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README.md
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[简体中文](README_zh_CN.md)
**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/Compression/Overview.md">Model Compression</a>.
**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.rst">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.rst">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.rst">Hyperparameter Tuning</a> and <a href="docs/en_US/Compression/Overview.rst">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>, <a href="docs/en_US/TrainingService/DLTSMode.md">DLWorkspace (aka. DLTS)</a>, <a href="docs/en_US/TrainingService/AMLMode.md">AML (Azure Machine Learning)</a>, <a href="docs/en_US/TrainingService/AdaptDLMode.md">AdaptDL (aka. ADL)</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 <a href="docs/en_US/TrainingService/LocalMode.rst">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.rst">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.rst">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.rst">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.rst">FrameworkController on K8S (AKS etc.)</a>, <a href="docs/en_US/TrainingService/DLTSMode.rst">DLWorkspace (aka. DLTS)</a>, <a href="docs/en_US/TrainingService/AMLMode.rst">AML (Azure Machine Learning)</a>, <a href="docs/en_US/TrainingService/AdaptDLMode.rst">AdaptDL (aka. ADL)</a> and other cloud options.
## **Who should consider using NNI**
......@@ -68,7 +68,7 @@ Within the following table, we summarized the current NNI capabilities, we are g
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
<a href="docs/en_US/SupportedFramework_Library.rst">More...</a><br/>
</ul>
</ul>
<ul>
......@@ -77,7 +77,7 @@ Within the following table, we summarized the current NNI capabilities, we are g
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
<a href="docs/en_US/SupportedFramework_Library.rst">More...</a><br/>
</ul>
</ul>
<ul>
......@@ -86,99 +86,99 @@ Within the following table, we summarized the current NNI capabilities, we are g
<li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
<li><a href="examples/trials/mnist-tfv1">MNIST-tensorflow</li></a>
<li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="docs/en_US/TrialExample/GbdtExample.md">Auto-gbdt</a></li>
<li><a href="docs/en_US/TrialExample/Cifar10Examples.md">Cifar10-pytorch</li></a>
<li><a href="docs/en_US/TrialExample/SklearnExamples.md">Scikit-learn</a></li>
<li><a href="docs/en_US/TrialExample/EfficientNet.md">EfficientNet</a></li>
<li><a href="docs/en_US/TrialExample/OpEvoExamples.md">Kernel Tunning</li></a>
<a href="docs/en_US/SupportedFramework_Library.md">More...</a><br/>
<li><a href="docs/en_US/TrialExample/GbdtExample.rst">Auto-gbdt</a></li>
<li><a href="docs/en_US/TrialExample/Cifar10Examples.rst">Cifar10-pytorch</li></a>
<li><a href="docs/en_US/TrialExample/SklearnExamples.rst">Scikit-learn</a></li>
<li><a href="docs/en_US/TrialExample/EfficientNet.rst">EfficientNet</a></li>
<li><a href="docs/en_US/TrialExample/OpEvoExamples.rst">Kernel Tunning</li></a>
<a href="docs/en_US/SupportedFramework_Library.rst">More...</a><br/>
</ul>
</ul>
</td>
<td align="left" >
<a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a>
<a href="docs/en_US/Tuner/BuiltinTuner.rst">Hyperparameter Tuning</a>
<ul>
<b>Exhaustive search</b>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Random">Random Search</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#Batch">Batch</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#Random">Random Search</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#GridSearch">Grid Search</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#Batch">Batch</a></li>
</ul>
<b>Heuristic search</b>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Anneal">Anneal</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#Hyperband">Hyperband</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#PBTTuner">PBT</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#Evolution">Naïve Evolution</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#Anneal">Anneal</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#Hyperband">Hyperband</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#PBTTuner">PBT</a></li>
</ul>
<b>Bayesian optimization</b>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#BOHB">BOHB</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#TPE">TPE</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#SMAC">SMAC</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#GPTuner">GP Tuner</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#BOHB">BOHB</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#TPE">TPE</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#SMAC">SMAC</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#MetisTuner">Metis Tuner</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#GPTuner">GP Tuner</a></li>
</ul>
<b>RL Based</b>
<ul>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#PPOTuner">PPO Tuner</a> </li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#PPOTuner">PPO Tuner</a> </li>
</ul>
</ul>
<a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>
<a href="docs/en_US/NAS/Overview.rst">Neural Architecture Search</a>
<ul>
<ul>
<li><a href="docs/en_US/NAS/ENAS.md">ENAS</a></li>
<li><a href="docs/en_US/NAS/DARTS.md">DARTS</a></li>
<li><a href="docs/en_US/NAS/PDARTS.md">P-DARTS</a></li>
<li><a href="docs/en_US/NAS/CDARTS.md">CDARTS</a></li>
<li><a href="docs/en_US/NAS/SPOS.md">SPOS</a></li>
<li><a href="docs/en_US/NAS/Proxylessnas.md">ProxylessNAS</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
<li><a href="docs/en_US/NAS/TextNAS.md">TextNAS</a></li>
<li><a href="docs/en_US/NAS/Cream.md">Cream</a></li>
<li><a href="docs/en_US/NAS/ENAS.rst">ENAS</a></li>
<li><a href="docs/en_US/NAS/DARTS.rst">DARTS</a></li>
<li><a href="docs/en_US/NAS/PDARTS.rst">P-DARTS</a></li>
<li><a href="docs/en_US/NAS/CDARTS.rst">CDARTS</a></li>
<li><a href="docs/en_US/NAS/SPOS.rst">SPOS</a></li>
<li><a href="docs/en_US/NAS/Proxylessnas.rst">ProxylessNAS</a></li>
<li><a href="docs/en_US/Tuner/BuiltinTuner.rst#NetworkMorphism">Network Morphism</a></li>
<li><a href="docs/en_US/NAS/TextNAS.rst">TextNAS</a></li>
<li><a href="docs/en_US/NAS/Cream.rst">Cream</a></li>
</ul>
</ul>
<a href="docs/en_US/Compression/Overview.md">Model Compression</a>
<a href="docs/en_US/Compression/Overview.rst">Model Compression</a>
<ul>
<b>Pruning</b>
<ul>
<li><a href="docs/en_US/Compression/Pruner.md#agp-pruner">AGP Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.md#slim-pruner">Slim Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.md#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.md#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.md#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.md#admm-pruner">ADMM Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.md#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#agp-pruner">AGP Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#slim-pruner">Slim Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#admm-pruner">ADMM Pruner</a></li>
<li><a href="docs/en_US/Compression/Pruner.rst#autocompress-pruner">AutoCompress Pruner</a></li>
</ul>
<b>Quantization</b>
<ul>
<li><a href="docs/en_US/Compression/Quantizer.md#qat-quantizer">QAT Quantizer</a></li>
<li><a href="docs/en_US/Compression/Quantizer.md#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="docs/en_US/Compression/Quantizer.rst#qat-quantizer">QAT Quantizer</a></li>
<li><a href="docs/en_US/Compression/Quantizer.rst#dorefa-quantizer">DoReFa Quantizer</a></li>
</ul>
</ul>
<a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering (Beta)</a>
<a href="docs/en_US/FeatureEngineering/Overview.rst">Feature Engineering (Beta)</a>
<ul>
<li><a href="docs/en_US/FeatureEngineering/GradientFeatureSelector.md">GradientFeatureSelector</a></li>
<li><a href="docs/en_US/FeatureEngineering/GBDTSelector.md">GBDTSelector</a></li>
<li><a href="docs/en_US/FeatureEngineering/GradientFeatureSelector.rst">GradientFeatureSelector</a></li>
<li><a href="docs/en_US/FeatureEngineering/GBDTSelector.rst">GBDTSelector</a></li>
</ul>
<a href="docs/en_US/Assessor/BuiltinAssessor.md">Early Stop Algorithms</a>
<a href="docs/en_US/Assessor/BuiltinAssessor.rst">Early Stop Algorithms</a>
<ul>
<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>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.rst#Medianstop">Median Stop</a></li>
<li><a href="docs/en_US/Assessor/BuiltinAssessor.rst#Curvefitting">Curve Fitting</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a></li>
<li><a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a></li>
<li><a href="docs/en_US/TrainingService/AMLMode.md">AML(Azure Machine Learning)</a></li>
<li><a href="docs/en_US/TrainingService/LocalMode.rst">Local Machine</a></li>
<li><a href="docs/en_US/TrainingService/RemoteMachineMode.rst">Remote Servers</a></li>
<li><a href="docs/en_US/TrainingService/AMLMode.rst">AML(Azure Machine Learning)</a></li>
<li><b>Kubernetes based services</b></li>
<ul>
<li><a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a></li>
<li><a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a></li>
<li><a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="docs/en_US/TrainingService/DLTSMode.md">DLWorkspace (aka. DLTS)</a></li>
<li><a href="docs/en_US/TrainingService/AdaptDLMode.md">AdaptDL (aka. ADL)</a></li>
<li><a href="docs/en_US/TrainingService/PaiMode.rst">OpenPAI</a></li>
<li><a href="docs/en_US/TrainingService/KubeflowMode.rst">Kubeflow</a></li>
<li><a href="docs/en_US/TrainingService/FrameworkControllerMode.rst">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="docs/en_US/TrainingService/DLTSMode.rst">DLWorkspace (aka. DLTS)</a></li>
<li><a href="docs/en_US/TrainingService/AdaptDLMode.rst">AdaptDL (aka. ADL)</a></li>
</ul>
</ul>
</td>
......@@ -193,21 +193,21 @@ Within the following table, we summarized the current NNI capabilities, we are g
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/latest/autotune_ref.html#trial">Python API</a></li>
<li><a href="docs/en_US/Tutorial/AnnotationSpec.md">NNI Annotation</a></li>
<li><a href="docs/en_US/Tutorial/AnnotationSpec.rst">NNI Annotation</a></li>
<li><a href="https://nni.readthedocs.io/en/latest/installation.html">Supported OS</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/Tuner/CustomizeTuner.md">CustomizeTuner</a></li>
<li><a href="docs/en_US/Assessor/CustomizeAssessor.md">CustomizeAssessor</a></li>
<li><a href="docs/en_US/Tutorial/InstallCustomizedAlgos.md">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
<li><a href="docs/en_US/Tuner/CustomizeTuner.rst">CustomizeTuner</a></li>
<li><a href="docs/en_US/Assessor/CustomizeAssessor.rst">CustomizeAssessor</a></li>
<li><a href="docs/en_US/Tutorial/InstallCustomizedAlgos.rst">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="docs/en_US/TrainingService/Overview.md">Support TrainingService</li>
<li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.md">Implement TrainingService</a></li>
<li><a href="docs/en_US/TrainingService/Overview.rst">Support TrainingService</li>
<li><a href="docs/en_US/TrainingService/HowToImplementTrainingService.rst">Implement TrainingService</a></li>
</ul>
</td>
</tr>
......@@ -239,8 +239,8 @@ For detail system requirements of NNI, please refer to [here](https://nni.readth
Note:
* If there is any privilege issue, add `--user` to install NNI in the user directory.
* Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install [NNI on Windows](docs/en_US/Tutorial/InstallationWin.md).
* If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/Tutorial/FAQ.md). For FAQ on Windows, please refer to [NNI on Windows](docs/en_US/Tutorial/InstallationWin.md#faq).
* Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install [NNI on Windows](docs/en_US/Tutorial/InstallationWin.rst).
* If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/Tutorial/FAQ.rst). For FAQ on Windows, please refer to [NNI on Windows](docs/en_US/Tutorial/InstallationWin.rst#faq).
### **Verify installation**
......@@ -294,7 +294,7 @@ You can use these commands to get more information about the experiment
-----------------------------------------------------------------------
```
* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/Tutorial/WebUI.md) are more Web UI pages.
* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/Tutorial/WebUI.rst) are more Web UI pages.
<table style="border: none">
<th><img src="./docs/img/webui-img/full-oview.png" alt="drawing" width="395" height="300"/></th>
......@@ -316,14 +316,14 @@ This project has adopted the [Microsoft Open Source Code of Conduct](https://ope
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 simple issues: ['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).
* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.md)
* [How to debug](docs/en_US/Tutorial/HowToDebug.md)
* If you have any questions on usage, review [FAQ](https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/FAQ.md) first, if there are no relevant issues and answers to your question, try contact NNI dev team and users in [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) or [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
* [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](docs/en_US/NAS/Advanced.md)
* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.md)
* We recommend new contributors to start with simple issues: [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).
* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.rst)
* [How to debug](docs/en_US/Tutorial/HowToDebug.rst)
* If you have any questions on usage, review [FAQ](https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/FAQ.rst) first, if there are no relevant issues and answers to your question, try contact NNI dev team and users in [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) or [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.rst)
* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.rst)
* [Implement a new NAS trainer on NNI](docs/en_US/NAS/Advanced.rst)
* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.rst)
## **External Repositories and References**
With authors' permission, we listed a set of NNI usage examples and relevant articles.
......@@ -331,14 +331,14 @@ With authors' permission, we listed a set of NNI usage examples and relevant art
* ### **External Repositories** ###
* Run [ENAS](examples/nas/enas/README.md) with NNI
* [Automatic Feature Engineering](examples/feature_engineering/auto-feature-engineering/README.md) with NNI
* [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
* [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/examples/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
* [scikit-nni](https://github.com/ksachdeva/scikit-nni) Hyper-parameter search for scikit-learn pipelines using NNI
* ### **Relevant Articles** ###
* [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparison.md)
* [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparison.md)
* [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
* [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
* [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
* [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparison.rst)
* [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparison.rst)
* [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.rst)
* [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.rst)
* [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.rst)
* [Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI](https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1)
* **Blog (in Chinese)** - [AutoML tools (Advisor, NNI and Google Vizier) comparison](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90) by [@gaocegege](https://github.com/gaocegege) - 总结与分析 section of design and implementation of kubeflow/katib
* **Blog (in Chinese)** - [A summary of NNI new capabilities in 2019](https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA) by @squirrelsc
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## 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.
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.
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