.. modified from index.html
.. replace \{\{ pathto\('(.*)'\) \}\} -> $1.html

###########################
Neural Network Intelligence
###########################


..  toctree::
    :maxdepth: 2
    :caption: Get Started
    :hidden:

    Installation <installation>
    QuickStart <Tutorial/QuickStart>
    Tutorials <tutorials>

..  toctree::
    :maxdepth: 2
    :caption: Full-scale Materials
    :hidden:

    Hyperparameter Optimization <hpo/index>
    Neural Architecture Search <nas/index>
    Model Compression <compression/index>
    Feature Engineering <feature_engineering>
    Experiment <experiment/overview>

..  toctree::
    :maxdepth: 2
    :caption: References
    :hidden:

    nnictl Commands <reference/nnictl>
    Experiment Configuration <reference/experiment_config>
    Python API <reference/_modules/nni>

..  toctree::
    :maxdepth: 2
    :caption: Misc
    :hidden:

    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="codesnippet-card-container">

.. codesnippetcard::
   :icon: ../img/thumbnails/hpo-icon-small.png
   :title: Hyper-parameter Tuning
   :link: autotune_ref

   .. code-block::

      params = nni.get_next_parameter()

      class Net(nn.Module):
          ...

      model = Net()
      optimizer = optim.SGD(model.parameters(),
                            params['lr'],
                            params['momentum'])

      for epoch in range(10):
          train(...)

      accuracy = test(model)
      nni.report_final_result(accuracy)

.. codesnippetcard::
   :icon: ../img/thumbnails/pruning-icon-small.png
   :title: Model Pruning
   :link: tutorials/pruning_quick_start_mnist

   .. code-block::

      # define a config_list
      config = [{
          'sparsity': 0.8,
          'op_types': ['Conv2d']
      }]

      # generate masks for simulated pruning
      wrapped_model, masks = \
          L1NormPruner(model, config). \
          compress()

      # apply the masks for real speed up
      ModelSpeedup(unwrapped_model, input, masks). \
          speedup_model()

.. codesnippetcard::
   :icon: ../img/thumbnails/quantization-icon-small.png
   :title: Quantization
   :link: tutorials/quantization_speed_up

   .. code-block::

      # define a config_list
      config = [{
          'quant_types': ['input', 'weight'],
          'quant_bits': {'input': 8, 'weight': 8},
          'op_types': ['Conv2d']
      }]

      # in case quantizer needs a extra training
      quantizer = QAT_Quantizer(model, config)
      quantizer.compress()
      # Training...

      # export calibration config and
      # generate TensorRT engine for real speed up
      calibration_config = quantizer.export_model(
          model_path, calibration_path)
      engine = ModelSpeedupTensorRT(
          model, input_shape, config=calib_config)
      engine.compress()

.. codesnippetcard::
   :icon: ../img/thumbnails/multi-trial-nas-icon-small.png
   :title: Neural Architecture Search
   :link: tutorials/hello_nas

   .. code-block:: diff

      # define model space
      -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
      +   self.conv2 = nn.LayerChoice([
      +       nn.Conv2d(32, 64, 3, 1),
      +       DepthwiseSeparableConv(32, 64)
      +   ])
      # search strategy + evaluator
      strategy = RegularizedEvolution()
      evaluator = FunctionalEvaluator(
          train_eval_fn)

      # run experiment
      RetiariiExperiment(model_space,
          evaluator, strategy).run()

.. codesnippetcard::
   :icon: ../img/thumbnails/one-shot-nas-icon-small.png
   :title: One-shot NAS
   :link: nas/index

   .. code-block::

      # define model space
      space = AnySearchSpace()

      # get a darts trainer
      trainer = DartsTrainer(space, loss, metrics)
      trainer.fit()

      # get final searched architecture
      arch = trainer.export()

.. codesnippetcard::
   :icon: ../img/thumbnails/feature-engineering-icon-small.png
   :title: Feature Engineering
   :link: FeatureEngineering/Overview

   .. code-block::

      selector = GBDTSelector()
      selector.fit(
          X_train, y_train,
          lgb_params=lgb_params,
          eval_ratio=eval_ratio,
          early_stopping_rounds=10,
          importance_type='gain',
          num_boost_round=1000)

      # get selected features
      features = selector.get_selected_features()

.. End of code snippet card

.. raw:: html

   </div>

.. 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>