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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 [v0.5](https://github.com/Microsoft/nni/releases) has been released!**
<p align="center">
<img src="./docs/img/nni_arch_overview.png" alt="drawing"/>
<a href=#><img src="https://rawgit.com/QuanluZhang/nni/update-doc11/overview.svg" /></a>
</p>
<table>
<tbody>
<tr align="center">
<td>
<b>User Code + SDK( import in )</b>
<img src="https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png"/>
</td>
<td>
<b>Tunning Algorithm Extensions</b>
<img src="https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png"/>
</td>
<td>
<b>Training Service Extensions</b>
<img src="https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png"/>
</td>
</tr>
<tr/>
<tr valign="top">
<td>
<ul>
<li>CNTK</li>
<li>Tensorflow</li>
<li>PyTorch</li>
<li>Keras</li>
<li>...</li>
</ul>
(Python based frameworks)
</td>
<td>
<a href="docs/HowToChooseTuner.md">Tuner</a>
<ul>
<li><a href="docs/HowToChooseTuner.md#TPE">TPE</a></li>
<li><a href="docs/HowToChooseTuner.md#Random">Random Search</a></li>
<li><a href="docs/HowToChooseTuner.md#Anneal">Anneal</a></li>
<li><a href="docs/HowToChooseTuner.md#Evolution">Naive Evolution</a></li>
<li><a href="docs/HowToChooseTuner.md#SMAC">SMAC</a></li>
<li><a href="docs/HowToChooseTuner.md#Batch">Batch</a></li>
<li><a href="docs/HowToChooseTuner.md#Grid">Grid Search</a></li>
<li><a href="docs/HowToChooseTuner.md#Hyperband">Hyperband</a></li>
<li><a href="docs/HowToChooseTuner.md#NetworkMorphism">Network Morphism</a></li>
<li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
<li><a href="docs/HowToChooseTuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
</ul>
<a href="docs/HowToChooseTuner.md#assessor">Assessor</a>
<ul>
<li><a href="docs/HowToChooseTuner.md#Medianstop">Median Stop</a></li>
<li><a href="docs/HowToChooseTuner.md#Curvefitting">Curve Fitting</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="docs/tutorial_1_CR_exp_local_api.md">Local Machine</a></li>
<li><a href="docs/tutorial_2_RemoteMachineMode.md">Remote Servers</a></li>
<li><a href="docs/PAIMode.md">OpenPAI</a></li>
<li><a href="docs/KubeflowMode.md">Kubeflow</a></li>
<li><a href="docs/KubeflowMode.md">FrameworkController on K8S (AKS etc.)</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.
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docs/HowToChooseTuner.md
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optimize_mode: maximize
```
<a name="assessor"></a>
# How to use Assessor that NNI supports?
For now, NNI has supported the following assessor algorithms.
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docs/NNICTLDOC.md
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nnictl
# nnictl
===
## Introduction
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