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{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<div class="rowHeight">
<div class="chinese"><a href="https://nni.readthedocs.io/zh/stable/">简体中文</a></div>
<b>NNI (Neural Network Intelligence)</b> is a lightweight but powerful toolkit to
help users <b>automate</b>
<a href="{{ pathto('FeatureEngineering/Overview') }}">Feature Engineering</a>,
<a href="{{ pathto('NAS/Overview') }}">Neural Architecture Search</a>,
<a href="{{ pathto('Tuner/BuiltinTuner') }}">Hyperparameter Tuning</a> and
<a href="{{ pathto('Compression/Overview') }}">Model Compression</a>.
</div>
<p class="gap rowHeight">
The tool manages automated machine learning (AutoML) experiments,
<b>dispatches and runs</b>
experiments' trial jobs generated by tuning algorithms to search the best neural
architecture and/or hyper-parameters in
<b>different training environments</b> like
<a href="{{ pathto('TrainingService/LocalMode') }}">Local Machine</a>,
<a href="{{ pathto('TrainingService/RemoteMachineMode') }}">Remote Servers</a>,
<a href="{{ pathto('TrainingService/PaiMode') }}">OpenPAI</a>,
<a href="{{ pathto('TrainingService/KubeflowMode') }}">Kubeflow</a>,
<a href="{{ pathto('TrainingService/FrameworkControllerMode') }}">FrameworkController on K8S (AKS etc.)</a>,
<a href="{{ pathto('TrainingService/DLTSMode') }}">DLWorkspace (aka. DLTS)</a>,
<a href="{{ pathto('TrainingService/AMLMode') }}">AML (Azure Machine Learning)</a>,
<a href="{{ pathto('TrainingService/AdaptDLMode') }}">AdaptDL (aka. ADL)</a>, other cloud options and even <a href="{{ pathto('TrainingService/HybridMode') }}">Hybrid mode</a>.
</p>
<!-- Who should consider using NNI -->
<div>
<h2 class="title">Who should consider using NNI</h2>
<ul>
<li>Those who want to <b>try different AutoML algorithms</b> in their training code/model.</li>
<li>Those who want to run AutoML trial jobs <b>in different environments</b> to speed up search.</li>
<li class="rowHeight">Researchers and data scientists who want to easily <b>implement and experiement new AutoML
algorithms</b>
, may it be: hyperparameter tuning algorithm,
neural architect search algorithm or model compression algorithm.
</li>
<li>ML Platform owners who want to <b>support AutoML in their platform</b></li>
</ul>
</div>
<!-- what's new -->
<div>
<div class="inline gap">
<h2>What's NEW! </h2>
<img width="48" src="_static/img/release_icon.png">
</div>
<hr class="whatNew"/>
<ul>
<li><b>New release:</b> <a href='https://github.com/microsoft/nni/releases/tag/v2.6'>{{ release }} is available2 <i>- released on Jan-18-2022</i></a></li>
<li><b>New demo available:</b> <a href="https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw">Youtube entry</a> | <a href="https://space.bilibili.com/1649051673">Bilibili</a> 入口 <i>- last updated on May-26-2021</i></li>
<li><b>New webinar:</b> <a href="https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-On-Demand.html">
Introducing Retiarii: A deep learning exploratory-training framework on NNI
</a> <i>- scheduled on June-24-2021</i>
</li>
<li><b>New community channel:</b> <a href="https://github.com/microsoft/nni/discussions">Discussions</a></li>
<li>
<div><b>New emoticons release:</b> <a href="{{ pathto('nnSpider') }}">nnSpider</a></div>
<img class="gap" src="_static/img/home.svg"></img>
</li>
</ul>
</div>
<!-- NNI capabilities in a glance -->
<div class="gap">
<h2 class="title">NNI capabilities in a glance</h2>
<p class="rowHeight">
NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements.
With the extensible API, you can customize your own AutoML algorithms and training services.
To make it easy for new users, NNI also provides a set of build-in stat-of-the-art
AutoML algorithms and out of box support for popular training platforms.
</p>
<p class="rowHeight">
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>
</div>
<p align="center">
<a href="#overview"><img src="_static/img/overview.svg" /></a>
</p>
<table class="list">
<tbody>
<tr align="center" valign="bottom" class="column">
<td></td>
<td class="framework">
<b>Frameworks & Libraries</b>
</td>
<td>
<b>Algorithms</b>
</td>
<td>
<b>Training Services</b>
</td>
</tr>
</tr>
<tr>
<td class="verticalMiddle"><b>Built-in</b></td>
<td>
<ul class="firstUl">
<li><b>Supported Frameworks</b></li>
<ul class="circle">
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="{{ pathto('SupportedFramework_Library') }}">More...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>Supported Libraries</b></li>
<ul class="circle">
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="{{ pathto('SupportedFramework_Library') }}">More...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>Examples</b></li>
<ul class="circle">
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv2">MNIST-tensorflow</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="{{ pathto('TrialExample/GbdtExample') }}">Auto-gbdt</a></li>
<li><a href="{{ pathto('TrialExample/Cifar10Examples') }}">Cifar10-pytorch</li></a>
<li><a href="{{ pathto('TrialExample/SklearnExamples') }}">Scikit-learn</a></li>
<li><a href="{{ pathto('TrialExample/EfficientNet') }}">EfficientNet</a></li>
<li><a href="{{ pathto('TrialExample/OpEvoExamples') }}">Kernel Tunning</li></a>
<a href="{{ pathto('SupportedFramework_Library') }}">More...</a><br />
</ul>
</ul>
</td>
<td align="left">
<a href="{{ pathto('Tuner/BuiltinTuner') }}">Hyperparameter Tuning</a>
<ul class="firstUl">
<div><b>Exhaustive search</b></div>
<ul class="circle">
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Random">Random Search</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#GridSearch">Grid Search</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Batch">Batch</a></li>
</ul>
<div><b>Heuristic search</b></div>
<ul class="circle">
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Evolution">Naïve Evolution</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Anneal">Anneal</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Hyperband">Hyperband</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#PBTTuner">PBT</a></li>
</ul>
<div><b>Bayesian optimization</b></div>
<ul class="circle">
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#BOHB">BOHB</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#TPE">TPE</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#SMAC">SMAC</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#MetisTuner">Metis Tuner</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#GPTuner">GP Tuner</a> </li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#DNGOTuner">DNGO Tuner</a></li>
</ul>
</ul>
<a href="{{ pathto('NAS/Overview') }}">Neural Architecture Search (Retiarii)</a>
<ul class="firstUl">
<ul class="circle">
<li><a href="{{ pathto('NAS/ENAS') }}">ENAS</a></li>
<li><a href="{{ pathto('NAS/DARTS') }}">DARTS</a></li>
<li><a href="{{ pathto('NAS/SPOS') }}">SPOS</a></li>
<li><a href="{{ pathto('NAS/Proxylessnas') }}">ProxylessNAS</a></li>
<li><a href="{{ pathto('NAS/FBNet') }}">FBNet</a></li>
<li><a href="{{ pathto('NAS/ExplorationStrategies') }}">Reinforcement Learning</a></li>
<li><a href="{{ pathto('NAS/ExplorationStrategies') }}">Regularized Evolution</a></li>
<li><a href="{{ pathto('NAS/Overview') }}">More...</a></li>
</ul>
</ul>
<a href="{{ pathto('Compression/Overview') }}">Model Compression</a>
<ul class="firstUl">
<div><b>Pruning</b></div>
<ul class="circle">
<li><a href="{{ pathto('Compression/Pruner') }}#agp-pruner">AGP Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#slim-pruner">Slim Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#admm-pruner">ADMM Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="{{ pathto('Compression/Overview') }}">More...</a></li>
</ul>
<div><b>Quantization</b></div>
<ul class="circle">
<li><a href="{{ pathto('Compression/Quantizer') }}#qat-quantize">QAT Quantizer</a></li>
<li><a href="{{ pathto('Compression/Quantizer') }}#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="{{ pathto('Compression/Quantizer') }}#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="{{ pathto('FeatureEngineering/Overview') }}">Feature Engineering (Beta)</a>
<ul class="circle">
<li><a href="{{ pathto('FeatureEngineering/GradientFeatureSelector') }}">GradientFeatureSelector</a></li>
<li><a href="{{ pathto('FeatureEngineering/GBDTSelector') }}">GBDTSelector</a></li>
</ul>
<a href="{{ pathto('Assessor/BuiltinAssessor') }}">Early Stop Algorithms</a>
<ul class="circle">
<li><a href="{{ pathto('Assessor/BuiltinAssessor') }}#MedianStop">Median Stop</a></li>
<li><a href="{{ pathto('Assessor/BuiltinAssessor') }}#Curvefitting">Curve Fitting</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('TrainingService/LocalMode') }}">Local Machine</a></li>
<li><a href="{{ pathto('TrainingService/RemoteMachineMode') }}">Remote Servers</a></li>
<li><a href="{{ pathto('TrainingService/HybridMode') }}">Hybrid mode</a></li>
<li><a href="{{ pathto('TrainingService/AMLMode') }}">AML(Azure Machine Learning)</a></li>
<li><b>Kubernetes based services</b></li>
<ul>
<li><a href="{{ pathto('TrainingService/PaiMode') }}">OpenPAI</a></li>
<li><a href="{{ pathto('TrainingService/KubeflowMode') }}">Kubeflow</a></li>
<li><a href="{{ pathto('TrainingService/FrameworkControllerMode') }}">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="{{ pathto('TrainingService/DLTSMode') }}">DLWorkspace (aka. DLTS)</a></li>
<li><a href="{{ pathto('TrainingService/AdaptDLMode') }}">AdaptDL (aka. ADL)</a></li>
</ul>
</ul>
</td>
</tr>
<tr valign="top">
<td class="verticalMiddle"><b>References</b></td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('Tutorial/HowToLaunchFromPython') }}">Python API</a></li>
<li><a href="{{ pathto('Tutorial/AnnotationSpec') }}">NNI Annotation</a></li>
<li><a href="{{ pathto('installation') }}">Supported OS</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('Tuner/CustomizeTuner') }}">CustomizeTuner</a></li>
<li><a href="{{ pathto('Assessor/CustomizeAssessor') }}">CustomizeAssessor</a></li>
<li><a href="{{ pathto('Tutorial/InstallCustomizedAlgos') }}">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
<li><a href="{{ pathto('NAS/QuickStart') }}">Define NAS Model Space</a></li>
<li><a href="{{ pathto('NAS/ApiReference') }}">NAS/Retiarii APIs</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('TrainingService/Overview') }}">Support TrainingService</a></li>
<li><a href="{{ pathto('TrainingService/HowToImplementTrainingService') }}">Implement TrainingService</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
<!-- Installation -->
<div class="gap">
<h2 class="title">Installation</h2>
<div>
<h3 class="second-title">Install</h3>
<div class="gap2">
NNI supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1,
and Windows 10 >= 1809. Simply run the following <code>pip install</code>
in an environment that has <code>python 64-bit >= 3.6</code>.
</div>
<div class="command-intro">Linux or macOS</div>
<div class="command">python3 -m pip install --upgrade nni</div>
<div class="command-intro">Windows</div>
<div class="command">python -m pip install --upgrade nni</div>
<div class="command-intro">If you want to try latest code, please <a href="{{ pathto('installation') }}">install
NNI</a> from source code.
</div>
<div class="chinese">For detail system requirements of NNI, please refer to <a href="{{ pathto('Tutorial/InstallationLinux') }}">here</a>
for Linux & macOS, and <a href="{{ pathto('Tutorial/InstallationWin') }}">here</a> for Windows.</div>
</div>
<div>
<p>Note:</p>
<ul>
<li>If there is any privilege issue, add --user to install NNI in the user directory.</li>
<li class="rowHeight">Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly
recommended to install <a href="{{ pathto('Tutorial/InstallationWin') }}">NNI on Windows</a>.</li>
<li>If there is any error like Segmentation fault, please refer to <a
href="{{ pathto('installation') }}">FAQ</a>. For FAQ on Windows, please refer
to <a href="{{ pathto('Tutorial/InstallationWin') }}">NNI on Windows</a>.</li>
</ul>
</div>
<div>
<h3 class="second-title gap">Verify installation</h3>
<div>
The following example is built on TensorFlow 1.x. Make sure <b>TensorFlow 1.x is used</b> when running
it.
</div>
<ul>
<li>
<div class="command-intro">Download the examples via clone the source code.</div>
<div class="command">git clone -b {{ release }} https://github.com/Microsoft/nni.git</div>
</li>
<li>
<div>Run the MNIST example.</div>
<div class="command-intro">Linux or macOS</div>
<div class="command">nnictl create --config nni/examples/trials/mnist-pytorch/config.yml</div>
<div class="command-intro">Windows</div>
<div class="command">nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml</div>
</li>
<li>
<div class="rowHeight">
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.
</div>
<!-- Indentation affects style！ -->
<pre class="code">
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
-----------------------------------------------------------------------
</pre>
</li>
<li class="rowHeight">
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. <a href="{{ pathto('Tutorial/WebUI') }}">Here</a> are more Web UI
pages.
<img class="gap" src="_static/img/webui.gif" width="100%"/>
</div>
</li>
</ul>
</div>
<!-- Releases and Contributing -->
<div class="gap">
<h2 class="title">Releases and Contributing</h2>
<div>NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by filling an issue.</div>
<br/>
<div>We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.</div>
<br/>
<div class="rowHeight">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.</div>
<br/>
<div>To learn more about making a contribution to NNI, please refer to our <a href="{{ pathto('contribution') }}"">How-to contribution page</a>.</div>
<br/>
<div>We appreciate all contributions and thank all the contributors!</div>
<img class="gap" src="_static/img/contributors.png"></img>
</div>
<!-- feedback -->
<div class="gap">
<h2 class="title">Feedback</h2>
<ul>
<li><a href="https://github.com/microsoft/nni/issues/new/choose">File an issue</a> on GitHub.</li>
<li>Open or participate in a <a href="https://github.com/microsoft/nni/discussions">discussion</a>.</li>
<li>Discuss on the <a href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">NNI Gitter</a> in NNI.</li>
</ul>
<div>
<div class="rowHeight">Join IM discussion groups:</div>
<table class="gap" border=1 style="border-collapse: collapse;">
<tbody>
<tr style="line-height: 30px;">
<th>Gitter</th>
<td></td>
<th>WeChat</th>
</tr>
<tr>
<td class="QR">
<img src="https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png" alt="Gitter" />
</td>
<td width="80" align="center" class="or">OR</td>
<td class="QR">
<img src="https://github.com/scarlett2018/nniutil/raw/master/wechat.png" alt="NNI Wechat" />
</td>
</tr>
</tbody>
</table>
</div>
</div>
<!-- Test status -->
<div class="gap">
<h2 class="title">Test status</h2>
<h3>Essentials</h3>
<table class="pipeline">
<tr>
<th>Type</th>
<th>Status</th>
</tr>
<tr>
<td>Fast test</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/fast%20test?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Full linux</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&repoName=microsoft%2Fnni&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?repoName=microsoft%2Fnni&branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Full windows</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=63&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20windows?branchName=master"/>
</a>
</td>
</tr>
</table>
<h3 class="gap">Training services</h3>
<table class="pipeline">
<tr>
<th>Type</th>
<th>Status</th>
</th>
<tr>
<td>Remote - linux to linux</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20linux?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Remote - linux to windows</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=67&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20windows?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Remote - windows to linux</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=68&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20windows%20to%20linux?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>OpenPAI</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20openpai%20-%20linux?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Frameworkcontroller</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20frameworkcontroller?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Kubeflow</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20kubeflow?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Hybrid</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20hybrid?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>AzureML</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20aml?branchName=master"/>
</a>
</td>
</tr>
</table>
</div>
<!-- Related Projects -->
<div class="gap">
<h2 class="title">Related Projects</h2>
<p class="rowHeight">
Targeting at openness and advancing state-of-art technology,
<a href="https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/">Microsoft Research (MSR)</a>
had also released few
other open source projects.</p>
<ul id="relatedProject">
<li class="rowHeight">
<a href="https://github.com/Microsoft/pai">OpenPAI</a> : 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.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/frameworkcontroller">FrameworkController</a> : an open source
general-purpose Kubernetes Pod Controller that orchestrate
all kinds of applications on Kubernetes by a single controller.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/MMdnn">MMdnn</a> : 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.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/SPTAG">SPTAG</a> : Space Partition Tree And Graph (SPTAG) is an open
source library
for large scale vector approximate nearest neighbor search scenario.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/SPTAG">nn-Meter</a> : An accurate inference latency predictor for DNN models on diverse edge devices.
</li>
</ul>
<p>We encourage researchers and students leverage these projects to accelerate the AI development and research.</p>
</div>
<!-- License -->
<div>
<h2 class="title">License</h2>
<p>The entire codebase is under <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT license</a></p>
</div>
</div>
{% endblock %}

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@ -1,30 +0,0 @@
{% extends "!layout.html" %}
{% block sidebartitle %}
{% if logo and theme_logo_only %}
<a href="{{ pathto('index') }}">
{% else %}
<a href="{{ pathto('index') }}" class="icon icon-home"> {{ project }}
{% endif %}
{% if logo %}
{# Not strictly valid HTML, but it's the only way to display/scale it properly, without weird scripting or heaps of work #}
<img src="{{ pathto('_static/' + logo, 1) }}" class="logo" />
{% endif %}
</a>
{% if theme_display_version %}
{%- set nav_version = version %}
{% if READTHEDOCS and current_version %}
{%- set nav_version = current_version %}
{% endif %}
{% if nav_version %}
<div class="version">
{{ nav_version }}
</div>
{% endif %}
{% endif %}
{% include "searchbox.html" %}
{% endblock %}

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@ -1,92 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2 class="center">nnSpider emoticons</h2>
<ul class="emotion">
<li class="first">
<div>
<a href="{{ pathto('nnSpider/nobug') }}">
<img src="_static/img/NoBug.png" alt="NoBug" />
</a>
</div>
<p class="center">NoBug</p>
</li>
<li class="first">
<div>
<a href="{{ pathto('nnSpider/holiday') }}">
<img src="_static/img/Holiday.png" alt="Holiday" />
</a>
</div>
<p class="center">Holiday</p>
</li>
<li class="first">
<div>
<a href="{{ pathto('nnSpider/errorEmotion') }}">
<img src="_static/img/Error.png" alt="Error" />
</a>
</div>
<p class="center">Error</p>
</li>
<li class="second">
<div>
<a href="{{ pathto('nnSpider/working') }}">
<img class="working" src="_static/img/Working.png" alt="Working" />
</a>
</div>
<p class="center">Working</p>
</li>
<li class="second">
<div>
<a href="{{ pathto('nnSpider/sign') }}">
<img class="sign" src="_static/img/Sign.png" alt="Sign" />
</a>
</div>
<p class="center">Sign</p>
</li>
<li class="second">
<div>
<a href="{{ pathto('nnSpider/crying') }}">
<img class="crying" src="_static/img/Crying.png" alt="Crying" />
</a>
</div>
<p class="center">Crying</p>
</li>
<li class="three">
<div>
<a href="{{ pathto('nnSpider/cut') }}">
<img src="_static/img/Cut.png" alt="Crying" />
</a>
</div>
<p class="center">Cut</p>
</li>
<li class="three">
<div>
<a href="{{ pathto('nnSpider/weaving') }}">
<img class="weaving" src="_static/img/Weaving.png" alt="Weaving" />
</a>
</div>
<p class="center">weaving</p>
</li>
<li class="three">
<div class="comfort">
<a href="{{ pathto('nnSpider/comfort') }}">
<img src="_static/img/Comfort.png" alt="Weaving" />
</a>
</div>
<p class="center">comfort</p>
</li>
<li class="four">
<div>
<a href="{{ pathto('nnSpider/sweat') }}">
<img src="_static/img/Sweat.png" alt="Sweat" />
</a>
</div>
<p class="center">Sweat</p>
</li>
<div class="clear"></div>
</ul>
{% endblock %}

12
docs/en_US/_templates/nnSpider/comfort.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Comfort</h2>
<div class="details-container">
<img src="../_static/img/Comfort.png" alt="Comfort" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/crying.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Crying</h2>
<div class="details-container">
<img src="../_static/img/Crying.png" alt="Crying" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/cut.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Cut</h2>
<div class="details-container">
<img src="../_static/img/Cut.png" alt="Cut" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/errorEmotion.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Error</h2>
<div class="details-container">
<img src="../_static/img/Error.png" alt="Error" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/holiday.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Holiday</h2>
<div class="details-container">
<img src="../_static/img/Holiday.png" alt="NoBug" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/nobug.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>NoBug</h2>
<div class="details-container">
<img src="../_static/img/NoBug.png" alt="NoBug" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/sign.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Sign</h2>
<div class="details-container">
<img src="../_static/img/Sign.png" alt="Sign" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/sweat.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Sweat</h2>
<div class="details-container">
<img src="../_static/img/Sweat.png" alt="Sweat" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/weaving.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Weaving</h2>
<div class="details-container">
<img src="../_static/img/Weaving.png" alt="Weaving" />
</div>
{% endblock %}

12
docs/en_US/_templates/nnSpider/working.html
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@ -1,12 +0,0 @@
{% extends "!layout.html" %}
{% set title = "Welcome To Neural Network Intelligence !!!"%}
{% block document %}
<h2>Working</h2>
<div class="details-container">
<img src="../_static/img/Working.png" alt="Working" />
</div>
{% endblock %}

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docs/en_US/conf.py
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@ -20,13 +20,15 @@ sys.path.insert(0, os.path.abspath('../..'))
# -- Project information ---------------------------------------------------
from datetime import datetime
project = 'NNI'
copyright = '2021, Microsoft'
copyright = f'{datetime.now().year}, Microsoft'
author = 'Microsoft'
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
# FIXME: this should be written somewhere globally
release = 'v2.6'
# -- General configuration ---------------------------------------------------
@ -54,14 +56,14 @@ extensions = [
autodoc_mock_imports = ['apex', 'nni_node', 'tensorrt', 'pycuda', 'nn_meter']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
templates_path = ['../templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
source_suffix = ['.rst']
# The master toctree document.
master_doc = 'contents'
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@ -78,38 +80,64 @@ exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'Release_v1.0.md', '**.i
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = None
html_additional_pages = {
'index': 'index.html',
'nnSpider': 'nnSpider.html',
'nnSpider/nobug': 'nnSpider/nobug.html',
'nnSpider/holiday': 'nnSpider/holiday.html',
'nnSpider/errorEmotion': 'nnSpider/errorEmotion.html',
'nnSpider/working': 'nnSpider/working.html',
'nnSpider/sign': 'nnSpider/sign.html',
'nnSpider/crying': 'nnSpider/crying.html',
'nnSpider/cut': 'nnSpider/cut.html',
'nnSpider/weaving': 'nnSpider/weaving.html',
'nnSpider/comfort': 'nnSpider/comfort.html',
'nnSpider/sweat': 'nnSpider/sweat.html'
}
# -- Options for HTML output -------------------------------------------------
# HTML logo
html_logo = '../img/nni_icon.svg'
# HTML favicon
html_favicon = '../img/favicon.ico'
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
html_theme = 'sphinx_material'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
html_theme_options = {
'logo_only': True,
# Set the name of the project to appear in the navigation.
'nav_title': 'Neural Network Intelligence',
# Set you GA account ID to enable tracking
'google_analytics_account': 'UA-136029994-1',
# Specify a base_url used to generate sitemap.xml. If not
# specified, then no sitemap will be built.
'base_url': 'https://nni.readthedocs.io/',
# Set the color and the accent color
# We can't have our customized themes currently
# Remember to update static/css/material_custom.css when this is updated.
'color_primary': 'indigo',
'color_accent': 'pink',
# Set the repo location to get a badge with stats
'repo_url': 'https://github.com/microsoft/nni/',
'repo_name': 'nni',
# Visible levels of the global TOC; -1 means unlimited
'globaltoc_depth': 3,
'version_dropdown': True,
# This is a placeholder, which should be replaced later.
'version_info': {
'current': '/'
},
# Text to appear at the top of the home page in a "hero" div.
'heroes': {
# We can have heroes for the home pages of HPO, NAS, Compression in future.
'index': 'An open source AutoML toolkit for neural architecture search, model compression and hyper-parameter tuning.'
}
}
# Disable show source link.
html_show_sourcelink = False
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
@ -123,11 +151,22 @@ html_static_path = ['../static']
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
html_sidebars = {
"**": ["logo-text.html", "globaltoc.html", "localtoc.html", "searchbox.html"]
}
html_logo = '../img/nni_logo_dark.png'
html_title = 'An open source AutoML toolkit for neural architecture search, model compression and hyper-parameter tuning (%s %s)' % \
(project, release)
html_title = 'Neural Network Intelligence'
# Add extra css files and js files
html_css_files = [
'css/material_custom.css',
'css/material_dropdown.css',
'css/index_page.css',
]
html_js_files = [
'js/version.js',
'js/github.js',
]
# -- Options for HTMLHelp output ---------------------------------------------
@ -210,7 +249,3 @@ git_commit_id = subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode().s
extlinks = {
'githublink': ('https://github.com/microsoft/nni/blob/' + git_commit_id + '/%s', 'Github link: ')
}
# -- Extension configuration -------------------------------------------------
def setup(app):
app.add_css_file('css/custom.css')

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@ -1,23 +0,0 @@
###########################
Neural Network Intelligence
###########################
.. toctree::
:caption: Table of Contents
:maxdepth: 2
:titlesonly:
Overview
Installation <installation>
QuickStart <Tutorial/QuickStart>
Auto (Hyper-parameter) Tuning <hyperparameter_tune>
Neural Architecture Search <nas>
Model Compression <model_compression>
Feature Engineering <feature_engineering>
References <reference>
Use Cases and Solutions <CommunitySharings/community_sharings>
Research and Publications <ResearchPublications>
FAQ <Tutorial/FAQ>
How to Contribute <contribution>
Change Log <Release>

565
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@ -0,0 +1,565 @@
.. modified from index.html
.. replace \{\{ pathto\('(.*)'\) \}\} -> $1.html
###########################
Neural Network Intelligence
###########################
.. toctree::
:maxdepth: 2
:titlesonly:
:hidden:
Overview
Installation <installation>
QuickStart <Tutorial/QuickStart>
Auto (Hyper-parameter) Tuning <hyperparameter_tune>
Neural Architecture Search <nas>
Model Compression <model_compression>
Feature Engineering <feature_engineering>
References <reference>
Use Cases and Solutions <CommunitySharings/community_sharings>
Research and Publications <ResearchPublications>
FAQ <Tutorial/FAQ>
How to Contribute <contribution>
Change Log <Release>
.. raw:: html
<div class="rowHeight">
<div class="chinese"><a href="https://nni.readthedocs.io/zh/stable/">简体中文</a></div>
<b>NNI (Neural Network Intelligence)</b> is a lightweight but powerful toolkit to
help users <b>automate</b>
<a href="FeatureEngineering/Overview.html">Feature Engineering</a>,
<a href="NAS/Overview.html">Neural Architecture Search</a>,
<a href="Tuner/BuiltinTuner.html">Hyperparameter Tuning</a> and
<a href="Compression/Overview.html">Model Compression</a>.
</div>
<p class="gap rowHeight">
The tool manages automated machine learning (AutoML) experiments,
<b>dispatches and runs</b>
experiments' trial jobs generated by tuning algorithms to search the best neural
architecture and/or hyper-parameters in
<b>different training environments</b> like
<a href="TrainingService/LocalMode.html">Local Machine</a>,
<a href="TrainingService/RemoteMachineMode.html">Remote Servers</a>,
<a href="TrainingService/PaiMode.html">OpenPAI</a>,
<a href="TrainingService/KubeflowMode.html">Kubeflow</a>,
<a href="TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a>,
<a href="TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a>,
<a href="TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>,
<a href="TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a>, other cloud options and even <a href="TrainingService/HybridMode.html">Hybrid mode</a>.
</p>
<!-- Who should consider using NNI -->
<div>
<h2 class="title">Who should consider using NNI</h2>
<ul>
<li>Those who want to <b>try different AutoML algorithms</b> in their training code/model.</li>
<li>Those who want to run AutoML trial jobs <b>in different environments</b> to speed up search.</li>
<li class="rowHeight">Researchers and data scientists who want to easily <b>implement and experiement new AutoML
algorithms</b>
, may it be: hyperparameter tuning algorithm,
neural architect search algorithm or model compression algorithm.
</li>
<li>ML Platform owners who want to <b>support AutoML in their platform</b></li>
</ul>
</div>
<!-- what's new -->
<div>
<div class="inline gap">
<h2>What's NEW! </h2>
<img width="48" src="_static/img/release_icon.png">
</div>
<hr class="whatNew"/>
<ul>
<li><b>New release:</b> <a href='https://github.com/microsoft/nni/releases/tag/v2.6'>v2.6 is available. <i>- released on Jan-18-2022</i></a></li>
<li><b>New demo available:</b> <a href="https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw">Youtube entry</a> | <a href="https://space.bilibili.com/1649051673">Bilibili</a> 入口 <i>- last updated on May-26-2021</i></li>
<li><b>New webinar:</b> <a href="https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-On-Demand.html">
Introducing Retiarii: A deep learning exploratory-training framework on NNI
</a> <i>- scheduled on June-24-2021</i>
</li>
<li><b>New community channel:</b> <a href="https://github.com/microsoft/nni/discussions">Discussions</a></li>
<li>
<div><b>New emoticons release:</b> <a href="nnSpider.html">nnSpider</a></div>
<img class="gap" src="_static/img/home.svg"></img>
</li>
</ul>
</div>
<!-- NNI capabilities in a glance -->
<div class="gap">
<h2 class="title">NNI capabilities in a glance</h2>
<p class="rowHeight">
NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements.
With the extensible API, you can customize your own AutoML algorithms and training services.
To make it easy for new users, NNI also provides a set of build-in stat-of-the-art
AutoML algorithms and out of box support for popular training platforms.
</p>
<p class="rowHeight">
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>
</div>
<p align="center">
<a href="#overview"><img src="_static/img/overview.svg" /></a>
</p>
<table class="main-table">
<tbody>
<tr align="center" valign="bottom" class="column">
<td></td>
<td class="framework">
<b>Frameworks & Libraries</b>
</td>
<td>
<b>Algorithms</b>
</td>
<td>
<b>Training Services</b>
</td>
</tr>
</tr>
<tr>
<td class="verticalMiddle"><b>Built-in</b></td>
<td>
<ul class="firstUl">
<li><b>Supported Frameworks</b></li>
<ul class="circle">
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="SupportedFramework_Library.html">More...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>Supported Libraries</b></li>
<ul class="circle">
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="SupportedFramework_Library.html">More...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>Examples</b></li>
<ul class="circle">
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv2">MNIST-tensorflow</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="TrialExample/GbdtExample.html">Auto-gbdt</a></li>
<li><a href="TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
<li><a href="TrialExample/SklearnExamples.html">Scikit-learn</a></li>
<li><a href="TrialExample/EfficientNet.html">EfficientNet</a></li>
<li><a href="TrialExample/OpEvoExamples.html">Kernel Tunning</li></a>
<a href="SupportedFramework_Library.html">More...</a><br />
</ul>
</ul>
</td>
<td align="left">
<a href="Tuner/BuiltinTuner.html">Hyperparameter Tuning</a>
<ul class="firstUl">
<div><b>Exhaustive search</b></div>
<ul class="circle">
<li><a href="Tuner/BuiltinTuner.html#Random">Random Search</a></li>
<li><a href="Tuner/BuiltinTuner.html#GridSearch">Grid Search</a></li>
<li><a href="Tuner/BuiltinTuner.html#Batch">Batch</a></li>
</ul>
<div><b>Heuristic search</b></div>
<ul class="circle">
<li><a href="Tuner/BuiltinTuner.html#Evolution">Naïve Evolution</a></li>
<li><a href="Tuner/BuiltinTuner.html#Anneal">Anneal</a></li>
<li><a href="Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
<li><a href="Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
</ul>
<div><b>Bayesian optimization</b></div>
<ul class="circle">
<li><a href="Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
<li><a href="Tuner/BuiltinTuner.html#TPE">TPE</a></li>
<li><a href="Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
<li><a href="Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
<li><a href="Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a> </li>
<li><a href="Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
</ul>
</ul>
<a href="NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
<ul class="firstUl">
<ul class="circle">
<li><a href="NAS/ENAS.html">ENAS</a></li>
<li><a href="NAS/DARTS.html">DARTS</a></li>
<li><a href="NAS/SPOS.html">SPOS</a></li>
<li><a href="NAS/Proxylessnas.html">ProxylessNAS</a></li>
<li><a href="NAS/FBNet.html">FBNet</a></li>
<li><a href="NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
<li><a href="NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
<li><a href="NAS/Overview.html">More...</a></li>
</ul>
</ul>
<a href="Compression/Overview.html">Model Compression</a>
<ul class="firstUl">
<div><b>Pruning</b></div>
<ul class="circle">
<li><a href="Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
<li><a href="Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
<li><a href="Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
<li><a href="Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="Compression/Overview.html">More...</a></li>
</ul>
<div><b>Quantization</b></div>
<ul class="circle">
<li><a href="Compression/Quantizer.html#qat-quantize">QAT Quantizer</a></li>
<li><a href="Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
<ul class="circle">
<li><a href="FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
<li><a href="FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
</ul>
<a href="Assessor/BuiltinAssessor.html">Early Stop Algorithms</a>
<ul class="circle">
<li><a href="Assessor/BuiltinAssessor.html#MedianStop">Median Stop</a></li>
<li><a href="Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="TrainingService/LocalMode.html">Local Machine</a></li>
<li><a href="TrainingService/RemoteMachineMode.html">Remote Servers</a></li>
<li><a href="TrainingService/HybridMode.html">Hybrid mode</a></li>
<li><a href="TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
<li><b>Kubernetes based services</b></li>
<ul>
<li><a href="TrainingService/PaiMode.html">OpenPAI</a></li>
<li><a href="TrainingService/KubeflowMode.html">Kubeflow</a></li>
<li><a href="TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a></li>
<li><a href="TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a></li>
</ul>
</ul>
</td>
</tr>
<tr valign="top">
<td class="verticalMiddle"><b>References</b></td>
<td>
<ul class="firstUl">
<li><a href="Tutorial/HowToLaunchFromPython.html">Python API</a></li>
<li><a href="Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
<li><a href="installation.html">Supported OS</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
<li><a href="Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
<li><a href="Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
<li><a href="NAS/QuickStart.html">Define NAS Model Space</a></li>
<li><a href="NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="TrainingService/Overview.html">Support TrainingService</a></li>
<li><a href="TrainingService/HowToImplementTrainingService.html">Implement TrainingService</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
<!-- Installation -->
<div class="gap">
<h2 class="title">Installation</h2>
<div>
<h3 class="second-title">Install</h3>
<div class="gap2">
NNI supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1,
and Windows 10 >= 1809. Simply run the following <code>pip install</code>
in an environment that has <code>python 64-bit >= 3.6</code>.
</div>
<div class="command-intro">Linux or macOS</div>
<div class="command">python3 -m pip install --upgrade nni</div>
<div class="command-intro">Windows</div>
<div class="command">python -m pip install --upgrade nni</div>
<div class="command-intro">If you want to try latest code, please <a href="installation.html">install
NNI</a> from source code.
</div>
<div class="chinese">For detail system requirements of NNI, please refer to <a href="Tutorial/InstallationLinux.html">here</a>
for Linux & macOS, and <a href="Tutorial/InstallationWin.html">here</a> for Windows.</div>
</div>
<div>
<p>Note:</p>
<ul>
<li>If there is any privilege issue, add --user to install NNI in the user directory.</li>
<li class="rowHeight">Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly
recommended to install <a href="Tutorial/InstallationWin.html">NNI on Windows</a>.</li>
<li>If there is any error like Segmentation fault, please refer to <a
href="installation.html">FAQ</a>. For FAQ on Windows, please refer
to <a href="Tutorial/InstallationWin.html">NNI on Windows</a>.</li>
</ul>
</div>
<div>
<h3 class="second-title gap">Verify installation</h3>
<div>
The following example is built on TensorFlow 1.x. Make sure <b>TensorFlow 1.x is used</b> when running
it.
</div>
<ul>
<li>
<div class="command-intro">Download the examples via clone the source code.</div>
<div class="command">git clone -b v2.6 https://github.com/Microsoft/nni.git</div>
</li>
<li>
<div>Run the MNIST example.</div>
<div class="command-intro">Linux or macOS</div>
<div class="command">nnictl create --config nni/examples/trials/mnist-pytorch/config.yml</div>
<div class="command-intro">Windows</div>
<div class="command">nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml</div>
</li>
<li>
<div class="rowHeight">
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.
</div>
<!-- Indentation affects style！ -->
<pre class="main-code">
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
-----------------------------------------------------------------------
</pre>
</li>
<li class="rowHeight">
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. <a href="Tutorial/WebUI.html">Here</a> are more Web UI
pages.
<img class="gap" src="_static/img/webui.gif" width="100%"/>
</div>
</li>
</ul>
</div>
<!-- Releases and Contributing -->
<div class="gap">
<h2 class="title">Releases and Contributing</h2>
<div>NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by filling an issue.</div>
<br/>
<div>We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.</div>
<br/>
<div class="rowHeight">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.</div>
<br/>
<div>To learn more about making a contribution to NNI, please refer to our <a href="contribution.html"">How-to contribution page</a>.</div>
<br/>
<div>We appreciate all contributions and thank all the contributors!</div>
<img class="gap" src="_static/img/contributors.png"></img>
</div>
<!-- feedback -->
<div class="gap">
<h2 class="title">Feedback</h2>
<ul>
<li><a href="https://github.com/microsoft/nni/issues/new/choose">File an issue</a> on GitHub.</li>
<li>Open or participate in a <a href="https://github.com/microsoft/nni/discussions">discussion</a>.</li>
<li>Discuss on the <a href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">NNI Gitter</a> in NNI.</li>
</ul>
<div>
<div class="rowHeight">Join IM discussion groups:</div>
<table class="gap" border=1 style="border-collapse: collapse;">
<tbody>
<tr style="line-height: 30px;">
<th>Gitter</th>
<td></td>
<th>WeChat</th>
</tr>
<tr>
<td class="QR">
<img src="https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png" alt="Gitter" />
</td>
<td width="80" align="center" class="or">OR</td>
<td class="QR">
<img src="https://github.com/scarlett2018/nniutil/raw/master/wechat.png" alt="NNI Wechat" />
</td>
</tr>
</tbody>
</table>
</div>
</div>
<!-- Test status -->
<div class="gap">
<h2 class="title">Test status</h2>
<h3>Essentials</h3>
<table class="pipeline">
<tr>
<th>Type</th>
<th>Status</th>
</tr>
<tr>
<td>Fast test</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/fast%20test?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Full linux</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&repoName=microsoft%2Fnni&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?repoName=microsoft%2Fnni&branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Full windows</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=63&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20windows?branchName=master"/>
</a>
</td>
</tr>
</table>
<h3 class="gap">Training services</h3>
<table class="pipeline">
<tr>
<th>Type</th>
<th>Status</th>
</th>
<tr>
<td>Remote - linux to linux</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20linux?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Remote - linux to windows</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=67&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20windows?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Remote - windows to linux</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=68&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20windows%20to%20linux?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>OpenPAI</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20openpai%20-%20linux?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Frameworkcontroller</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20frameworkcontroller?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Kubeflow</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20kubeflow?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>Hybrid</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20hybrid?branchName=master"/>
</a>
</td>
</tr>
<tr>
<td>AzureML</td>
<td>
<a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master">
<img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20aml?branchName=master"/>
</a>
</td>
</tr>
</table>
</div>
<!-- Related Projects -->
<div class="gap">
<h2 class="title">Related Projects</h2>
<p class="rowHeight">
Targeting at openness and advancing state-of-art technology,
<a href="https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/">Microsoft Research (MSR)</a>
had also released few
other open source projects.</p>
<ul id="relatedProject">
<li class="rowHeight">
<a href="https://github.com/Microsoft/pai">OpenPAI</a> : 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.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/frameworkcontroller">FrameworkController</a> : an open source
general-purpose Kubernetes Pod Controller that orchestrate
all kinds of applications on Kubernetes by a single controller.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/MMdnn">MMdnn</a> : 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.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/SPTAG">SPTAG</a> : Space Partition Tree And Graph (SPTAG) is an open
source library
for large scale vector approximate nearest neighbor search scenario.
</li>
<li class="rowHeight">
<a href="https://github.com/Microsoft/SPTAG">nn-Meter</a> : An accurate inference latency predictor for DNN models on diverse edge devices.
</li>
</ul>
<p>We encourage researchers and students leverage these projects to accelerate the AI development and research.</p>
</div>
<!-- License -->
<div>
<h2 class="title">License</h2>
<p>The entire codebase is under <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT license</a></p>
</div>
</div>

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<h2 class="center">nnSpider emoticons</h2>
<ul class="emotion">
<li class="first">
<div>
<a href="{{ pathto('nnSpider/nobug') }}">
<img src="_static/img/NoBug.png" alt="NoBug" />
</a>
</div>
<p class="center">NoBug</p>
</li>
<li class="first">
<div>
<a href="{{ pathto('nnSpider/holiday') }}">
<img src="_static/img/Holiday.png" alt="Holiday" />
</a>
</div>
<p class="center">Holiday</p>
</li>
<li class="first">
<div>
<a href="{{ pathto('nnSpider/errorEmotion') }}">
<img src="_static/img/Error.png" alt="Error" />
</a>
</div>
<p class="center">Error</p>
</li>
<li class="second">
<div>
<a href="{{ pathto('nnSpider/working') }}">
<img class="working" src="_static/img/Working.png" alt="Working" />
</a>
</div>
<p class="center">Working</p>
</li>
<li class="second">
<div>
<a href="{{ pathto('nnSpider/sign') }}">
<img class="sign" src="_static/img/Sign.png" alt="Sign" />
</a>
</div>
<p class="center">Sign</p>
</li>
<li class="second">
<div>
<a href="{{ pathto('nnSpider/crying') }}">
<img class="crying" src="_static/img/Crying.png" alt="Crying" />
</a>
</div>
<p class="center">Crying</p>
</li>
<li class="three">
<div>
<a href="{{ pathto('nnSpider/cut') }}">
<img src="_static/img/Cut.png" alt="Crying" />
</a>
</div>
<p class="center">Cut</p>
</li>
<li class="three">
<div>
<a href="{{ pathto('nnSpider/weaving') }}">
<img class="weaving" src="_static/img/Weaving.png" alt="Weaving" />
</a>
</div>
<p class="center">weaving</p>
</li>
<li class="three">
<div class="comfort">
<a href="{{ pathto('nnSpider/comfort') }}">
<img src="_static/img/Comfort.png" alt="Weaving" />
</a>
</div>
<p class="center">comfort</p>
</li>
<li class="four">
<div>
<a href="{{ pathto('nnSpider/sweat') }}">
<img src="_static/img/Sweat.png" alt="Sweat" />
</a>
</div>
<p class="center">Sweat</p>
</li>
<div class="clear"></div>
</ul>

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<h2>Comfort</h2>
<div class="details-container">
<img src="../_static/img/Comfort.png" alt="Comfort" />
</div>

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:orphan:
.. raw:: html
<h2>Crying</h2>
<div class="details-container">
<img src="../_static/img/Crying.png" alt="Crying" />
</div>

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.. raw:: html
<h2>Cut</h2>
<div class="details-container">
<img src="../_static/img/Cut.png" alt="Cut" />
</div>

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<h2>Error</h2>
<div class="details-container">
<img src="../_static/img/Error.png" alt="Error" />
</div>

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.. raw:: html
<h2>Holiday</h2>
<div class="details-container">
<img src="../_static/img/Holiday.png" alt="NoBug" />
</div>

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.. raw:: html
<h2>NoBug</h2>
<div class="details-container">
<img src="../_static/img/NoBug.png" alt="NoBug" />
</div>

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.. raw:: html
<h2>Sign</h2>
<div class="details-container">
<img src="../_static/img/Sign.png" alt="Sign" />
</div>

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.. raw:: html
<h2>Sweat</h2>
<div class="details-container">
<img src="../_static/img/Sweat.png" alt="Sweat" />
</div>

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.. raw:: html
<h2>Weaving</h2>
<div class="details-container">
<img src="../_static/img/Weaving.png" alt="Weaving" />
</div>

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<h2>Working</h2>
<div class="details-container">
<img src="../_static/img/Working.png" alt="Working" />
</div>

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pytorch-lightning
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}
.rowHeight{
.rowHeight {
line-height: 24px;
}
@ -19,9 +21,10 @@
border-bottom: 1px solid #ccc;
}
.second-title{
.second-title {
margin-top: 24px;
}
/* command style */
.command {
background-color: #f8f8f8;
@ -32,73 +35,73 @@
padding-left: 10px;
}
.command-intro {
line-height: 48px;
}
.code{
.main-code {
background-color: #f8f8f8;
padding: 15px 20px;
}
.command-intro {
line-height: 48px;
}
/* document body width */
.wy-nav-content{
.wy-nav-content {
max-width: 100% !important;
background-color: #fff;
}
/* nni logo bgcolor */
.wy-side-nav-search{
.wy-side-nav-search {
background-color: black;
}
/* document body font-family */
.wy-body-for-nav, h1, h2, h3, h4, h5, h6, div, p, ul, li, a{
font-family: "Lato", segoe ui, "proxima-nova","Helvetica Neue",Arial,sans-serif;
}
.wy-nav-content ul li{
.wy-nav-content ul li {
list-style: disc;
margin-bottom: 12px;
margin-left: 24px;
}
.list, .list tr, .list td{
.main-table, .main-table tr, .main-table td {
border: 1px solid #ccc;
}
.column b{
.column b {
padding-bottom: 4px;
border-bottom: 2px solid blue;
}
.column td{
.column td {
width: 200px;
text-align: center;
line-height: 36px;
}
td.framework{
width:300px;
td.framework {
width: 300px;
-webkit-width: 220px;
}
.list .circle li{
.main-table .circle li {
list-style-type: circle;
}
.list .firstUl, .circle{
.main-table .firstUl, .circle {
padding-left: 20px;
}
.list .verticalMiddle{
.main-table .verticalMiddle {
vertical-align: middle !important;
text-align: center;
}
.inline img, .inline h2{
.inline img, .inline h2 {
display: inline-block;
margin-bottom: 0px;
}
inline hr{
.inline hr {
margin-top: 0px;
}
@ -106,28 +109,28 @@ inline hr{
margin-top: -20px;
}
.ui-img img{
.ui-img img {
height: 350px;
}
.wy-side-nav-search>div.version{
color: #fff !important;
.wy-side-nav-search>div.version {
color: #fff !important;
}
.list{
.main-table {
width: 90%;
margin: 0 auto;
}
.chinese{
.chinese {
margin-bottom: 16px;
}
.QR{
.QR {
width: 180px;
}
.or{
.or {
vertical-align: middle;
}
@ -146,23 +149,23 @@ inline hr{
list-style: none;
}
.emotion li div{
.emotion li div {
transition: 0.2s;
text-align: center;
vertical-align: middle;
}
.emotion li div:hover{
.emotion li div:hover {
transform: translate(1.1);
box-shadow: 0 15px 30px rgb(0 0 0 / 10%);
}
.center{
.center {
line-height: 54px;
text-align: center;
}
.emotion img{
.emotion img {
width: 250px;
}
@ -170,47 +173,47 @@ inline hr{
margin: 50px 24px;
}
.emotion .second .working{
.emotion .second .working {
margin: 67px 24px;
}
.emotion .second .sign{
.emotion .second .sign {
margin: 77px 24px;
}
.emotion .second .crying{
.emotion .second .crying {
margin: 80px 24px;
}
.emotion .three img{
.emotion .three img {
margin: 66px 24px;
}
.emotion .three .weaving{
.emotion .three .weaving {
margin: 75px 24px;
}
.three .comfort img{
.three .comfort img {
margin: 92px 24px;
}
.emotion .four img{
.emotion .four img {
margin: 81px 24px;
}
.details-container{
.details-container {
text-align: center;
}
.clear{
.clear {
clear: both;
}
.whatNew{
.whatNew {
margin-top: 6px;
}
.pipeline tr, .pipeline td, .pipeline th{
.pipeline tr, .pipeline td, .pipeline th {
width: 248px;
line-height: 26px;
text-align: center;

44
docs/static/css/material_custom.css поставляемый Normal file
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@ -0,0 +1,44 @@
/* viewcode link should have left padding */
span.viewcode-link {
padding-left: 0.6rem;
}
/* adding an eyebrow for API references */
dt.sig-object {
position: relative;
background: #f4f5f7;
padding: 0.5rem;
border-left: 0.2rem solid #ec407a; /* this should be matched with theme color. */
word-wrap: break-word;
}
/* logo is too large */
a.md-logo img {
padding: 3px;
}
/* Add split for navigation */
nav.md-tabs .md-tabs__item:not(:last-child) {
padding-right: 0;
}
nav.md-tabs .md-tabs__item:not(:last-child) .md-tabs__link:after {
content: "»";
font-family: "Material Icons";
padding-left: 0.6rem;
}
/* hide the floating button generated by readthedocs */
.rst-versions.rst-badge {
display: none !important;
}
/* Add some margin between main content and footer */
.md-footer {
margin-top: 1rem;
}
/* make footer appearing inline */
.md-footer-copyright__highlight {
display: inline;
}

61
docs/static/css/material_dropdown.css поставляемый Normal file
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@ -0,0 +1,61 @@
/* https://codepen.io/mildrenben/pen/RPwQEY */
.drop {
width: 125px;
vertical-align: middle;
}
.drop button {
color: inherit;
font-weight: 700;
font-size: .65rem;
cursor: pointer;
}
.drop button:after {
content: "\e5c5";
padding: 0 0 0.3rem 0.2rem;
font-size: 0.5rem;
font-family: "Material Icons";
display: inline-block;
transition: all 0.2s ease;
transform: rotate(0);
transform-origin: 65% 30%;
}
.drop button.active:after {
transform: rotate(180deg);
}
.drop ul {
position: absolute;
left: 0;
top: 6em;
transition: all 0.1s ease;
padding: .625em 0;
margin-top: -0.9rem;
transform: scale(0);
transform-origin: 0 0;
background: #fcfcfc;
box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.16), 0 2px 8px 0 rgba(0, 0, 0, 0.12);
}
.drop ul.active {
transform: scale(1);
}
.drop ul li {
display: block;
width: 100%;
}
.drop ul li a.md-nav__link {
width: 100%;
padding: .4rem 1em;
display: inline-block;
white-space: pre;
box-sizing: border-box;
color: rgba(0, 0, 0, .87);
margin-top: 0;
font-size: .75rem;
}

1
docs/static/img/README.md поставляемый Normal file
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@ -0,0 +1 @@
This folder is deprecated. Please do not put files here in future!

26
docs/static/js/github.js поставляемый Normal file
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@ -0,0 +1,26 @@
function add_descriptive_text(texts) {
const d = $('*[data-md-source="nni"]');
// clear first
d.find("ul").remove();
let s = "";
for (const text of texts) {
s += '<li class="md-source__fact">' + text + '</li>';
}
d.find(".md-source__repository").append('<ul class="md-source__facts">' + s + '</ul>');
}
function kFormatter(num) {
// https://stackoverflow.com/questions/9461621/format-a-number-as-2-5k-if-a-thousand-or-more-otherwise-900
return Math.abs(num) > 999 ? Math.sign(num)*((Math.abs(num)/1000).toFixed(1)) + 'k' : Math.sign(num)*Math.abs(num);
}
$(document).ready(function() {
add_descriptive_text(["View on GitHub"]);
$.getJSON("https://api.github.com/repos/microsoft/nni", function (data) {
add_descriptive_text([
kFormatter(data["stargazers_count"]) + " stars",
kFormatter(data["forks"]) + " forks",
]);
});
});

111
docs/static/js/version.js поставляемый Normal file
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@ -0,0 +1,111 @@
// Uncomment the following for debug
// READTHEDOCS_DATA = {
// "ad_free": false,
// "api_host": "https://readthedocs.org",
// "build_date": "2022-01-25T06:27:55Z",
// "builder": "sphinx",
// "canonical_url": null,
// "commit": "ca66e346",
// "docroot": "/docs/en_US/",
// "features": { "docsearch_disabled": false },
// "global_analytics_code": "UA-17997319-1",
// "language": "en",
// "page": "Tutorial",
// "programming_language": "words",
// "project": "nni",
// "proxied_api_host": "/_",
// "source_suffix": ".rst",
// "subprojects": { "nni-zh": "https://nni.readthedocs.io/zh/stable/" },
// "theme": "sphinx_material",
// "user_analytics_code": "UA-136029994-1",
// "version": "latest"
// };
// READTHEDOCS_VERSIONS = [
// ["latest", "/en/latest/"],
// ["stable", "/en/stable/"],
// ["v2.6", "/en/v2.6/"],
// ["v2.5", "/en/v2.5/"],
// ["v2.4", "/en/v2.4/"],
// ["v2.3", "/en/v2.3/"]
// ];
// The above code is injected by readthedocs in production.
function create_dropdown(button_text, items) {
const dropdown = document.createElement("div");
dropdown.className = "md-flex__cell md-flex__cell--shrink drop";
const button = document.createElement("button");
button.innerHTML = button_text;
const content = document.createElement("ul");
// content.className = "dropdown-content md-hero";
dropdown.appendChild(button);
dropdown.appendChild(content);
for (const key in items) {
if (items.hasOwnProperty(key)) {
const li = document.createElement("li");
const a = document.createElement("a");
a.className = "md-nav__link"
a.innerHTML = key;
a.title = key;
a.href = items[key];
li.appendChild(a);
content.appendChild(li);
}
}
$(button).click(function (e) {
// first close all others.
$(".drop").find(".active").removeClass("active");
$(dropdown).find("ul").addClass("active");
$(dropdown).find("button").addClass("active");
e.stopPropagation();
})
$(document).click(function () {
$(".drop").find(".active").removeClass("active");
})
return dropdown;
}
function remove_version_dropdown() {
$(".navheader").children().last().remove();
}
function add_version_dropdown() {
const prev_versions = Object.assign({}, ...READTHEDOCS_VERSIONS.map(([k, v]) => ({ [k]: v })));
const current_version = 'v: ' + READTHEDOCS_DATA["version"];
$(".navheader").append(create_dropdown(current_version, prev_versions));
}
function add_language_dropdown() {
const language_dropdown = {
'en': 'English',
'zh': '简体中文'
};
let current_language = 'en';
const pathname_prefix = window.location.pathname.split('/');
if (pathname_prefix.length > 1 && language_dropdown.hasOwnProperty(pathname_prefix[1])) {
current_language = pathname_prefix[1];
}
function get_dropdown_href(lang) {
let pathname = window.location.pathname.split('/');
if (pathname.length > 1) {
pathname[1] = lang;
}
return pathname.join('/');
}
$(".navheader").append(create_dropdown(language_dropdown[current_language], {
[language_dropdown['en']]: get_dropdown_href('en'),
[language_dropdown['zh']]: get_dropdown_href('zh')
}))
}
$(document).ready(function () {
remove_version_dropdown();
add_language_dropdown();
add_version_dropdown();
});

20
docs/templates/layout.html поставляемый Normal file
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@ -0,0 +1,20 @@
{% extends "!layout.html" %}
{#- TO INJECT INFORMATION FROM READTHEDOCS HERE #}
{% block scripts %}
{{ super() }}
{% if versions %}
<script type="text/javascript">
READTHEDOCS_VERSIONS = {{ versions | tojson }}
</script>
{% endif %}
{% endblock %}
{#- REPLACE ATTRIBUTES INSTANTLY TO DISABLE SOME HOOKS #}
{% block footer_scripts %}
<script type="text/javascript">
// try to disable original hook for md-source.
$('*[data-md-source="github"]').attr("data-md-source", "nni");
</script>
{{ super() }}
{% endblock %}

2
docs/tools/chineselink.py
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@ -27,7 +27,7 @@ def walk(path):
# Not the recommended way of sphinx though: https://docs.readthedocs.io/en/stable/guides/manage-translations-sphinx.html
whitelist = [
'_templates/index.html', # I think no one ever remembers to update this file. Might need to rethink about this.
'index.rst', # I think no one ever remembers to update this file. Might need to rethink about this.
'Overview.rst',
'installation.rst',
'Tutorial/InstallationLinux.rst',

460
docs/zh_CN/_templates/index.html
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@ -1,460 +0,0 @@
{% extends "!layout.html" %}
{% set title = "欢迎使用 Neural Network Intelligence !!!"%}
{% block document %}
<div>
<div class="chinese"><a href="https://nni.readthedocs.io/zh/stable/">English</a></div>
<b>NNI (Neural Network Intelligence)</b> 是一个轻量但强大的工具包，帮助用户<b>自动</b>的进行
<a href="{{ pathto('FeatureEngineering/Overview') }}">特征工程</a>，<a href="{{ pathto('NAS/Overview') }}">神经网络架构搜索</a>， <a href="{{ pathto('Tuner/BuiltinTuner') }}">超参调优</a>以及<a href="{{ pathto('Compression/Overview') }}">模型压缩</a>。
</div>
<p class="topMargin">
NNI 管理自动机器学习 (AutoML) 的 Experiment，
<b>调度运行</b>
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
<b>各种训练环境</b>，如
<a href="{{ pathto('TrainingService/LocalMode') }}">本机</a>,
<a href="{{ pathto('TrainingService/RemoteMachineMode') }}">远程服务器</a>,
<a href="{{ pathto('TrainingService/PaiMode') }}">OpenPAI</a>,
<a href="{{ pathto('TrainingService/KubeflowMode') }}">Kubeflow</a>,
<a href="{{ pathto('TrainingService/FrameworkControllerMode') }}">基于 K8S 的 FrameworkController（如，AKS 等)</a>,
<a href="{{ pathto('TrainingService/DLTSMode') }}">DLWorkspace (又称 DLTS)</a>,
<a href="{{ pathto('TrainingService/AMLMode') }}">AML (Azure Machine Learning)</a>
以及其它云服务。
</p>
<!-- Who should consider using NNI -->
<div>
<h1 class="title">使用场景</h1>
<ul>
<li>想要在自己的代码、模型中试验<b>不同的自动机器学习算法</b>。</li>
<li>想要在<b>不同的环境中</b>加速运行自动机器学习。</li>
<li>想要更容易<b>实现或试验新的自动机器学习算法</b>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。
</li>
<li>在机器学习平台中<b>支持自动机器学习</b>。</li>
</ul>
</div>
<!-- nni release to version -->
<div class="inline">
<h3><a href="https://github.com/microsoft/nni/releases">NNI {{ release }} 已发布！</a></h3>
<img width="48" src="_static/img/release_icon.png">
</div>
<!-- NNI capabilities in a glance -->
<div>
<h1 class="title">NNI 功能一览</h1>
<p>
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。
</p>
<p>
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>
</div>
<p align="center">
<a href="#overview"><img src="_static/img/overview.svg" /></a>
</p>
<table class="list">
<tbody>
<tr align="center" valign="bottom" class="column">
<td></td>
<td class="framework">
<b>框架和库</b>
</td>
<td>
<b>算法</b>
</td>
<td>
<b>训练平台</b>
</td>
</tr>
</tr>
<tr>
<td class="verticalMiddle"><b>内置</b></td>
<td>
<ul class="firstUl">
<li><b>支持的框架</b></li>
<ul class="circle">
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="{{ pathto('SupportedFramework_Library') }}">更多...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>支持的库</b></li>
<ul class="circle">
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="{{ pathto('SupportedFramework_Library') }}">更多...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>示例</b></li>
<ul class="circle">
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv2">MNIST-tensorflow</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="{{ pathto('TrialExample/GbdtExample') }}">Auto-gbdt</a></li>
<li><a href="{{ pathto('TrialExample/Cifar10Examples') }}">Cifar10-pytorch</li></a>
<li><a href="{{ pathto('TrialExample/SklearnExamples') }}">Scikit-learn</a></li>
<li><a href="{{ pathto('TrialExample/EfficientNet') }}">EfficientNet</a></li>
<li><a href="{{ pathto('TrialExample/OpEvoExamples') }}">GPU Kernel 调优</li></a>
<a href="{{ pathto('SupportedFramework_Library') }}">更多...</a><br />
</ul>
</ul>
</td>
<td align="left">
<a href="{{ pathto('Tuner/BuiltinTuner') }}">超参调优</a>
<ul class="firstUl">
<div><b>穷举搜索</b></div>
<ul class="circle">
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Random">Random Search（随机搜索）</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#GridSearch">Grid Search（遍历搜索）</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Batch">Batch（批处理）</a></li>
</ul>
<div><b>启发式搜索</b></div>
<ul class="circle">
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Evolution">Naïve Evolution（朴素进化）</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Anneal">Anneal（退火算法）</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#Hyperband">Hyperband</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#PBTTuner">P-DARTS</a></li>
</ul>
<div><b>贝叶斯优化</b></div>
<ul class="circle">
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#BOHB">BOHB</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#TPE">TPE</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#SMAC">SMAC</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#MetisTuner">Metis Tuner</a></li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#GPTuner">GP Tuner</a> </li>
<li><a href="{{ pathto('Tuner/BuiltinTuner') }}#DNGOTuner">PPO Tuner</a></li>
</ul>
</ul>
<a href="{{ pathto('NAS/Overview') }}">神经网络架构搜索</a>
<ul class="firstUl">
<ul class="circle">
<li><a href="{{ pathto('NAS/ENAS') }}">ENAS</a></li>
<li><a href="{{ pathto('NAS/DARTS') }}">DARTS</a></li>
<li><a href="{{ pathto('NAS/SPOS') }}">SPOS</a></li>
<li><a href="{{ pathto('NAS/Proxylessnas') }}">ProxylessNAS</a></li>
<li><a href="{{ pathto('NAS/FBNet') }}">微信</a></li>
<li><a href="{{ pathto('NAS/ExplorationStrategies') }}">基于强化学习</a></li>
<li><a href="{{ pathto('NAS/ExplorationStrategies') }}">Network Morphism</a></li>
<li><a href="{{ pathto('NAS/Overview') }}">TextNAS</a></li>
</ul>
</ul>
<a href="{{ pathto('Compression/Overview') }}">模型压缩</a>
<ul class="firstUl">
<div><b>剪枝</b></div>
<ul class="circle">
<li><a href="{{ pathto('Compression/Pruner') }}#agp-pruner">AGP Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#slim-pruner">Slim Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#admm-pruner">ADMM Pruner</a></li>
<li><a href="{{ pathto('Compression/Pruner') }}#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="{{ pathto('Compression/Overview') }}">更多...</a></li>
</ul>
<div><b>量化</b></div>
<ul class="circle">
<li><a href="{{ pathto('Compression/Quantizer') }}#qat-quantize">QAT Quantizer</a></li>
<li><a href="{{ pathto('Compression/Quantizer') }}#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="{{ pathto('Compression/Quantizer') }}#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="{{ pathto('FeatureEngineering/Overview') }}">特征工程（测试版）</a>
<ul class="circle">
<li><a href="{{ pathto('FeatureEngineering/GradientFeatureSelector') }}">GradientFeatureSelector</a></li>
<li><a href="{{ pathto('FeatureEngineering/GBDTSelector') }}">GBDTSelector</a></li>
</ul>
<a href="{{ pathto('Assessor/BuiltinAssessor') }}">提前终止算法</a>
<ul class="circle">
<li><a href="{{ pathto('Assessor/BuiltinAssessor') }}#MedianStop">Median Stop（中位数终止）</a></li>
<li><a href="{{ pathto('Assessor/BuiltinAssessor') }}#Curvefitting">Curve Fitting（曲线拟合）</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('TrainingService/LocalMode') }}">本机</a></li>
<li><a href="{{ pathto('TrainingService/RemoteMachineMode') }}">远程计算机</a></li>
<li><a href="{{ pathto('TrainingService/HybridMode') }}">混合模式</a></li>
<li><a href="{{ pathto('TrainingService/AMLMode') }}">AML(Azure Machine Learning)</a></li>
<li><b>基于 Kubernetes 的平台</b></li>
<ul>
<li><a href="{{ pathto('TrainingService/PaiMode') }}">OpenPAI</a></li>
<li><a href="{{ pathto('TrainingService/KubeflowMode') }}">Kubeflow</a></li>
<li><a href="{{ pathto('TrainingService/FrameworkControllerMode') }}">基于 K8S 的 FrameworkController (如 AKS 等)</a></li>
<li><a href="{{ pathto('TrainingService/DLTSMode') }}">DLWorkspace (又称 DLTS)</a></li>
<li><a href="{{ pathto('TrainingService/AdaptDLMode') }}">AML (Azure Machine Learning)</a></li>
</ul>
</ul>
</td>
</tr>
<tr valign="top">
<td class="verticalMiddle"><b>参考</b></td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('Tutorial/HowToLaunchFromPython') }}">Python API</a></li>
<li><a href="{{ pathto('Tutorial/AnnotationSpec') }}">NNI Annotation</a></li>
<li><a href="{{ pathto('installation') }}">支持的操作系统</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('Tuner/CustomizeTuner') }}">自定义 Tuner</a></li>
<li><a href="{{ pathto('Assessor/CustomizeAssessor') }}">自定义 Assessor</a></li>
<li><a href="{{ pathto('Tutorial/InstallCustomizedAlgos') }}">安装自定义的 Tuner，Assessor，Advisor</a></li>
<li><a href="{{ pathto('NAS/QuickStart') }}">定义 NAS 模型空间</a></li>
<li><a href="{{ pathto('NAS/ApiReference') }}">NAS/Retiarii APIs</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="{{ pathto('TrainingService/Overview') }}">支持训练平台</a></li>
<li><a href="{{ pathto('TrainingService/HowToImplementTrainingService') }}">实现训练平台</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
<!-- Installation -->
<div>
<h1 class="title">安装</h1>
<div>
<h2 class="second-title">安装</h2>
<p>
NNI 支持并在 Ubuntu >= 16.04, macOS >= 10.14.1, 和 Windows 10 >= 1809 通过了测试。 在 <code>python 64-bit >= 3.6</code> 的环境中，只需要运行 <code>pip install</code> 即可完成安装。
</p>
<div class="command-intro">Linux 或 macOS</div>
<div class="command">python3 -m pip install --upgrade nni</div>
<div class="command-intro">Windows</div>
<div class="command">python -m pip install --upgrade nni</div>
<p class="topMargin">如果想要尝试最新代码，可通过源代码<a href="{{ pathto('installation') }}">安装
NNI</a>。
</p>
<p>Linux 和 macOS 下 NNI 系统需求<a href="{{ pathto('Tutorial/InstallationLinux') }}">参考这里</a>，Windows <a href="{{ pathto('Tutorial/InstallationWin') }}">参考这里</a>。</p>
</div>
<div>
<p>注意：</p>
<ul>
<li>如果遇到任何权限问题，可添加 --user 在用户目录中安装 NNI。</li>
<li>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 <a href="{{ pathto('Tutorial/InstallationWin') }}">在 Windows 上安装 NNI</a>。</li>
<li>如果遇到如 Segmentation fault 这样的任何错误请参考 <a
href="{{ pathto('installation') }}">常见问题</a>。 Windows 上的常见问题，参考在 <a href="{{ pathto('Tutorial/InstallationWin') }}">Windows 上使用 NNI</a>。 Windows 上的常见问题，参考在 <a href="{{ pathto('Tutorial/InstallationWin') }}">Windows 上使用 NNI</a>。</li>
</ul>
</div>
<div>
<h2 class="second-title">验证安装</h2>
<p>
以下示例基于 TensorFlow 1.x 构建。 确保运行环境中使用的是 <b>TensorFlow 1.x</b>。
</p>
<ul>
<li>
<p>通过克隆源代码下载示例。</p>
<div class="command">git clone -b {{ release }} https://github.com/Microsoft/nni.git</div>
</li>
<li>
<p>运行 MNIST 示例。</p>
<div class="command-intro">Linux 或 macOS</div>
<div class="command">nnictl create --config nni/examples/trials/mnist-tfv1/config.yml</div>
<div class="command-intro">Windows</div>
<div class="command">nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml</div>
</li>
<li>
<p>
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。
</p>
<!-- Indentation affects style！ -->
<pre class="code">
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
-----------------------------------------------------------------------
</pre>
</li>
<li>
在浏览器中打开 Web UI 地址，可看到下图的 Experiment 详细信息，以及所有的 Trial 任务。 查看<a href="{{ pathto('Tutorial/WebUI') }}">这里的</a>更多页面示例。
<img src="_static/img/webui.gif" width="100%"/>
</div>
</li>
</ul>
</div>
<!-- Documentation -->
<div>
<h1 class="title">文档</h1>
<ul>
<li>要了解 NNI，请阅读 <a href="{{ pathto('Overview') }}">NNI 概述</a>。</li>
<li>要熟悉如何使用 NNI，请阅读<a href="{{ pathto('index') }}">文档</a>。</li>
<li>要安装 NNI，请参阅<a href="{{ pathto('installation') }}">安装 NNI</a>。</li>
</ul>
</div>
<!-- Contributing -->
<div>
<h1 class="title">贡献</h1>
<p>
本项目欢迎任何贡献和建议。 大多数贡献都需要你同意参与者许可协议（CLA），来声明你有权，并实际上授予我们有权使用你的贡献。
有关详细信息，请访问 <a href="https://cla.microsoft.com">https://cla.microsoft.com</a>。
</p>
<p>
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）。 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。
</p>
<p>
该项目采用了 <a href="https://opensource.microsoft.com/codeofconduct/">Microsoft 开源行为准则 </a>。 有关详细信息,请参阅<a href="https://opensource.microsoft.com/codeofconduct/faq/">行为守则常见问题解答</a>或联系 <a
href="mailto:opencode@microsoft.com">opencode@microsoft.com</a> 咨询问题或评论。
</p>
<p>
熟悉贡献协议后，即可按照 NNI 开发人员教程，创建第一个 PR =) 了：
</p>
<ul>
<li>推荐新贡献者先从简单的问题开始：<a
href="https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">'good first issue'</a> 或 <a
href="https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22">'help-wanted'</a>。
</li>
<li><a href="{{ pathto('Tutorial/SetupNniDeveloperEnvironment') }}">NNI 开发环境安装教程</a></li>
<li><a href="{{ pathto('Tutorial/HowToDebug') }}">如何调试</a></li>
<li>
如果有使用上的问题，可先查看<a href="{{ pathto('Tutorial/FAQ') }}">常见问题解答</a>。如果没能解决问题，可通过 <a
href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">Gitter</a>
联系 NNI 开发团队或在 GitHub 上<a href="https://github.com/microsoft/nni/issues/new/choose">报告问题</a>。
</li>
<li><a href="{{ pathto('Tuner/CustomizeTuner') }}">自定义 Tuner</a></li>
<li><a href="{{ pathto('TrainingService/HowToImplementTrainingService') }}">实现定制的训练平台</a>
</li>
<li><a href="{{ pathto('NAS/Advanced') }}">在 NNI 上实现新的 NAS Trainer</a></li>
<li><a href="{{ pathto('Tuner/CustomizeAdvisor') }}">自定义 Advisor</a></li>
</ul>
</div>
<!-- External Repositories and References -->
<div>
<h1 class="title">其它代码库和参考</h1>
<p>经作者许可的一些 NNI 用法示例和相关文档。</p>
<ul>
<h2>外部代码库</h2>
<li>在 NNI 中运行 <a href="{{ pathto('NAS/ENAS') }}">ENAS</a></li>
<li>
https://github.com/microsoft/nni/blob/master/examples/feature_engineering/auto-feature-engineering/README_zh_CN.md
</li>
<li>使用 NNI 的 <a
href="https://github.com/microsoft/recommenders/blob/master/examples/04_model_select_and_optimize/nni_surprise_svd.ipynb">矩阵分解超参调优</a></li>
<li><a href="https://github.com/ksachdeva/scikit-nni">scikit-nni</a> 使用 NNI 为 scikit-learn 开发的超参搜索。</li>
</ul>
<!-- Relevant Articles -->
<ul>
<h2>相关文章</h2>
<li><a href="{{ pathto('CommunitySharings/HpoComparison') }}">超参数优化的对比</a></li>
<li><a href="{{ pathto('CommunitySharings/NasComparison') }}">神经网络结构搜索的对比</a></li>
<li><a href="{{ pathto('CommunitySharings/ParallelizingTpeSearch') }}">并行化顺序算法：TPE</a>
</li>
<li><a href="{{ pathto('CommunitySharings/RecommendersSvd') }}">使用 NNI 为 SVD 自动调参</a></li>
<li><a href="{{ pathto('CommunitySharings/SptagAutoTune') }}">使用 NNI 为 SPTAG 自动调参</a></li>
<li><a
href="https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1">
使用 NNI 为 scikit-learn 查找超参
</a></li>
<li>
<strong>博客</strong> - <a
href="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">AutoML 工具（Advisor，NNI 与 Google Vizier）的对比</a> 作者：@gaocegege - kubeflow/katib 的设计与实现的总结与分析章节
</li>
<li>
Blog (中文) - <a href="https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA">NNI 2019 新功能汇总</a> by @squirrelsc
</li>
</ul>
</div>
<!-- feedback -->
<div>
<h1 class="title">反馈</h1>
<ul>
<li><a href="https://github.com/microsoft/nni/issues/new/choose">在 GitHub 上提交问题</a>。</li>
<li>在 <a
href="https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true">Stack Overflow</a> 上使用 nni 标签提问。
</li>
<li>在 <a
href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">Gitter</a> 中参与讨论。</li>
</ul>
<div>
<div>加入聊天组：</div>
<table border=1 style="border-collapse: collapse;">
<tbody>
<tr style="line-height: 30px;">
<th>Gitter</th>
<td></td>
<th>微信</th>
</tr>
<tr>
<td class="QR">
<img src="https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png" alt="Gitter" />
</td>
<td width="80" align="center" class="or">或</td>
<td class="QR">
<img src="https://github.com/scarlett2018/nniutil/raw/master/wechat.png" alt="NNI 微信" />
</td>
</tr>
</tbody>
</table>
</div>
</div>
<!-- Related Projects -->
<div>
<h1 class="title">相关项目</h1>
<p>
以探索先进技术和开放为目标，<a href="https://www.microsoft.com/zh-cn/research/group/systems-and-networking-research-group-asia/">Microsoft Research (MSR)</a> 还发布了一些相关的开源项目。</p>
<ul id="relatedProject">
<li>
<a href="https://github.com/Microsoft/pai">OpenPAI</a>：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。
</li>
<li>
<a href="https://github.com/Microsoft/frameworkcontroller">FrameworkController</a>：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。
</li>
<li>
<a href="https://github.com/Microsoft/MMdnn">MMdnn</a>：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。
</li>
<li>
<a href="https://github.com/Microsoft/SPTAG">SPTAG</a> : 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。
</li>
</ul>
<p>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。</p>
</div>
<!-- License -->
<div>
<h1 class="title">许可协议</h1>
<p>代码库遵循 <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT 许可协议</a></p>
</div>
</div>
{% endblock %}

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.. 823dad4710f371e923033eebd9dba255
###########################
Neural Network Intelligence
###########################
.. toctree::
:caption: 目录
:maxdepth: 2
:titlesonly:
概述<Overview>
安装 <installation>
入门<Tutorial/QuickStart>
自动（超参数）调优 <hyperparameter_tune>
神经网络架构搜索<nas>
模型压缩<model_compression>
特征工程<feature_engineering>
参考<reference>
示例与解决方案<CommunitySharings/community_sharings>
研究和出版物 <ResearchPublications>
常见问题 <Tutorial/FAQ>
如何贡献 <contribution>
更改日志 <Release>

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.. 24e980bd73ba5e335fba0d026916955e
###########################
Neural Network Intelligence
###########################
.. toctree::
:maxdepth: 2
:titlesonly:
:hidden:
概述<Overview>
安装 <installation>
入门<Tutorial/QuickStart>
自动（超参数）调优 <hyperparameter_tune>
神经网络架构搜索<nas>
模型压缩<model_compression>
特征工程<feature_engineering>
参考<reference>
示例与解决方案<CommunitySharings/community_sharings>
研究和出版物 <ResearchPublications>
常见问题 <Tutorial/FAQ>
如何贡献 <contribution>
更改日志 <Release>
.. raw:: html
<div class="rowHeight">
<div class="chinese"><a href="https://nni.readthedocs.io/zh/stable/">English</a></div>
<b>NNI (Neural Network Intelligence)</b> 是一个轻量但强大的工具包，帮助用户<b>自动</b>的进行
<a href="FeatureEngineering/Overview.html">特征工程</a>，<a href="NAS/Overview.html">神经网络架构搜索</a>， <a href="Tuner/BuiltinTuner.html">超参调优</a>以及<a href="Compression/Overview.html">模型压缩</a>。
</div>
<p class="gap rowHeight">
NNI 管理自动机器学习 (AutoML) 的 Experiment，
<b>调度运行</b>
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
<b>各种训练环境</b>，如
<a href="TrainingService/LocalMode.html">本机</a>,
<a href="TrainingService/RemoteMachineMode.html">远程服务器</a>,
<a href="TrainingService/PaiMode.html">OpenPAI</a>,
<a href="TrainingService/KubeflowMode.html">Kubeflow</a>,
<a href="TrainingService/FrameworkControllerMode.html">基于 K8S 的 FrameworkController（如，AKS 等)</a>,
<a href="TrainingService/DLTSMode.html">DLWorkspace (又称 DLTS)</a>,
<a href="TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>
以及其它云服务。
</p>
<!-- Who should consider using NNI -->
<div>
<h1 class="title">使用场景</h1>
<ul>
<li>想要在自己的代码、模型中试验<b>不同的自动机器学习算法</b>。</li>
<li>想要在<b>不同的环境中</b>加速运行自动机器学习。</li>
<li>想要更容易<b>实现或试验新的自动机器学习算法</b>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。
</li>
<li>在机器学习平台中<b>支持自动机器学习</b>。</li>
</ul>
</div>
<!-- nni release to version -->
<div class="inline gap">
<h3><a href="https://github.com/microsoft/nni/releases">NNI v2.6 已发布！</a></h3>
<img width="48" src="_static/img/release_icon.png">
</div>
<!-- NNI capabilities in a glance -->
<div class="gap">
<h1 class="title">NNI 功能一览</h1>
<p class="rowHeight">
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。
</p>
<p class="rowHeight">
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>
</div>
<p align="center">
<a href="#overview"><img src="_static/img/overview.svg" /></a>
</p>
<table class="main-table">
<tbody>
<tr align="center" valign="bottom" class="column">
<td></td>
<td class="framework">
<b>框架和库</b>
</td>
<td>
<b>算法</b>
</td>
<td>
<b>训练平台</b>
</td>
</tr>
</tr>
<tr>
<td class="verticalMiddle"><b>内置</b></td>
<td>
<ul class="firstUl">
<li><b>支持的框架</b></li>
<ul class="circle">
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="SupportedFramework_Library.html">更多...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>支持的库</b></li>
<ul class="circle">
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="SupportedFramework_Library.html">更多...</a><br />
</ul>
</ul>
<ul class="firstUl">
<li><b>示例</b></li>
<ul class="circle">
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv2">MNIST-tensorflow</li>
</a>
<li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="TrialExample/GbdtExample.html">Auto-gbdt</a></li>
<li><a href="TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
<li><a href="TrialExample/SklearnExamples.html">Scikit-learn</a></li>
<li><a href="TrialExample/EfficientNet.html">EfficientNet</a></li>
<li><a href="TrialExample/OpEvoExamples.html">GPU Kernel 调优</li></a>
<a href="SupportedFramework_Library.html">更多...</a><br />
</ul>
</ul>
</td>
<td align="left">
<a href="Tuner/BuiltinTuner.html">超参调优</a>
<ul class="firstUl">
<div><b>穷举搜索</b></div>
<ul class="circle">
<li><a href="Tuner/BuiltinTuner.html#Random">Random Search（随机搜索）</a></li>
<li><a href="Tuner/BuiltinTuner.html#GridSearch">Grid Search（遍历搜索）</a></li>
<li><a href="Tuner/BuiltinTuner.html#Batch">Batch（批处理）</a></li>
</ul>
<div><b>启发式搜索</b></div>
<ul class="circle">
<li><a href="Tuner/BuiltinTuner.html#Evolution">Naïve Evolution（朴素进化）</a></li>
<li><a href="Tuner/BuiltinTuner.html#Anneal">Anneal（退火算法）</a></li>
<li><a href="Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
<li><a href="Tuner/BuiltinTuner.html#PBTTuner">P-DARTS</a></li>
</ul>
<div><b>贝叶斯优化</b></div>
<ul class="circle">
<li><a href="Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
<li><a href="Tuner/BuiltinTuner.html#TPE">TPE</a></li>
<li><a href="Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
<li><a href="Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
<li><a href="Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a> </li>
<li><a href="Tuner/BuiltinTuner.html#DNGOTuner">PPO Tuner</a></li>
</ul>
</ul>
<a href="NAS/Overview.html">神经网络架构搜索</a>
<ul class="firstUl">
<ul class="circle">
<li><a href="NAS/ENAS.html">ENAS</a></li>
<li><a href="NAS/DARTS.html">DARTS</a></li>
<li><a href="NAS/SPOS.html">SPOS</a></li>
<li><a href="NAS/Proxylessnas.html">ProxylessNAS</a></li>
<li><a href="NAS/FBNet.html">微信</a></li>
<li><a href="NAS/ExplorationStrategies.html">基于强化学习</a></li>
<li><a href="NAS/ExplorationStrategies.html">Network Morphism</a></li>
<li><a href="NAS/Overview.html">TextNAS</a></li>
</ul>
</ul>
<a href="Compression/Overview.html">模型压缩</a>
<ul class="firstUl">
<div><b>剪枝</b></div>
<ul class="circle">
<li><a href="Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
<li><a href="Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
<li><a href="Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
<li><a href="Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="Compression/Overview.html">更多...</a></li>
</ul>
<div><b>量化</b></div>
<ul class="circle">
<li><a href="Compression/Quantizer.html#qat-quantize">QAT Quantizer</a></li>
<li><a href="Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="FeatureEngineering/Overview.html">特征工程（测试版）</a>
<ul class="circle">
<li><a href="FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
<li><a href="FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
</ul>
<a href="Assessor/BuiltinAssessor.html">提前终止算法</a>
<ul class="circle">
<li><a href="Assessor/BuiltinAssessor.html#MedianStop">Median Stop（中位数终止）</a></li>
<li><a href="Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting（曲线拟合）</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="TrainingService/LocalMode.html">本机</a></li>
<li><a href="TrainingService/RemoteMachineMode.html">远程计算机</a></li>
<li><a href="TrainingService/HybridMode.html">混合模式</a></li>
<li><a href="TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
<li><b>基于 Kubernetes 的平台</b></li>
<ul>
<li><a href="TrainingService/PaiMode.html">OpenPAI</a></li>
<li><a href="TrainingService/KubeflowMode.html">Kubeflow</a></li>
<li><a href="TrainingService/FrameworkControllerMode.html">基于 K8S 的 FrameworkController (如 AKS 等)</a></li>
<li><a href="TrainingService/DLTSMode.html">DLWorkspace (又称 DLTS)</a></li>
<li><a href="TrainingService/AdaptDLMode.html">AML (Azure Machine Learning)</a></li>
</ul>
</ul>
</td>
</tr>
<tr valign="top">
<td class="verticalMiddle"><b>参考</b></td>
<td>
<ul class="firstUl">
<li><a href="Tutorial/HowToLaunchFromPython.html">Python API</a></li>
<li><a href="Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
<li><a href="installation.html">支持的操作系统</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="Tuner/CustomizeTuner.html">自定义 Tuner</a></li>
<li><a href="Assessor/CustomizeAssessor.html">自定义 Assessor</a></li>
<li><a href="Tutorial/InstallCustomizedAlgos.html">安装自定义的 Tuner，Assessor，Advisor</a></li>
<li><a href="NAS/QuickStart.html">定义 NAS 模型空间</a></li>
<li><a href="NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
</ul>
</td>
<td>
<ul class="firstUl">
<li><a href="TrainingService/Overview.html">支持训练平台</a></li>
<li><a href="TrainingService/HowToImplementTrainingService.html">实现训练平台</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
<!-- Installation -->
<div>
<h1 class="title">安装</h1>
<div>
<h2 class="second-title">安装</h2>
<p>
NNI 支持并在 Ubuntu >= 16.04, macOS >= 10.14.1, 和 Windows 10 >= 1809 通过了测试。 在 <code>python 64-bit >= 3.6</code> 的环境中，只需要运行 <code>pip install</code> 即可完成安装。
</p>
<div class="command-intro">Linux 或 macOS</div>
<div class="command">python3 -m pip install --upgrade nni</div>
<div class="command-intro">Windows</div>
<div class="command">python -m pip install --upgrade nni</div>
<p class="topMargin">如果想要尝试最新代码，可通过源代码<a href="installation.html">安装
NNI</a>。
</p>
<p>Linux 和 macOS 下 NNI 系统需求<a href="Tutorial/InstallationLinux.html">参考这里</a>，Windows <a href="Tutorial/InstallationWin.html">参考这里</a>。</p>
</div>
<div>
<p>注意：</p>
<ul>
<li>如果遇到任何权限问题，可添加 --user 在用户目录中安装 NNI。</li>
<li>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 <a href="Tutorial/InstallationWin.html">在 Windows 上安装 NNI</a>。</li>
<li>如果遇到如 Segmentation fault 这样的任何错误请参考 <a
href="installation.html">常见问题</a>。 Windows 上的常见问题，参考在 <a href="Tutorial/InstallationWin.html">Windows 上使用 NNI</a>。 Windows 上的常见问题，参考在 <a href="Tutorial/InstallationWin.html">Windows 上使用 NNI</a>。</li>
</ul>
</div>
<div>
<h2 class="second-title">验证安装</h2>
<p>
以下示例基于 TensorFlow 1.x 构建。 确保运行环境中使用的是 <b>TensorFlow 1.x</b>。
</p>
<ul>
<li>
<p>通过克隆源代码下载示例。</p>
<div class="command">git clone -b v2.6 https://github.com/Microsoft/nni.git</div>
</li>
<li>
<p>运行 MNIST 示例。</p>
<div class="command-intro">Linux 或 macOS</div>
<div class="command">nnictl create --config nni/examples/trials/mnist-tfv1/config.yml</div>
<div class="command-intro">Windows</div>
<div class="command">nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml</div>
</li>
<li>
<p>
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。
</p>
<!-- Indentation affects style！ -->
<pre class="main-code">
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
-----------------------------------------------------------------------
</pre>
</li>
<li class="rowHeight">
在浏览器中打开 Web UI 地址，可看到下图的 Experiment 详细信息，以及所有的 Trial 任务。 查看<a href="Tutorial/WebUI.html">这里的</a>更多页面示例。
<img src="_static/img/webui.gif" width="100%"/>
</div>
</li>
</ul>
</div>
<!-- Documentation -->
<div>
<h1 class="title">文档</h1>
<ul>
<li>要了解 NNI，请阅读 <a href="Overview.html">NNI 概述</a>。</li>
<li>要熟悉如何使用 NNI，请阅读<a href="index.html">文档</a>。</li>
<li>要安装 NNI，请参阅<a href="installation.html">安装 NNI</a>。</li>
</ul>
</div>
<!-- Contributing -->
<div>
<h1 class="title">贡献</h1>
<p>
本项目欢迎任何贡献和建议。 大多数贡献都需要你同意参与者许可协议（CLA），来声明你有权，并实际上授予我们有权使用你的贡献。
有关详细信息，请访问 <a href="https://cla.microsoft.com">https://cla.microsoft.com</a>。
</p>
<p>
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）。 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。
</p>
<p>
该项目采用了 <a href="https://opensource.microsoft.com/codeofconduct/">Microsoft 开源行为准则 </a>。 有关详细信息,请参阅<a href="https://opensource.microsoft.com/codeofconduct/faq/">行为守则常见问题解答</a>或联系 <a
href="mailto:opencode@microsoft.com">opencode@microsoft.com</a> 咨询问题或评论。
</p>
<p>
熟悉贡献协议后，即可按照 NNI 开发人员教程，创建第一个 PR =) 了：
</p>
<ul>
<li>推荐新贡献者先从简单的问题开始：<a
href="https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">'good first issue'</a> 或 <a
href="https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22">'help-wanted'</a>。
</li>
<li><a href="Tutorial/SetupNniDeveloperEnvironment.html">NNI 开发环境安装教程</a></li>
<li><a href="Tutorial/HowToDebug.html">如何调试</a></li>
<li>
如果有使用上的问题，可先查看<a href="Tutorial/FAQ.html">常见问题解答</a>。如果没能解决问题，可通过 <a
href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">Gitter</a>
联系 NNI 开发团队或在 GitHub 上<a href="https://github.com/microsoft/nni/issues/new/choose">报告问题</a>。
</li>
<li><a href="Tuner/CustomizeTuner.html">自定义 Tuner</a></li>
<li><a href="TrainingService/HowToImplementTrainingService.html">实现定制的训练平台</a>
</li>
<li><a href="NAS/Advanced.html">在 NNI 上实现新的 NAS Trainer</a></li>
<li><a href="Tuner/CustomizeAdvisor.html">自定义 Advisor</a></li>
</ul>
</div>
<!-- External Repositories and References -->
<div>
<h1 class="title">其它代码库和参考</h1>
<p>经作者许可的一些 NNI 用法示例和相关文档。</p>
<ul>
<h2>外部代码库</h2>
<li>在 NNI 中运行 <a href="NAS/ENAS.html">ENAS</a></li>
<li>
https://github.com/microsoft/nni/blob/master/examples/feature_engineering/auto-feature-engineering/README_zh_CN.md
</li>
<li>使用 NNI 的 <a
href="https://github.com/microsoft/recommenders/blob/master/examples/04_model_select_and_optimize/nni_surprise_svd.ipynb">矩阵分解超参调优</a></li>
<li><a href="https://github.com/ksachdeva/scikit-nni">scikit-nni</a> 使用 NNI 为 scikit-learn 开发的超参搜索。</li>
</ul>
<!-- Relevant Articles -->
<ul>
<h2>相关文章</h2>
<li><a href="CommunitySharings/HpoComparison.html">超参数优化的对比</a></li>
<li><a href="CommunitySharings/NasComparison.html">神经网络结构搜索的对比</a></li>
<li><a href="CommunitySharings/ParallelizingTpeSearch.html">并行化顺序算法：TPE</a>
</li>
<li><a href="CommunitySharings/RecommendersSvd.html">使用 NNI 为 SVD 自动调参</a></li>
<li><a href="CommunitySharings/SptagAutoTune.html">使用 NNI 为 SPTAG 自动调参</a></li>
<li><a
href="https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1">
使用 NNI 为 scikit-learn 查找超参
</a></li>
<li>
<strong>博客</strong> - <a
href="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">AutoML 工具（Advisor，NNI 与 Google Vizier）的对比</a> 作者：@gaocegege - kubeflow/katib 的设计与实现的总结与分析章节
</li>
<li>
Blog (中文) - <a href="https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA">NNI 2019 新功能汇总</a> by @squirrelsc
</li>
</ul>
</div>
<!-- feedback -->
<div>
<h1 class="title">反馈</h1>
<ul>
<li><a href="https://github.com/microsoft/nni/issues/new/choose">在 GitHub 上提交问题</a>。</li>
<li>在 <a
href="https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true">Stack Overflow</a> 上使用 nni 标签提问。
</li>
<li>在 <a
href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">Gitter</a> 中参与讨论。</li>
</ul>
<div>
<div>加入聊天组：</div>
<table border=1 style="border-collapse: collapse;">
<tbody>
<tr style="line-height: 30px;">
<th>Gitter</th>
<td></td>
<th>微信</th>
</tr>
<tr>
<td class="QR">
<img src="https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png" alt="Gitter" />
</td>
<td width="80" align="center" class="or">或</td>
<td class="QR">
<img src="https://github.com/scarlett2018/nniutil/raw/master/wechat.png" alt="NNI 微信" />
</td>
</tr>
</tbody>
</table>
</div>
</div>
<!-- Related Projects -->
<div>
<h1 class="title">相关项目</h1>
<p>
以探索先进技术和开放为目标，<a href="https://www.microsoft.com/zh-cn/research/group/systems-and-networking-research-group-asia/">Microsoft Research (MSR)</a> 还发布了一些相关的开源项目。</p>
<ul id="relatedProject">
<li>
<a href="https://github.com/Microsoft/pai">OpenPAI</a>：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。
</li>
<li>
<a href="https://github.com/Microsoft/frameworkcontroller">FrameworkController</a>：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。
</li>
<li>
<a href="https://github.com/Microsoft/MMdnn">MMdnn</a>：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。
</li>
<li>
<a href="https://github.com/Microsoft/SPTAG">SPTAG</a> : 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。
</li>
</ul>
<p>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。</p>
</div>
<!-- License -->
<div>
<h1 class="title">许可协议</h1>
<p>代码库遵循 <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT 许可协议</a></p>
</div>
</div>

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