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README.md
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NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
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>**
<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">
......@@ -56,27 +58,27 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
<a href="docs/en_US/Tuner/BuiltinTuner.md">Tuner</a>
<br />
<ul>
<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>
<b style="margin-left:-20px">General Tuner</b>
<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>
<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>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Batch">Batch</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#GridSearch">Grid Search</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Hyperband">Hyperband</a></li>
<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>
<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>
<a href="docs/en_US/Assessor/BuiltinAssessor.md">Assessor</a>
<ul>
<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>
<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>
</td>
<td>
......@@ -91,52 +93,10 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
</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**
......@@ -291,17 +251,18 @@ Maybe you want to read:
* [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 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)
* [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/CustomizeAssessor.md)
* [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](examples/trials/ga_squad/README.md)
* [Implement a customized assessor](docs/en_US/Assessor/CustomizeAssessor.md)
* [Use Genetic Algorithm to find good model architectures for Reading Comprehension task](docs/en_US/TrialExample/SquadEvolutionExamples.md)
## **Contribute**
This project welcomes contributions and there are many ways in which you can participate in the project, for example:
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