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[issue#1270] Update NNI Introduction Board 

[issue#1270] Update NNI Introduction Board
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README.md
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......@@ -28,7 +28,9 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Supported Frameworks</b>
</td>
<td>
<b>Frameworks & Libraries</b>
<img src="docs/img/bar.png"/>
</td>
<td>
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</tr>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>Built-in</b>
</td>
<td>
<ul><li><b>Supported Frameworks</b></li>
<ul>
<li>PyTorch</li>
<li>TensorFlow</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<li>CNTK (Python language)</li>
<li>Chainer</li>
<li>Theano</li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Supported Libraries</b></li>
<ul>
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
</ul>
</ul>
<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-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>
<li><a href="docs/en_US/TrialExample/SklearnExamples.md">Scikit-learn</a></li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
</ul>
</ul>
</td>
<td align="left">
<td align="left" >
<a href="docs/en_US/Tuner/BuiltinTuner.md">Tuner</a>
<br />
<ul>
<b style="margin-left:-20px">General Tuner</b>
<li><b>General Tuner</b></li>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Random">Random Search</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
<b style="margin-left:-20px">Tuner for HPO</b>
</ul>
<li><b>Tuner for <a href="docs/en_US/CommunitySharings/HpoComparision.md">HPO</a></b></li>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#TPE">TPE</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Anneal">Anneal</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#SMAC">SMAC</a></li>
......@@ -71,15 +99,20 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
<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>
<b style="margin-left:-20px">Tuner for NAS</b>
</ul>
<li><b>Tuner for <a href="docs/en_US/CommunitySharings/NasComparision.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>
</ul>
</ul>
<a href="docs/en_US/Assessor/BuiltinAssessor.md">Assessor</a>
<ul>
<ul>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Medianstop">Median Stop</a></li>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.md#Curvefitting">Curve Fitting</a></li>
</ul>
</ul>
</td>
<td>
<ul>
......@@ -93,11 +126,40 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
</ul>
</td>
</tr>
<tr align="center" valign="bottom">
</td>
</tr>
<tr valign="top">
<td valign="middle">
<b>References</b>
</td>
<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/Tutorial/Installation.md">Supported OS</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/Tuner/CustomizeTuner.md">CustomizeTuner</a></li>
<li><a href="docs/en_US/Assessor/CustomizeAssessor.md">CustomizeAssessor</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/TrainingService/SupportTrainingService.md">Support TrainingService</li>
<li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.md">Implement TrainingService</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
## **Who should consider using NNI**
* Those who want to try different AutoML algorithms in their training code (model) at their local machine.
......@@ -235,60 +297,69 @@ Maybe you want to read:
* [NNI overview](docs/en_US/Overview.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)
* [Contributing](docs/en_US/Tutorial/Contributing.md)
## **How to**
* [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)
* [Define a trial](docs/en_US/TrialExample/Trials.md)
* [Config an experiment](docs/en_US/Tutorial/ExperimentConfig.md)
* [How to use annotation](docs/en_US/TrialExample/Trials.md#nni-python-annotation)
* [Define search space](docs/en_US/Tutorial/SearchSpaceSpec.md)
* [choose tuner/search-algorithm](docs/en_US/Tuner/BuiltinTuner.md)
* [Use annotation](docs/en_US/TrialExample/Trials.md#nni-python-annotation)
* [Use NNIBoard](docs/en_US/Tutorial/WebUI.md)
## **Tutorials**
* [Run an experiment on local (with multiple GPUs)](docs/en_US/TrainingService/LocalMode.md)
* [Run an experiment on OpenPAI](docs/en_US/TrainingService/PaiMode.md)
* [Run an experiment on Kubeflow](docs/en_US/TrainingService/KubeflowMode.md)
* [Run an experiment on local (with multiple GPUs)](docs/en_US/TrainingService/LocalMode.md)
* [Run an experiment on multiple machines](docs/en_US/TrainingService/RemoteMachineMode.md)
* [Try different tuners](docs/en_US/Tuner/BuiltinTuner.md)
* [Try different assessors](docs/en_US/Assessor/BuiltinAssessor.md)
* [Implement a customized tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [Implement a customized assessor](docs/en_US/Assessor/CustomizeAssessor.md)
* [Implement TrainingService in NNI](docs/en_US/TrainingService/HowToImplementTrainingService.md)
* [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](docs/en_US/TrialExample/SquadEvolutionExamples.md)
* [Advanced Neural Architecture Search](docs/en_US/AdvancedFeature/AdvancedNas.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
* Open [bug reports](https://github.com/microsoft/nni/issues/new/choose).
* Request a [new feature](https://github.com/microsoft/nni/issues/new/choose).
* Suggest or ask some questions on the [How to Debug](docs/en_US/Tutorial/HowToDebug.md) guidance document.
* 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:
Before providing your hacks, you can review the [Contributing Instruction](docs/en_US/Tutorial/Contributing.md) to get more information. In addition, we also provide you with the following documents:
* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
* [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
* [Customize Your Own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
* [Customize Your Own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [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
## **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.
* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true)
* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
## **License**
The entire codebase is under [MIT license](LICENSE)
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