<div align="center">
<img src="docs/img/nni_logo.png" width="600"/>
</div>
<br/>
[](LICENSE)
[](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
[](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
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[<img src="docs/img/readme_banner.png" width="100%"/>](https://nni.readthedocs.io/en/stable)
NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our **[official documentation](https://nni.readthedocs.io/) ([简体中文版点这里](https://nni.readthedocs.io/zh/stable))**.
## What's NEW! <a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
* **New release**: [v3.0 preview is available](https://github.com/microsoft/nni/releases/tag/v3.0rc1) - _released on May-5-2022_
* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on June-22-2022_
* **New research paper**: [SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute](https://www.usenix.org/system/files/osdi22-zheng-ningxin.pdf) - _published in OSDI 2022_
* **New research paper**: [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.pdf) - _published in CVPR 2022_
* **Newly upgraded documentation**: [Doc upgraded](https://nni.readthedocs.io/en/stable)
## Installation
See the [NNI installation guide](https://nni.readthedocs.io/en/stable/installation.html) to install from pip, or build from source.
To install the current release:
```
$ pip install nni
```
To update NNI to the latest version, add `--upgrade` flag to the above commands.
## NNI capabilities in a glance
<img src="docs/img/overview.svg" width="100%"/>
<table>
<tbody>
<tr align="center" valign="bottom">
<td></td>
<td>
<b>Hyperparameter Tuning</b>
<img src="docs/img/bar.png" />
</td>
<td>
<b>Neural Architecture Search</b>
<img src="docs/img/bar.png" />
</td>
<td>
<b>Model Compression</b>
<img src="docs/img/bar.png" />
</td>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>Algorithms</b>
</td>
<td>
<ul>
<li><b>Exhaustive search</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner">Grid Search</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.random_tuner.RandomTuner">Random</a></li>
</ul>
<li><b>Heuristic search</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner">Anneal</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.evolution_tuner.EvolutionTuner">Evolution</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.hyperband_advisor.Hyperband">Hyperband</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.pbt_tuner.PBTTuner">PBT</a></li>
</ul>
<li><b>Bayesian optimization</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.bohb_advisor.BOHB">BOHB</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.dngo_tuner.DNGOTuner">DNGO</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.gp_tuner.GPTuner">GP</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.metis_tuner.MetisTuner">Metis</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.smac_tuner.SMACTuner">SMAC</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.tpe_tuner.TpeTuner">TPE</a></li>
</ul>
</ul>
</td>
<td>
<ul>
<li><b>Multi-trial</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#grid-search-strategy">Grid Search</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#policy-based-rl-strategy">Policy Based RL</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#random-strategy">Random</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#regularized-evolution-strategy">Regularized Evolution</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#tpe-strategy">TPE</a></li>
</ul>
<li><b>One-shot</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#darts-strategy">DARTS</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#enas-strategy">ENAS</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#fbnet-strategy">FBNet</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#proxylessnas-strategy">ProxylessNAS</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#spos-strategy">SPOS</a></li>
</ul>
</ul>
</td>
<td>
<ul>
<li><b>Pruning</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#level-pruner">Level</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#l1-norm-pruner">L1 Norm</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#taylor-fo-weight-pruner">Taylor FO Weight</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#movement-pruner">Movement</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#agp-pruner">AGP</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#auto-compress-pruner">Auto Compress</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html">More...</a></li>
</ul>
<li><b>Quantization</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#naive-quantizer">Naive</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#qat-quantizer">QAT</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#lsq-quantizer">LSQ</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#observer-quantizer">Observer</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#dorefa-quantizer">DoReFa</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#bnn-quantizer">BNN</a></li>
</ul>
</ul>
</td>
<tr align="center" valign="bottom">
<td></td>
<td>
<b>Supported Frameworks</b>
<img src="docs/img/bar.png" />
</td>
<td>
<b>Training Services</b>
<img src="docs/img/bar.png" />
</td>
<td>
<b>Tutorials</b>
<img src="docs/img/bar.png" />
</td>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>Supports</b>
</td>
<td>
<ul>
<li>PyTorch</li>
<li>TensorFlow</li>
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<li>MXNet</li>
<li>Caffe2</li>
<li>More...</li>
</ul>
</td>
<td>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/local.html">Local machine</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/remote.html">Remote SSH servers</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/aml.html">Azure Machine Learning (AML)</a></li>
<li><b>Kubernetes Based</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/openpai.html">OpenAPI</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/kubeflow.html">Kubeflow</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/frameworkcontroller.html">FrameworkController</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/adaptdl.html">AdaptDL</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/paidlc.html">PAI DLC</a></li>
</ul>
<li><a href="https://nni.readthedocs.io/en/latest/experiment/hybrid.html">Hybrid training services</a></li>
</ul>
</td>
<td>
<ul>
<li><b>HPO</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/hpo_quickstart_pytorch/main.html">PyTorch</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/hpo_quickstart_tensorflow/main.html">TensorFlow</a></li>
</ul>
<li><b>NAS</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/hello_nas.html">Hello NAS</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/nasbench_as_dataset.html">NAS Benchmarks</a></li>
</ul>
<li><b>Compression</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/pruning_quick_start.html">Pruning</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/pruning_speed_up.html">Pruning Speedup</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/quantization_quick_start.html">Quantization</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/tutorials/quantization_speed_up.html">Quantization Speedup</a></li>
</ul>
</ul>
</td>
</tbody>
</table>
<img src="docs/static/img/webui.gif" alt="webui" width="100%"/>
## Resources
* [NNI Documentation Homepage](https://nni.readthedocs.io/en/stable)
* [NNI Installation Guide](https://nni.readthedocs.io/en/stable/installation.html)
* [NNI Examples](https://nni.readthedocs.io/en/latest/examples.html)
* [Python API Reference](https://nni.readthedocs.io/en/latest/reference/python_api.html)
* [Releases (Change Log)](https://nni.readthedocs.io/en/latest/release.html)
* [Related Research and Publications](https://nni.readthedocs.io/en/latest/notes/research_publications.html)
* [Youtube Channel of NNI](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw)
* [Bilibili Space of NNI](https://space.bilibili.com/1649051673)
* [Webinar of Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html)
* [Community Discussions](https://github.com/microsoft/nni/discussions)
## Contribution guidelines
If you want to contribute to NNI, be sure to review the [contribution guidelines](https://nni.readthedocs.io/en/stable/notes/contributing.html), which includes instructions of submitting feedbacks, best coding practices, and code of conduct.
We use [GitHub issues](https://github.com/microsoft/nni/issues) to track tracking requests and bugs.
Please use [NNI Discussion](https://github.com/microsoft/nni/discussions) for general questions and new ideas.
For questions of specific use cases, please go to [Stack Overflow](https://stackoverflow.com/questions/tagged/nni).
Participating discussions via the following IM groups is also welcomed.
|Gitter||WeChat|
|----|----|----|
|| OR ||
Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors.
We appreciate all contributions from community to make NNI thrive.
<img src="https://img.shields.io/github/contributors-anon/microsoft/nni"/>
<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/nni&max=240&columns=18" /></a>
## Test status
### Essentials
| Type | Status |
| :---: | :---: |
| Fast test | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master) |
| Full test - HPO | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=90&repoName=microsoft%2Fnni&branchName=master) |
| Full test - NAS | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=89&repoName=microsoft%2Fnni&branchName=master) |
| Full test - compression | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=91&repoName=microsoft%2Fnni&branchName=master) |
### Training services
| Type | Status |
| :---: | :---: |
| Local - linux | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=92&branchName=master) |
| Local - windows | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=98&branchName=master) |
| Remote - linux to linux | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master) |
| Remote - windows to windows | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=99&branchName=master) |
| OpenPAI | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master) |
| Frameworkcontroller | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master) |
| Kubeflow | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master) |
| Hybrid | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master) |
| AzureML | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master) |
## Related Projects
Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-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.
* [nn-Meter](https://github.com/microsoft/nn-Meter) : An accurate inference latency predictor for DNN models on diverse edge devices.
We encourage researchers and students leverage these projects to accelerate the AI development and research.
## License
The entire codebase is under [MIT license](LICENSE).
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"description": "<div align=\"center\">\n<img src=\"docs/img/nni_logo.png\" width=\"600\"/>\n</div>\n\n<br/>\n\n[](LICENSE)\n[](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)\n[](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)\n[](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)\n[](https://github.com/Microsoft/nni/releases)\n[](https://nni.readthedocs.io/en/stable/?badge=stable)\n[](https://github.com/microsoft/nni/graphs/contributors)\n\n\n\n[<img src=\"docs/img/readme_banner.png\" width=\"100%\"/>](https://nni.readthedocs.io/en/stable)\n\nNNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our **[official documentation](https://nni.readthedocs.io/) ([\u7b80\u4f53\u4e2d\u6587\u7248\u70b9\u8fd9\u91cc](https://nni.readthedocs.io/zh/stable))**.\n\n## What's NEW! <a href=\"#nni-released-reminder\"><img width=\"48\" src=\"docs/img/release_icon.png\"></a>\n\n* **New release**: [v3.0 preview is available](https://github.com/microsoft/nni/releases/tag/v3.0rc1) - _released on May-5-2022_\n* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili \u5165\u53e3](https://space.bilibili.com/1649051673) - _last updated on June-22-2022_\n* **New research paper**: [SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute](https://www.usenix.org/system/files/osdi22-zheng-ningxin.pdf) - _published in OSDI 2022_\n* **New research paper**: [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.pdf) - _published in CVPR 2022_\n* **Newly upgraded documentation**: [Doc upgraded](https://nni.readthedocs.io/en/stable)\n\n\n## Installation\n\nSee the [NNI installation guide](https://nni.readthedocs.io/en/stable/installation.html) to install from pip, or build from source.\n\nTo install the current release:\n\n```\n$ pip install nni\n```\n\nTo update NNI to the latest version, add `--upgrade` flag to the above commands.\n\n## NNI capabilities in a glance\n\n<img src=\"docs/img/overview.svg\" width=\"100%\"/>\n\n<table>\n<tbody>\n<tr align=\"center\" valign=\"bottom\">\n<td></td>\n<td>\n<b>Hyperparameter Tuning</b>\n<img src=\"docs/img/bar.png\" />\n</td>\n<td>\n<b>Neural Architecture Search</b>\n<img src=\"docs/img/bar.png\" />\n</td>\n<td>\n<b>Model Compression</b>\n<img src=\"docs/img/bar.png\" />\n</td>\n</tr>\n<tr valign=\"top\">\n<td align=\"center\" valign=\"middle\">\n<b>Algorithms</b>\n</td>\n<td>\n<ul>\n<li><b>Exhaustive search</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner\">Grid Search</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.random_tuner.RandomTuner\">Random</a></li>\n</ul>\n<li><b>Heuristic search</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner\">Anneal</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.evolution_tuner.EvolutionTuner\">Evolution</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.hyperband_advisor.Hyperband\">Hyperband</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.pbt_tuner.PBTTuner\">PBT</a></li>\n</ul>\n<li><b>Bayesian optimization</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.bohb_advisor.BOHB\">BOHB</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.dngo_tuner.DNGOTuner\">DNGO</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.gp_tuner.GPTuner\">GP</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.metis_tuner.MetisTuner\">Metis</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.smac_tuner.SMACTuner\">SMAC</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.tpe_tuner.TpeTuner\">TPE</a></li>\n</ul>\n</ul>\n</td>\n<td>\n<ul>\n<li><b>Multi-trial</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#grid-search-strategy\">Grid Search</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#policy-based-rl-strategy\">Policy Based RL</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#random-strategy\">Random</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#regularized-evolution-strategy\">Regularized Evolution</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#tpe-strategy\">TPE</a></li>\n</ul>\n<li><b>One-shot</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#darts-strategy\">DARTS</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#enas-strategy\">ENAS</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#fbnet-strategy\">FBNet</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#proxylessnas-strategy\">ProxylessNAS</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#spos-strategy\">SPOS</a></li>\n</ul>\n</ul>\n</td>\n<td>\n<ul>\n<li><b>Pruning</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html#level-pruner\">Level</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html#l1-norm-pruner\">L1 Norm</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html#taylor-fo-weight-pruner\">Taylor FO Weight</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html#movement-pruner\">Movement</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html#agp-pruner\">AGP</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html#auto-compress-pruner\">Auto Compress</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/pruner.html\">More...</a></li>\n</ul>\n<li><b>Quantization</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/quantizer.html#naive-quantizer\">Naive</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/quantizer.html#qat-quantizer\">QAT</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/quantizer.html#lsq-quantizer\">LSQ</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/quantizer.html#observer-quantizer\">Observer</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/quantizer.html#dorefa-quantizer\">DoReFa</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/compression/quantizer.html#bnn-quantizer\">BNN</a></li>\n</ul>\n</ul>\n</td>\n<tr align=\"center\" valign=\"bottom\">\n<td></td>\n<td>\n<b>Supported Frameworks</b>\n<img src=\"docs/img/bar.png\" />\n</td>\n<td>\n<b>Training Services</b>\n<img src=\"docs/img/bar.png\" />\n</td>\n<td>\n<b>Tutorials</b>\n<img src=\"docs/img/bar.png\" />\n</td>\n</tr>\n<tr valign=\"top\">\n<td align=\"center\" valign=\"middle\">\n<b>Supports</b>\n</td>\n<td>\n<ul>\n<li>PyTorch</li>\n<li>TensorFlow</li>\n<li>Scikit-learn</li>\n<li>XGBoost</li>\n<li>LightGBM</li>\n<li>MXNet</li>\n<li>Caffe2</li>\n<li>More...</li>\n</ul>\n</td>\n<td>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/local.html\">Local machine</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/remote.html\">Remote SSH servers</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/aml.html\">Azure Machine Learning (AML)</a></li>\n<li><b>Kubernetes Based</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/openpai.html\">OpenAPI</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/kubeflow.html\">Kubeflow</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/frameworkcontroller.html\">FrameworkController</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/adaptdl.html\">AdaptDL</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/paidlc.html\">PAI DLC</a></li>\n</ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/experiment/hybrid.html\">Hybrid training services</a></li>\n</ul>\n</td>\n<td>\n<ul>\n<li><b>HPO</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/hpo_quickstart_pytorch/main.html\">PyTorch</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/hpo_quickstart_tensorflow/main.html\">TensorFlow</a></li>\n</ul>\n<li><b>NAS</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/hello_nas.html\">Hello NAS</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/nasbench_as_dataset.html\">NAS Benchmarks</a></li>\n</ul>\n<li><b>Compression</b></li>\n<ul>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/pruning_quick_start.html\">Pruning</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/pruning_speed_up.html\">Pruning Speedup</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/quantization_quick_start.html\">Quantization</a></li>\n<li><a href=\"https://nni.readthedocs.io/en/latest/tutorials/quantization_speed_up.html\">Quantization Speedup</a></li>\n</ul>\n</ul>\n</td>\n</tbody>\n</table>\n\n<img src=\"docs/static/img/webui.gif\" alt=\"webui\" width=\"100%\"/>\n\n## Resources\n\n* [NNI Documentation Homepage](https://nni.readthedocs.io/en/stable)\n* [NNI Installation Guide](https://nni.readthedocs.io/en/stable/installation.html)\n* [NNI Examples](https://nni.readthedocs.io/en/latest/examples.html)\n* [Python API Reference](https://nni.readthedocs.io/en/latest/reference/python_api.html)\n* [Releases (Change Log)](https://nni.readthedocs.io/en/latest/release.html)\n* [Related Research and Publications](https://nni.readthedocs.io/en/latest/notes/research_publications.html)\n* [Youtube Channel of NNI](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw)\n* [Bilibili Space of NNI](https://space.bilibili.com/1649051673)\n* [Webinar of Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html)\n* [Community Discussions](https://github.com/microsoft/nni/discussions)\n\n## Contribution guidelines\n\nIf you want to contribute to NNI, be sure to review the [contribution guidelines](https://nni.readthedocs.io/en/stable/notes/contributing.html), which includes instructions of submitting feedbacks, best coding practices, and code of conduct.\n\nWe use [GitHub issues](https://github.com/microsoft/nni/issues) to track tracking requests and bugs.\nPlease use [NNI Discussion](https://github.com/microsoft/nni/discussions) for general questions and new ideas.\nFor questions of specific use cases, please go to [Stack Overflow](https://stackoverflow.com/questions/tagged/nni).\n\nParticipating discussions via the following IM groups is also welcomed.\n\n|Gitter||WeChat|\n|----|----|----|\n|| OR ||\n\nOver the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors.\nWe appreciate all contributions from community to make NNI thrive.\n\n<img src=\"https://img.shields.io/github/contributors-anon/microsoft/nni\"/>\n\n<a href=\"https://github.com/microsoft/nni/graphs/contributors\"><img src=\"https://contrib.rocks/image?repo=microsoft/nni&max=240&columns=18\" /></a>\n\n## Test status\n\n### Essentials\n\n| Type | Status |\n| :---: | :---: |\n| Fast test | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master) |\n| Full test - HPO | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=90&repoName=microsoft%2Fnni&branchName=master) |\n| Full test - NAS | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=89&repoName=microsoft%2Fnni&branchName=master) |\n| Full test - compression | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=91&repoName=microsoft%2Fnni&branchName=master) |\n\n### Training services\n\n| Type | Status |\n| :---: | :---: |\n| Local - linux | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=92&branchName=master) |\n| Local - windows | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=98&branchName=master) |\n| Remote - linux to linux | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master) |\n| Remote - windows to windows | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=99&branchName=master) |\n| OpenPAI | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master) |\n| Frameworkcontroller | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master) |\n| Kubeflow | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master) |\n| Hybrid | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master) |\n| AzureML | [](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master) |\n\n## Related Projects\n\nTargeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/) had also released few other open source projects.\n\n* [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.\n* [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.\n* [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.\n* [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.\n* [nn-Meter](https://github.com/microsoft/nn-Meter) : An accurate inference latency predictor for DNN models on diverse edge devices.\n\nWe encourage researchers and students leverage these projects to accelerate the AI development and research.\n\n## License\n\nThe entire codebase is under [MIT license](LICENSE).\n",
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