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.
NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by [filling an issue](https://github.com/microsoft/nni/issues/new/choose).
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.
We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.
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.
If you plan to contribute new features, new tuners, new training services, etc. please first open an issue or reuse an exisiting issue, and discuss the feature with us. We will discuss with you on the issue timely or set up conference calls if needed.
After getting familiar with contribution agreements, you are ready to create your first PR =), follow the NNI developer tutorials to get start:
To learn more about making a contribution to NNI, please refer to our [How-to contribution page](https://nni.readthedocs.io/en/stable/contribution.html).
* 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).
We appreciate all contributions and thank all the contributors!
* 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 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.
* ### **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/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
*[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
## **Feedback**
## **Feedback**
*[File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
*[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.
* 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.
Different from the tutorials and examples in the rest of the document which show the usage of a feature, this part mainly introduces end-to-end scenarios and use cases to help users further understand how NNI can help them. NNI can be widely adopted in various scenarios. We also encourage community contributors to share their AutoML practices especially the NNI usage practices from their experience.
Different from the tutorials and examples in the rest of the document which show the usage of a feature, this part mainly introduces end-to-end scenarios and use cases to help users further understand how NNI can help them. NNI can be widely adopted in various scenarios. We also encourage community contributors to share their AutoML practices especially the NNI usage practices from their experience.
Use Cases and Solutions
=======================
.. toctree::
.. toctree::
:maxdepth: 2
:maxdepth: 2
...
@@ -14,3 +16,24 @@ Different from the tutorials and examples in the rest of the document which show
...
@@ -14,3 +16,24 @@ Different from the tutorials and examples in the rest of the document which show
Performance measurement, comparison and analysis <perf_compare>
Performance measurement, comparison and analysis <perf_compare>
Use NNI on Google Colab <NNI_colab_support>
Use NNI on Google Colab <NNI_colab_support>
Auto Completion for nnictl Commands <AutoCompletion>
Auto Completion for nnictl Commands <AutoCompletion>
External Repositories and References
====================================
With authors' permission, we listed a set of NNI usage examples and relevant articles.
External Repositories
=====================
* `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
* `Parallelizing a Sequential Algorithm TPE <./ParallelizingTpeSearch.rst>`__
* `Automatically tuning SVD with NNI <./RecommendersSvd.rst>`__
* `Automatically tuning SPTAG with NNI <./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>`__
* `(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>`__
* `(in Chinese) A summary of NNI new capabilities in 2019 <https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA>`__
