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An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

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NNI Doc | 简体中文

NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.

The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.), DLWorkspace (aka. DLTS), AML (Azure Machine Learning), AdaptDL (aka. ADL) , other cloud options and even Hybrid mode.

Who should consider using NNI

  • Those who want to try different AutoML algorithms in their training code/model.
  • Those who want to run AutoML trial jobs in different environments to speed up search.
  • Researchers and data scientists who want to easily implement and experiment new AutoML algorithms, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
  • ML Platform owners who want to support AutoML in their platform.

What's NEW!  

NNI capabilities in a glance

NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in state-of-the-art AutoML algorithms and out of box support for popular training platforms.

Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.

<tr valign="top">
<td align="center" valign="middle">
  • Supported Frameworks
    • PyTorch
    • Keras
    • TensorFlow
    • MXNet
    • Caffe2
    • More...
    • Supported Libraries
      • Scikit-learn
      • XGBoost
      • LightGBM
      • More...
      <tr valign="top">
       <td valign="middle">
     <td style="border-top:#FF0000 solid 0px;">
        <li><a href="">Python API</a></li>
        <li><a href="">NNI Annotation</a></li>
         <li><a href="">Supported OS</a></li>
       <td style="border-top:#FF0000 solid 0px;">
        <li><a href="">CustomizeTuner</a></li>
        <li><a href="">CustomizeAssessor</a></li>
        <li><a href="">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
        <li><a href="">Define NAS Model Space</a></li>
        <li><a href="">NAS/Retiarii APIs</a></li>
        <td style="border-top:#FF0000 solid 0px;">
        <li><a href="">Support TrainingService</a></li>
        <li><a href="">Implement TrainingService</a></li>
    Frameworks & Libraries Algorithms Training Services
    Hyperparameter Tuning Neural Architecture Search (Retiarii) Model Compression Feature Engineering (Beta) Early Stop Algorithms



    NNI supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1, and Windows 10 >= 1809. Simply run the following

    pip install
    in an environment that has
    python 64-bit >= 3.6

    Linux or macOS

    python3 -m pip install --upgrade nni


    python -m pip install --upgrade nni

    If you want to try latest code, please install NNI from source code.

    For detail system requirements of NNI, please refer to here for Linux & macOS, and here for Windows.


    • If there is any privilege issue, add
      to install NNI in the user directory.
    • Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
    • If there is any error like
      Segmentation fault
      , please refer to FAQ. For FAQ on Windows, please refer to NNI on Windows.

    Verify installation

    • Download the examples via clone the source code.
      git clone -b v2.4
    • Run the MNIST example.

    Linux or macOS

      nnictl create --config nni/examples/trials/mnist-pytorch/config.yml


      nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml
    • Wait for the message
      INFO: Successfully started experiment!
      in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the
      Web UI url
    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:

    You can use these commands to get more information about the experiment

         commands                       description
    1. nnictl experiment show show the information of experiments
    2. nnictl trial ls list all of trial jobs
    3. nnictl top monitor the status of running experiments
    4. nnictl log stderr show stderr log content
    5. nnictl log stdout show stdout log content
    6. nnictl stop stop an experiment
    7. nnictl trial kill kill a trial job by id
    8. nnictl --help get help information about nnictl

    • Open the
      Web UI url
      in your browser, you can view detailed information of the experiment and all the submitted trial jobs as shown below. Here are more Web UI pages.


    Releases and Contributing

    NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by filling an issue.

    We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.

    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.

    To learn more about making a contribution to NNI, please refer to our How-to contribution page.

    We appreciate all contributions and thank all the contributors!


    Join IM discussion groups: |Gitter||WeChat| |----|----|----| |image| OR |image|

    Test status


    | Type | Status | | :---: | :---: | | Fast test | Build Status | | Full linux | Build Status | | Full windows | Build Status |

    Training services

    | Type | Status | | :---: | :---: | | Remote - linux to linux | Build Status | | Remote - linux to windows | Build Status | | Remote - windows to linux | Build Status | | OpenPAI | Build Status | | Frameworkcontroller | Build Status | | Kubeflow | Build Status | | Hybrid | Build Status | | AzureML | Build Status |

    Related Projects

    Targeting at openness and advancing state-of-art technology, Microsoft Research (MSR) had also released few other open source projects.

    • OpenPAI : an open source platform that provides complete AI model training and resource management capabilities, it is easy to extend and supports on-premise, cloud and hybrid environments in various scale.
    • FrameworkController : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller.
    • MMdnn : 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.
    • SPTAG : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario.
    • nn-Meter : An accurate inference latency predictor for DNN models on diverse edge devices.

    We encourage researchers and students leverage these projects to accelerate the AI development and research.


    The entire codebase is under MIT license

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