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/test/ut/retiarii/_debug_graph_data.json
/test/ut/retiarii/out.tmp
# example generated files
/nni_assets/**/data/
# Logs
logs
*.log
......
CONTRIBUTING.md deleted 100644 → 0
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# Contributing to NNI
Welcome, and thank you for your interest in contributing to NNI!
There are many ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
# Provide feedback or ask a question
* [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|
|----|----|----|
|![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)|
# Look for an existing issue
Before you create a new issue, please do a search in [open issues](https://github.com/microsoft/nni/issues) to see if the issue or feature request has already been filed.
Be sure to scan through the [most popular](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3AFAQ+sort%3Areactions-%2B1-desc) feature requests.
If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
* 👍 - upvote
* 👎 - downvote
If you cannot find an existing issue that describes your bug or feature, create a new issue using the guidelines below.
# Writing good bug reports or feature requests
File a single issue per problem and feature request. Do not enumerate multiple bugs or feature requests in the same issue.
Provide as much information as you think might relevant to the context (thinking the issue is assigning to you, what kinds of info you will need to debug it!!!). To give you a general idea about what kinds of info are useful for developers to dig out the issue, we had provided issue template for you.
Once you had submitted an issue, be sure to follow it for questions and discussions.
Once the bug is fixed or feature is addressed, be sure to close the issue.
# Contributing fixes or examples
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.opensource.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., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
# Code of Conduct
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](mailto:opencode@microsoft.com) with any additional questions or comments.
# How to Contribute
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.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)
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# 贡献代码
非常感谢您有兴趣对 NNI 做出贡献！
除了编写代码外，您还可以通过多种方式参与， 本文档的目的是提供一个如何参与贡献的高层次概述。
# 反馈或提问
* 在 Github 上创建 [issue](https://github.com/microsoft/nni/issues/new/choose)。
* 在 [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true) 上使用 nni 标签提问。
* 在 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 中参与讨论。
加入聊天组：
| Gitter | | 微信 |
| -------------------------------------------------------------------------------------------------------------- | - | ----------------------------------------------------------------------- |
| ![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png) | 或 | ![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png) |
# 查找现有问题
在创建新 issue 之前，请在 [open issues](https://github.com/microsoft/nni/issues) 中进行搜索，以查看问题或功能请求是否已经存在。
确保已经浏览了 [最热门](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3AFAQ+sort%3Areactions-%2B1-desc) 的功能请求。
如果您的问题已经存在，请在下方发表评论或添加[回应](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments)。 通过回应来代替“+1”评论：
* 👍 - 赞成
* 👎 - 反对
如果未能找到描述您 Bug 或功能的现有问题，请使用以下指南创建一个新问题。
# 编写良好的错误报告或功能请求
针对每个错误和功能请求提交一个问题， 不要在同一问题中列举多个 Bug 或功能请求。
尽可能多地提供您认为与上下文相关的信息（思考问题如果分配给您，您需要什么样的信息来调试它）。 为了让您大致了解哪些信息对开发人员解决问题有帮助，我们为您提供了问题模板。
提交问题后，请务必跟进问题并参与讨论。
修正 Bug 或实现功能后，请务必关闭此问题。
# 贡献修复或示例
此项目欢迎任何贡献和建议。 大多数贡献需要您同意参与者许可协议（CLA），来声明您有权并授予我们使用您贡献的权利。 有关详细信息，请访问 https://cla.opensource.microsoft.com。
当你提交拉取请求时，CLA 机器人会自动检查你是否需要提供 CLA，并修饰这个拉取请求（例如标签、注释等）。 只需要按照机器人提供的说明进行操作即可。 CLA 只需要同意一次，就能应用到所有的代码仓库上。
# 行为准则
该项目采用了 [ Microsoft 开源行为准则 ](https://opensource.microsoft.com/codeofconduct/)。 有关详细信息，请参阅[行为守则常见问题解答](https://opensource.microsoft.com/codeofconduct/faq/)或联系 opencode@microsoft.com 咨询问题或评论。
# 参与贡献
熟悉贡献协议后，即可按照 NNI 开发人员教程，创建第一个 PR =)：
* 推荐新贡献者先从简单的问题开始：['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) 或 ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22)。
* [NNI 开发环境安装教程](docs/zh_CN/Tutorial/SetupNniDeveloperEnvironment.rst)
* [如何调试](docs/zh_CN/Tutorial/HowToDebug.rst)
* 如果有使用上的问题，可先查看[常见问题解答](https://github.com/microsoft/nni/blob/master/docs/zh_CN/Tutorial/FAQ.rst)。如果没能解决问题，可通过 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 联系 NNI 开发团队或在 GitHub 上 [报告问题](https://github.com/microsoft/nni/issues/new/choose)。
* [自定义 Tuner](docs/zh_CN/Tuner/CustomizeTuner.rst)
* [实现定制的训练平台](docs/zh_CN/TrainingService/HowToImplementTrainingService.rst)
* [在 NNI 上实现新的 NAS Trainer](docs/zh_CN/NAS/Advanced.rst)
* [自定义 Advisor](docs/zh_CN/Tuner/CustomizeAdvisor.rst)
README.md
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<p align="center">
<img src="docs/img/nni_logo.png" width="300"/>
</p>
<div align="center">
<img src="docs/img/nni_logo.png" width="600"/>
</div>
<br/>
[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE)
[![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&branchName=master)
[![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
[![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
[![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Join the chat at https://gitter.im/Microsoft/nni](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases)
[![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=stable)](https://nni.readthedocs.io/en/stable/?badge=stable)
[![](https://img.shields.io/github/contributors-anon/microsoft/nni)](https://github.com/microsoft/nni/graphs/contributors)
[NNI Doc](https://nni.readthedocs.io/) | [简体中文](README_zh_CN.md)
**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering</a>, <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search</a>, <a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html">Hyperparameter Tuning</a> and <a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">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="https://nni.readthedocs.io/en/stable/TrainingService/LocalMode.html">Local Machine</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/RemoteMachineMode.html">Remote Servers</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/PaiMode.html">OpenPAI</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/KubeflowMode.html">Kubeflow</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a> , other cloud options and even <a href="https://nni.readthedocs.io/en/stable/TrainingService/HybridMode.html">Hybrid mode</a>.
## **Who should consider using NNI**
[<img src="docs/img/readme_banner.png" width="100%"/>](https://nni.readthedocs.io/)
* 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 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 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!** &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
## What's NEW! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
* **New release**: [v2.6 is available](https://github.com/microsoft/nni/releases/tag/v2.6) - _released on Jan-19-2022_
* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on May-26-2021_
* **New release**: [v2.7 is available](https://github.com/microsoft/nni/releases/tag/v2.7) - _released on Apr-18-2022_
* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on Apr-18-2022_
* **New webinar**: [Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - _scheduled on June-24-2021_
* **New community channel**: [Discussions](https://github.com/microsoft/nni/discussions)
* **New emoticons release**: [nnSpider](./docs/source/Tutorial/NNSpider.md)
<p align="center">
<a href="#nni-spider"><img width="100%" src="docs/img/emoicons/home.svg" /></a>
</p>
## **NNI capabilities in a glance**
NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. 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 state-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>
<table>
<tbody>
<tr align="center" valign="bottom">
<td>
</td>
<td>
<b>Frameworks & Libraries</b>
<img src="docs/img/bar.png"/>
</td>
<td>
<b>Algorithms</b>
<img src="docs/img/bar.png"/>
</td>
<td>
<b>Training Services</b>
<img src="docs/img/bar.png"/>
</td>
</tr>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>Built-in</b>
</td>
<td>
<ul><li><b>Supported Frameworks</b></li>
<ul>
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Supported Libraries</b></li>
<ul>
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Examples</b></li>
<ul>
<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="https://nni.readthedocs.io/en/stable/TrialExample/GbdtExample.html">Auto-gbdt</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/SklearnExamples.html">Scikit-learn</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/EfficientNet.html">EfficientNet</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/OpEvoExamples.html">Kernel Tunning</li></a>
<a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
</ul>
</ul>
</td>
<td align="left" >
<a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html">Hyperparameter Tuning</a>
<ul>
<b>Exhaustive search</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Random">Random Search</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GridSearch">Grid Search</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Batch">Batch</a></li>
</ul>
<b>Heuristic search</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Evolution">Naïve Evolution</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Anneal">Anneal</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
</ul>
<b>Bayesian optimization</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#TPE">TPE</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
</ul>
</ul>
<a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/FBNet.html">FBNet</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">More...</a></li>
</ul>
<a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>
<ul>
<b>Pruning</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">More...</a></li>
</ul>
<b>Quantization</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
</ul>
<a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html">Early Stop Algorithms</a>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html#MedianStop">Median Stop</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/LocalMode.html">Local Machine</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/RemoteMachineMode.html">Remote Servers</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/HybridMode.html">Hybrid mode</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
<li><b>Kubernetes based services</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/PaiMode.html">OpenPAI</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/KubeflowMode.html">Kubeflow</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a></li>
</ul>
</ul>
</td>
</tr>
<tr align="center" valign="bottom">
</td>
</tr>
<tr valign="top">
<td valign="middle">
<b>References</b>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/autotune_ref.html#trial">Python API</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/installation.html">Supported OS</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/QuickStart.html#define-your-model-space">Define NAS Model Space</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/Overview.html">Support TrainingService</li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/HowToImplementTrainingService.html">Implement TrainingService</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
## **Installation**
* **New emoticons release**: [nnSpider](https://nni.readthedocs.io/en/latest/sharings/nn_spider.html)
<div align="center">
<a href="#nni-spider"><img width="100%" src="docs/source/sharings/images/nn_spider/home.svg" /></a>
</div>
### **Install**
## Installation
NNI supports and is tested on Ubuntu >= 18.04, Windows 10 >= 21H2, and macOS >= 11.
Simply run the following `pip install` in an environment that has `python 64-bit >= 3.7`.
See the [NNI installation guide](https://nni.readthedocs.io/en/stable/installation.html) to install from pip, or build from source.
Linux or macOS
To install the current release:
```bash
python3 -m pip install --upgrade nni
```
Windows
```bash
python -m pip install --upgrade nni
$ pip install nni
```
If you want to try latest code, please [install NNI](https://nni.readthedocs.io/en/stable/installation.html) from source code.
For detail system requirements of NNI, please refer to [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationLinux.html#system-requirements) for Linux & macOS, and [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#system-requirements) for Windows.
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](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html).
* If there is any error like `Segmentation fault`, please refer to [FAQ](https://nni.readthedocs.io/en/stable/Tutorial/FAQ.html). For FAQ on Windows, please refer to [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#faq).
### **Verify installation**
To update NNI to the latest version, add `--upgrade` flag to the above commands.
* Download the examples via clone the source code.
## NNI capabilities in a glance
```bash
git clone -b v2.6 https://github.com/Microsoft/nni.git
```
<img src="docs/img/overview.svg" width="100%"/>
* Run the MNIST example.
Linux or macOS
```bash
nnictl create --config nni/examples/trials/mnist-pytorch/config.yml
```
Windows
```powershell
nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml
```
* Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`.
```text
INFO: Starting restful server...
INFO: Successfully started Restful server!
INFO: Setting local config...
INFO: Successfully set local config!
INFO: Starting experiment...
INFO: Successfully started experiment!
-----------------------------------------------------------------------
The experiment id is egchD4qy
The Web UI urls are: http://223.255.255.1:8080 http://127.0.0.1:8080
-----------------------------------------------------------------------
You can use these commands to get more information about the experiment
-----------------------------------------------------------------------
commands description
1. nnictl experiment show show the information of experiments
2. nnictl trial ls list all of trial jobs
3. nnictl top monitor the status of running experiments
4. nnictl log stderr show stderr log content
5. nnictl log stdout show stdout log content
6. nnictl stop stop an experiment
7. nnictl trial kill kill a trial job by id
8. nnictl --help get help information about nnictl
-----------------------------------------------------------------------
```
* Open the `Web UI url` in your browser, you can view detailed information of the experiment and all the submitted trial jobs as shown below. [Here](https://nni.readthedocs.io/en/stable/Tutorial/WebUI.html) are more Web UI pages.
<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/index.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_mnist.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_mnist.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%"/>
## **Releases and Contributing**
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).
We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.
## Resources
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.
* [NNI Documentation Homepage](https://nni.readthedocs.io/)
* [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)
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).
## Contribution guidelines
We appreciate all contributions and thank all the contributors!
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.
<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/nni" /></a>
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.
## **Feedback**
* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
* Open or participate in a [discussion](https://github.com/microsoft/nni/discussions).
* 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|
|----|----|----|
|![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)|
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
......@@ -363,6 +276,6 @@ Targeting at openness and advancing state-of-art technology, [Microsoft Research
We encourage researchers and students leverage these projects to accelerate the AI development and research.
## **License**
## License
The entire codebase is under [MIT license](LICENSE)
The entire codebase is under [MIT license](LICENSE).
README_zh_CN.md deleted 100644 → 0
View file @ 14d2966b
<p align="center">
<img src="docs/img/nni_logo.png" width="300"/>
</p>
* * *
[![MIT 许可证](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE) [![生成状态](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&branchName=master) [![问题](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen) [![Bug](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug) [![拉取请求](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen) [![版本](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![进入 https://gitter.im/Microsoft/nni 聊天室提问](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![文档状态](https://readthedocs.org/projects/nni/badge/?version=stable)](https://nni.readthedocs.io/zh/stable/?badge=stable)
[NNI 文档](https://nni.readthedocs.io/zh/stable/) | [English](README.md)
**NNI (Neural Network Intelligence)** 是一个帮助用户**自动**进行[特征工程](docs/zh_CN/FeatureEngineering/Overview.rst)，[神经网络架构搜索](docs/zh_CN/NAS/Overview.rst)，[超参调优](docs/zh_CN/Tuner/BuiltinTuner.rst)以及[模型压缩](docs/zh_CN/Compression/Overview.rst)的轻量且强大的工具包。
NNI 管理自动机器学习 (AutoML) 的 Experiment，**调度运行**由调优算法生成的 Trial 任务来找到最好的神经网络架构和/或超参，支持**各种训练环境**，如[本机](docs/zh_CN/TrainingService/LocalMode.rst)，[远程服务器](docs/zh_CN/TrainingService/RemoteMachineMode.rst)，[OpenPAI](docs/zh_CN/TrainingService/PaiMode.rst)，[Kubeflow](docs/zh_CN/TrainingService/KubeflowMode.rst)，[基于 K8S 的 FrameworkController（如，AKS 等）](docs/zh_CN/TrainingService/FrameworkControllerMode.rst)， [DLWorkspace (又称 DLTS)](docs/zh_CN/TrainingService/DLTSMode.rst), [AML (Azure Machine Learning)](docs/zh_CN/TrainingService/AMLMode.rst), [AdaptDL（又称 ADL）](docs/zh_CN/TrainingService/AdaptDLMode.rst) ，和其他的云平台甚至 [混合模式](docs/zh_CN/TrainingService/HybridMode.rst) 。 DLTS)</a>，[AML (Azure Machine Learning)](https://nni.readthedocs.io/zh/stable/TrainingService/AMLMode.html)[AdaptDL（又称 ADL）](https://nni.readthedocs.io/zh/stable/TrainingService/AdaptDLMode.html) ，和其他的云平台甚至[混合模式](https://nni.readthedocs.io/zh/stable/TrainingService/HybridMode.html) 。
## **使用场景**
* 想要在自己的代码、模型中试验**不同的自动机器学习算法**。
* 想要在**不同的环境中**加速运行自动机器学习。
* 想要更容易**实现或试验新的自动机器学习算法**的研究员或数据科学家，包括：超参调优算法，神经网络搜索算法以及模型压缩算法。
* 在机器学习平台中**支持自动机器学习**。
## **最新消息！** &nbsp;[<img width="48" src="docs/img/release_icon.png" />](#nni-released-reminder)
* **最新版本**：[v2.6 已发布](https://github.com/microsoft/nni/releases/tag/v2.6) - *2022年1月19日*
* **最新视频 demo**：[Youtube 入口](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - *上次更新：2021年5月26日*
* **最新网络研讨会**: [介绍Retiarii：NNI 上的深度学习探索性训练框架](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - *2021年6月24日*
* **最新互动渠道**: [Discussions](https://github.com/microsoft/nni/discussions)
* **最新粉丝福利表情包上线**： [nnSpider](./docs/en_US/Tutorial/NNSpider.md)
<p align="center">
<a href="#nni-spider"><img width="100%" src="docs/img/emoicons/home.svg" /></a>
</p>
## **NNI 功能一览**
NNI 提供命令行工具以及友好的 WebUI 来管理训练的 Experiment。 通过可扩展的 API，可定制自动机器学习算法和训练平台。 为了方便新用户，NNI 内置了最新的自动机器学习算法，并为流行的训练平台提供了开箱即用的支持。
下表中，包含了 NNI 的功能，同时在不断地增添新功能，也非常希望您能贡献其中。
<p align="center">
<a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
</p>
<table>
<tbody>
<tr align="center" valign="bottom">
<td>
</td>
<td>
<b>支持的框架和库</b>
<img src="docs/img/bar.png"/>
</td>
<td>
<b>算法</b>
<img src="docs/img/bar.png"/>
</td>
<td>
<b>训练平台</b>
<img src="docs/img/bar.png"/>
</td>
</tr>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>内置</b>
</td>
<td>
<ul><li><b>支持的框架</b></li>
<ul>
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="https://nni.readthedocs.io/zh/stable/SupportedFramework_Library.html">更多...</a><br/>
</ul>
</ul>
<ul>
<li><b>支持的库</b></li>
<ul>
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="https://nni.readthedocs.io/zh/stable/SupportedFramework_Library.html">更多...</a><br/>
</ul>
</ul>
<ul>
<li><b>示例</b></li>
<ul>
<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="https://nni.readthedocs.io/zh/stable/TrialExample/GbdtExample.html">Auto-gbdt</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
<li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/SklearnExamples.html">Scikit-learn</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/EfficientNet.html">EfficientNet</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/OpEvoExamples.html">GPU Kernel 调优</li></a>
<a href="https://nni.readthedocs.io/zh/stable/SupportedFramework_Library.html">更多...</a><br/>
</ul>
</ul>
</td>
<td align="left" >
<a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html">超参调优</a>
<ul>
<b>穷举搜索</b>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Random">Random Search（随机搜索）</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#GridSearch">Grid Search（遍历搜索）</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Batch">Batch（批处理）</a></li>
</ul>
<b>启发式搜索</b>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Evolution">Naïve Evolution（朴素进化）</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Anneal">Anneal（退火算法）</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
</ul>
<b>贝叶斯优化</b>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#TPE">TPE</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#DNGOTuner">PPO Tuner</a></li>
</ul>
</ul>
<a href="https://nni.readthedocs.io/zh/stable/NAS/Overview.html">神经网络架构搜索</a>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/ENAS.html">ENAS</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/DARTS.html">DARTS</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/SPOS.html">SPOS</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/FBNet.html">FBNet</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/ExplorationStrategies.html">基于强化学习</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/ExplorationStrategies.html">正则进化</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/Overview.html">更多...</a></li>
</ul>
<a href="https://nni.readthedocs.io/zh/stable/Compression/Overview.html">模型压缩</a>
<ul>
<b>剪枝</b>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Overview.html">更多...</a></li>
</ul>
<b>量化</b>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="https://nni.readthedocs.io/zh/stable/FeatureEngineering/Overview.html">特征工程（测试版）</a>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
</ul>
<a href="https://nni.readthedocs.io/zh/stable/Assessor/BuiltinAssessor.html">提前终止算法</a>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Assessor/BuiltinAssessor.html#MedianStop">Median Stop（中位数终止）</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting（曲线拟合）</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/LocalMode.html">本机</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/RemoteMachineMode.html">远程计算机</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/HybridMode.html">混合模式</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
<li><b>基于 Kubernetes 的平台</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/PaiMode.html">OpenPAI</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/KubeflowMode.html">Kubeflow</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/FrameworkControllerMode.html">基于 Kubernetes（AKS 等）的 FrameworkController</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/DLTSMode.html">DLWorkspace（又称 DLTS）</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/AdaptDLMode.html">AdaptDL（又称 ADL）</a></li>
</ul>
</ul>
</td>
</tr>
<tr align="center" valign="bottom">
</td>
</tr>
<tr valign="top">
<td valign="middle">
<b>参考</b>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/autotune_ref.html#trial">Python API</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/installation.html">支持的操作系统</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/Tuner/CustomizeTuner.html">自定义 Tuner</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Assessor/CustomizeAssessor.html">自定义 Assessor</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/Tutorial/InstallCustomizedAlgos.html">安装自定义的 Tuner，Assessor，Advisor</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/QuickStart.html#define-your-model-space">定义模型空间</a></li>
<li><a href="https://nni.readthedocs.io/zh/stable/NAS/ApiReference.html">NAS/Retiarii接口</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/Overview.html">支持训练平台</li>
<li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/HowToImplementTrainingService.html">实现训练平台</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
## **安装**
### **安装**
NNI 支持并在 Ubuntu >= 16.04, macOS >= 10.14.1, 和 Windows 10 >= 1809 通过了测试。 在 `python 64-bit >= 3.6` 的环境中，只需要运行 `pip install` 即可完成安装。
Linux 或 macOS
```bash
python3 -m pip install --upgrade nni
```
Windows
```bash
python -m pip install --upgrade nni
```
如果想试试最新代码，可参考从源代码[安装 NNI](https://nni.readthedocs.io/zh/latest/installation.html)。
Linux 和 macOS 下 NNI 系统需求[参考这里](https://nni.readthedocs.io/zh/latest/Tutorial/InstallationLinux.html#system-requirements) ，Windows [参考这里](https://nni.readthedocs.io/zh/latest/Tutorial/InstallationWin.html#system-requirements)。
注意：
* 如果遇到任何权限问题，可添加 `--user` 在用户目录中安装 NNI。
* 目前，Windows 上的 NNI 支持本机，远程和 OpenPAI 模式。 强烈推荐使用 Anaconda 或 Miniconda [在 Windows 上安装 NNI](https://nni.readthedocs.io/zh/stable/Tutorial/InstallationWin.html)。
* 如果遇到如 `Segmentation fault` 等错误参考[常见问题](docs/zh_CN/Tutorial/FAQ.rst)。 Windows 上的 FAQ 参考[在 Windows 上使用 NNI](docs/zh_CN/Tutorial/InstallationWin.rst#faq)。 Windows 上的 FAQ 参考[在 Windows 上使用 NNI](https://nni.readthedocs.io/zh/stable/Tutorial/InstallationWin.html#faq)。
### **验证安装**
* 通过克隆源代码下载示例。
```bash
git clone -b v2.6 https://github.com/Microsoft/nni.git
```
* 运行 MNIST 示例。
Linux 或 macOS
```bash
nnictl create --config nni/examples/trials/mnist-pytorch/config.yml
```
Windows
```powershell
nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml
```
* 在命令行中等待输出 `INFO: Successfully started experiment!`。 此消息表明 Experiment 已成功启动。 通过命令行输出的 `Web UI url` 来访问 Experiment 的界面。 此消息表明 Experiment 已成功启动。 通过命令行输出的 `Web UI url` 来访问 Experiment 的界面。
```text
INFO: Starting restful server...
INFO: Successfully started Restful server!
INFO: Setting local config...
INFO: Successfully set local config!
INFO: Starting experiment...
INFO: Successfully started experiment!
-----------------------------------------------------------------------
The experiment id is egchD4qy
The Web UI urls are: http://223.255.255.1:8080 http://127.0.0.1:8080
-----------------------------------------------------------------------
You can use these commands to get more information about the experiment
-----------------------------------------------------------------------
commands description
1. nnictl experiment show show the information of experiments
2. nnictl trial ls list all of trial jobs
3. nnictl top monitor the status of running experiments
4. nnictl log stderr show stderr log content
5. nnictl log stdout show stdout log content
6. nnictl stop stop an experiment
7. nnictl trial kill kill a trial job by id
8. nnictl --help get help information about nnictl
-----------------------------------------------------------------------
```
* 在浏览器中打开 `Web UI url`，可看到下图的 Experiment 详细信息，以及所有的 Trial 任务。 查看[这里](docs/zh_CN/Tutorial/WebUI.rst)的更多页面。 查看[这里](https://nni.readthedocs.io/zh/stable/Tutorial/WebUI.html)的更多页面。
<img src="docs/static/img/webui.gif" alt="webui" width="100%" />
## **发布和贡献**
NNI 有一个月度发布周期（主要发布）。 如果您遇到问题可以通过 [创建 issue](https://github.com/microsoft/nni/issues/new/choose) 来报告。
我们感谢所有的贡献。 我们感谢所有的贡献。 如果您计划提供任何 Bug 修复，请放手去做，不需要任何顾虑。
如果您计划提供新的功能、新的 Tuner 和 新的训练平台等， 请先创建一个新的 issue 或重用现有 issue，并与我们讨论该功能。 我们会及时与您讨论这个问题，如有需要会安排电话会议。
再次感谢所有的贡献者！
再次感谢所有的贡献者！
<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="docs/img/contributors.png" /></a>
## **反馈**
* [在 GitHub 上提交问题](https://github.com/microsoft/nni/issues/new/choose)。
* 在 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 中参与讨论。
* NNI 有一个月度发布周期（主要发布）。 如果您遇到问题可以通过 [创建 issue](https://github.com/microsoft/nni/issues/new/choose) 来报告。
加入聊天组：
| Gitter | | 微信 |
| -------------------------------------------------------------------------------------------------------------- | - | ----------------------------------------------------------------------- |
| ![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png) | 或 | ![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png) |
## 测试状态
### 必需
| 类型 | 状态 |
|:------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| Fast test | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/fast%20test?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master) |
| Full linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?repoName=microsoft%2Fnni&branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&repoName=microsoft%2Fnni&branchName=master) |
| Full windows | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20windows?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=63&branchName=master) |
### 训练平台
| 类型 | 状态 |
|:-------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| Remote - linux to linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master) |
| Remote - linux to windows | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20windows?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=67&branchName=master) |
| Remote - windows to linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20windows%20to%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=68&branchName=master) |
| OpenPAI | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20openpai%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master) |
| Frameworkcontroller | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20frameworkcontroller?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master) |
| Kubeflow | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20kubeflow?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master) |
| Hybrid | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20hybrid?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master) |
| AzureML | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20aml?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master) |
## 相关项目
针对开放性和推进最先进的技术，[微软研究院（MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/) 还发布了其他几个开源项目。
* [OpenPAI](https://github.com/Microsoft/pai)：作为开源平台，提供了完整的 AI 模型训练和资源管理能力，能轻松扩展，并支持各种规模的私有部署、云和混合环境。
* [FrameworkController](https://github.com/Microsoft/frameworkcontroller)：开源的通用 Kubernetes Pod 控制器，通过单个控制器来编排 Kubernetes 上所有类型的应用。
* [MMdnn](https://github.com/Microsoft/MMdnn)：一个完整、跨框架的解决方案，能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示 model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。 MMdnn 中的 "MM" 表示 model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。
* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) 是用于大规模向量的最近邻搜索场景的开源库。
我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。
## **许可协议**
代码库遵循 [MIT 许可协议](LICENSE)
SECURITY_zh_CN.md deleted 100644 → 0
View file @ 14d2966b
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->
## 安全
微软非常重视软件产品和服务的安全性，包括通过我们的 GitHub 组织管理的所有源代码库，其中涵盖 [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin)，和 [我们 GitHub 的组织](https://opensource.microsoft.com/)。
如果你在任何微软拥有的资源库中发现了安全漏洞，并且符合 [微软对安全漏洞的定义](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10))，请按照下文所述向我们报告。
## 报告安全问题
**请不要通过公开的 GitHub 问题报告安全漏洞。**
相反，请向微软安全响应中心（MSRC）报告，链接是 [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report)。
如果您希望在不登录的情况下提交，请发送电子邮件至 [secure@microsoft.com](mailto:secure@microsoft.com)。 如果可能的话，请用我们的 PGP 密钥对您的信息进行加密；请从以下网站下载该密钥 [微软安全响应中心 PGP 密钥页面](https://www.microsoft.com/en-us/msrc/pgp-key-msrc)。
你应该在24小时内收到回复。 如果由于某些原因你没有收到，请通过电子邮件跟进，以确保我们收到你的原始信息。 其他信息可以在以下网站找到 [microsoft.com/msrc](https://www.microsoft.com/msrc)。
请包括以下所要求的信息（尽可能多地提供），以帮助我们更好地了解可能的问题的性质和范围。
* 问题类型（如缓冲区溢出、SQL 注入、跨站脚本等）
* 与问题表现有关的源文件的完整路径
* 受影响的源代码位置（标签/分支/提交或 URL）
* 重现该问题所需的任何特殊配置
* 重现该问题的分步骤说明
* 概念证明或漏洞代码（如果可能的话）
* 该问题的影响，包括攻击者如何利用该问题
这些信息将帮助我们更快地对你的报告进行分流。
如果您需要报告错误赏金，更完整的报告可有助于获得更高的赏金奖励。 请访问我们的[微软漏洞赏金计划](https://microsoft.com/msrc/bounty)页面，以了解有关我们活动计划的更多详细信息。
## 首选语言
我们希望所有的交流都是用英语进行的。
## 政策
微软遵循[协调漏洞披露](https://www.microsoft.com/en-us/msrc/cvd)的原则。
<!-- END MICROSOFT SECURITY.MD BLOCK -->
dependencies/develop.txt
View file @ a911b856
......@@ -2,15 +2,20 @@ coverage
cython
flake8
ipython
jupyterlab
jupyter
jupyterlab == 3.0.9
nbsphinx
pylint
pyright
pytest
pytest-azurepipelines
pytest-cov
rstcheck
sphinx
sphinx >= 4.5
sphinx-argparse-nni >= 0.4.0
sphinx-copybutton
sphinx-gallery
sphinx-intl
sphinx-tabs
sphinxcontrib-bibtex
git+https://github.com/bashtage/sphinx-material.git
git+https://github.com/bashtage/sphinx-material@6e0ef82#egg=sphinx_material
dependencies/recommended.txt
View file @ a911b856
......@@ -7,8 +7,9 @@ torch == 1.10.0+cpu ; sys_platform != "darwin"
torch == 1.10.0 ; sys_platform == "darwin"
torchvision == 0.11.1+cpu ; sys_platform != "darwin"
torchvision == 0.11.1 ; sys_platform == "darwin"
pytorch-lightning >= 1.5.0
pytorch-lightning >= 1.5.0, < 1.6.0
torchmetrics
lightgbm
onnx
peewee
graphviz
......
dependencies/recommended_gpu.txt
View file @ a911b856
......@@ -4,7 +4,8 @@
tensorflow
torch == 1.10.0+cu111
torchvision == 0.11.1+cu111
pytorch-lightning >= 1.5.0
pytorch-lightning >= 1.5.0, < 1.6.0
lightgbm
onnx
peewee
graphviz
......
dependencies/recommended_legacy.txt
View file @ a911b856
......@@ -7,6 +7,7 @@ torchvision == 0.8.2+cpu
pytorch-lightning
torchmetrics
lightgbm
onnx
peewee
graphviz
......
dependencies/required.txt
View file @ a911b856
......@@ -6,6 +6,7 @@ hyperopt == 0.1.2
json_tricks >= 3.15.5
numpy < 1.22 ; python_version < "3.8"
numpy ; python_version >= "3.8"
packaging
pandas
prettytable
psutil
......@@ -18,5 +19,5 @@ scikit-learn >= 0.24.1
scipy < 1.8 ; python_version < "3.8"
scipy ; python_version >= "3.8"
typeguard
typing_extensions ; python_version < "3.8"
typing_extensions >= 4.0.0 ; python_version < "3.8"
websockets >= 10.1
docs/.gitignore
View file @ a911b856
......@@ -2,3 +2,12 @@ build/
# legacy build
_build/
# ignored copied rst in tutorials
/source/tutorials/**/cp_*.rst
# auto-generated reference table
_modules/
# Machine-style translation files
*.mo
docs/Makefile
View file @ a911b856
......@@ -11,6 +11,11 @@ BUILDDIR = build
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
# Build message catelogs for translation
i18n:
@$(SPHINXBUILD) -M getpartialtext "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
sphinx-intl update -p "$(BUILDDIR)/getpartialtext" -d "$(SOURCEDIR)/locales" -l zh
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
......
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