment
</table>
## **Documentation**
Our primary documentation is at [here](https://nni.readthedocs.io/en/latest/Overview.html) and is generated from this repository.<br/>
Maybe you want to read:
* [NNI overview](docs/en_US/Overview.md)
* [Quick start](docs/en_US/QuickStart.md)
* [Quick start](docs/en_US/Tutorial/QuickStart.md)
* [Contributing](docs/en_US/Tutorial/Contributing.md)
* [Examples](docs/en_US/examples.rst)
* [References](docs/en_US/reference.rst)
* [WebUI tutorial](docs/en_US/Tutorial/WebUI.md)
## **How to**
* [Install NNI](docs/en_US/Installation.md)
* [Use command line tool nnictl](docs/en_US/Nnictl.md)
* [Use NNIBoard](docs/en_US/WebUI.md)
* [How to define search space](docs/en_US/SearchSpaceSpec.md)
* [How to define a trial](docs/en_US/Trials.md)
* [How to choose tuner/search-algorithm](docs/en_US/BuiltinTuner.md)
* [Config an experiment](docs/en_US/ExperimentConfig.md)
* [How to use annotation](docs/en_US/Trials.md#nni-python-annotation)
* [Install NNI](docs/en_US/Tutorial/Installation.md)
* [Use command line tool nnictl](docs/en_US/Tutorial/Nnictl.md)
* [Use NNIBoard](docs/en_US/Tutorial/WebUI.md)
* [How to define search space](docs/en_US/Tutorial/SearchSpaceSpec.md)
* [How to define a trial](docs/en_US/TrialExample/Trials.md)
* [How to choose tuner/search-algorithm](docs/en_US/Tuner/BuiltinTuner.md)
* [Config an experiment](docs/en_US/Tutorial/ExperimentConfig.md)
* [How to use annotation](docs/en_US/TrialExample/Trials.md#nni-python-annotation)
## **Tutorials**
* [Run an experiment on local (with multiple GPUs)?](docs/en_US/LocalMode.md)
* [Run an experiment on multiple machines?](docs/en_US/RemoteMachineMode.md)
* [Run an experiment on OpenPAI?](docs/en_US/PaiMode.md)
* [Run an experiment on Kubeflow?](docs/en_US/KubeflowMode.md)
* [Run an experiment on local (with multiple GPUs)?](docs/en_US/LocalMode.md)
* [Run an experiment on multiple machines?](docs/en_US/RemoteMachineMode.md)
* [Try different tuners](docs/en_US/tuners.rst)
* [Try different assessors](docs/en_US/assessors.rst)
* [Implement a customized tuner](docs/en_US/CustomizeTuner.md)
* [Implement a customized tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [Implement a customized assessor](docs/en_US/CustomizeAssessor.md)
* [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](examples/trials/ga_squad/README.md)
## **Contribute**
This project welcomes contributions and there are many ways in which you can participate in the project, for example:
* Review [source code changes](https://github.com/microsoft/nni/pulls)
* Review the [documentation](https://github.com/microsoft/nni/tree/master/docs) and make pull requests for anything from typos to new content
* Find the issues tagged with ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22), these are simple and easy to start , we recommend new contributors to start with.
Before providing your hacks, there are a few simple guidelines that you need to follow:
* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
* [Code Styles & Naming Conventions](docs/en_US/Tutorial/Contributing.md)
* How to Set up [NNI developer environment](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
* Review the [Contributing Instruction](docs/en_US/Tutorial/Contributing.md) and get familiar with the NNI Code Contribution Guideline
## **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
## **Feedback**
* Open [bug reports](https://github.com/microsoft/nni/issues/new/choose).<br/>
* Request a [new feature](https://github.com/microsoft/nni/issues/new/choose).
* 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)or [file an issue](https://github.com/microsoft/nni/issues/new/choose)on GitHub.
* We are in construction of the instruction for [How to Debug](docs/en_US/Tutorial/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area.
This project welcomes contributions and suggestions, we use [GitHub issues](https://github.com/Microsoft/nni/issues) for tracking requests and bugs.
Issues with the **good first issue** label are simple and easy-to-start ones that we recommend new contributors to start with.
To set up environment for NNI development, refer to the instruction: [Set up NNI developer environment](docs/en_US/SetupNniDeveloperEnvironment.md)
Before start coding, review and get familiar with the NNI Code Contribution Guideline: [Contributing](docs/en_US/Contributing.md)
We are in construction of the instruction for [How to Debug](docs/en_US/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area.
## **License**
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