**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate**<ahref="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <ahref="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <ahref="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <ahref="docs/en_US/Compressor/Overview.md">Model Compression</a>.
The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <ahref="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <ahref="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <ahref="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <ahref="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <ahref="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a> and other cloud options.
The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <ahref="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <ahref="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <ahref="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <ahref="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <ahref="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a>, <ahref="docs/en_US/TrainingService/DLTSMode.md">DLWorkspace (aka. DLTS)</a> and other cloud options.
## **Who should consider using NNI**
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* Researchers and data scientists who want to easily **implement and experiment new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
* ML Platform owners who want to **support AutoML in their platform**.
### **NNI v1.5 has been released! <a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
### **[NNI v1.5 has been released!](https://github.com/microsoft/nni/releases) <a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
## **NNI capabilities in a glance**
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***Blog (in Chinese)** - [A summary of NNI new capabilities in 2019](https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA) by @squirrelsc
## **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.
*[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).
* 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.
Join IM discussion groups:
|Gitter||WeChat|
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For debugging NNI source code, your development environment should be under Ubuntu 16.04 (or above) system with python 3 and pip 3 installed, then follow the below steps.