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README.md
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[简体中文](README_zh_CN.md)
**NNI (Neural Network Intelligence)** is an efficient and automatic toolkit to help users design and search neural network architecture, tune machine learning model's parameters or complex system's parameters. 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 environments like local machine, remote servers and cloud.
**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <a href="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 <a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a> and other cloud options.
## **Who should consider using NNI**
* Those who want to **try different AutoML algorithms** in their training code/model.
* Those who want to run AutoML trial jobs **in different environments** to speed up search.
* Researchers and data scientists who want to easily **implement and experiement 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 v1.2 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
## **NNI capabilities in a glance**
NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in stat-of-the-art AutoML algorithms and out of box support for popular training platforms.
Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.
<p align="center">
<a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
</p>
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</ul>
</ul>
<a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>
<ul>
<ul>
<li><a href="docs/en_US/NAS/Overview.md#enas">ENAS</a></li>
<li><a href="docs/en_US/NAS/Overview.md#darts">DARTS</a></li>
<li><a href="docs/en_US/NAS/Overview.md#p-darts">P-DARTS</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a> </li>
</ul>
</ul>
<a href="docs/en_US/Compressor/Overview.md">Model Compression</a>
<ul>
<b>Pruning</b>
<ul>
<li><a href="docs/en_US/Compressor/Pruner.md#agp-pruner">AGP Pruner</a></li>
<li><a href="docs/en_US/Compressor/Pruner.md#slim-pruner">Slim Pruner</a></li>
<li><a href="docs/en_US/Compressor/Pruner.md#fpgm-pruner">FPGM Pruner</a></li>
</ul>
<b>Quantization</b>
<ul>
<li><a href="docs/en_US/Compressor/Quantizer.md#qat-quantizer">QAT Quantizer</a></li>
<li><a href="docs/en_US/Compressor/Quantizer.md#dorefa-quantizer">DoReFa Quantizer</a></li>
</ul>
</ul>
<a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering (Beta)</a>
<ul>
<li><a href="docs/en_US/FeatureEngineering/GradientFeatureSelector.md">GradientFeatureSelector</a></li>
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</tbody>
</table>
## **Who should consider using NNI**
* Those who want to try different AutoML algorithms in their training code (model) at their local machine.
* Those who want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud).
* Researchers and data scientists who want to implement their own AutoML algorithms and compare it with other algorithms.
* ML Platform owners who want to support AutoML in their platform.
## Related Projects
Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-research-group-asia/) had also released few other open source projects.
* [OpenPAI](https://github.com/Microsoft/pai) : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
* [FrameworkController](https://github.com/Microsoft/frameworkcontroller) : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
* [MMdnn](https://github.com/Microsoft/MMdnn) : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.
We encourage researchers and students leverage these projects to accelerate the AI development and research.
## **Install & Verify**
**Install through pip**
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</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/Tutorial/QuickStart.md)
* [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)
* [Define a trial](docs/en_US/TrialExample/Trials.md)
* [Config an experiment](docs/en_US/Tutorial/ExperimentConfig.md)
* [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**
* To learn about what's NNI, read the [NNI Overview](https://nni.readthedocs.io/en/latest/Overview.html).
* To get yourself familiar with how to use NNI, read the [documentation](https://nni.readthedocs.io/en/latest/index.html).
* To get started and install NNI on your system, please refer to [Install NNI](docs/en_US/Tutorial/Installation.md).
* [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 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)
## **Contributing**
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
## **Contribute**
This project welcomes contributions and there are many ways in which you can participate in the project, for example:
* 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.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the Code of [Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact opencode@microsoft.com with any additional questions or comments.
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:
After getting familiar with contribution agreements, you are ready to create your first PR =), follow the NNI developer tutorials to get start:
* We recommend new contributors to start 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 issues are simple and easy to start.
* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
* [Customize Your Own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
* [Customize Your Own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.md)
* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.md)
* [Implement a new NAS trainer on NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/NasInterface.md#implement-a-new-nas-trainer-on-nni)
* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
## **External Repositories and References**
With authors' permission, we listed a set of NNI usage examples and relevant articles.
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* [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).
## Related Projects
Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-research-group-asia/) had also released few other open source projects.
* [OpenPAI](https://github.com/Microsoft/pai) : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
* [FrameworkController](https://github.com/Microsoft/frameworkcontroller) : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
* [MMdnn](https://github.com/Microsoft/MMdnn) : A comprehensive, cross-framework solution to convert, visualize and diagnose deep neural network models. The "MM" in MMdnn stands for model management and "dnn" is an acronym for deep neural network.
* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.
We encourage researchers and students leverage these projects to accelerate the AI development and research.
## **License**
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