**A lightweight toolkit that automates feature engineering, neural architecture search, hyperparameter tuning, and model compression.**
[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE) [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&branchName=master) [![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen) [![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug) [![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen) [![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=stable)](https://nni.readthedocs.io/en/stable/?badge=stable) ______________________________________________________________________ Find the latest features, API, examples and tutorials in our **official documentation**: [NNI Doc](https://nni.readthedocs.io/) ## **What's NEW!**   * **New release**: [v2.6 is available](https://github.com/microsoft/nni/releases/tag/v2.6) - _released on Jan-19-2022_ * **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on May-26-2021_ * **New webinar**: [Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - _scheduled on June-24-2021_ * **New community channel**: [Discussions](https://github.com/microsoft/nni/discussions) * **New emoticons release**: [nnSpider](./docs/source/Tutorial/NNSpider.md)

## **NNI capabilities in a glance** **Neural Network Intelligence (NNI)** is a lightweight and powerful toolkit to help users **automate** Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression. NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. 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. 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.

Frameworks & Libraries Algorithms Training Services
Built-in
  • Supported Frameworks
    • PyTorch
    • Keras
    • TensorFlow
    • MXNet
    • Caffe2
      • More...
  • Supported Libraries
    • Scikit-learn
    • XGBoost
    • LightGBM
      • More...
 Hyperparameter Tuning Neural Architecture Search (Retiarii) Model Compression Feature Engineering (Beta) Early Stop Algorithms
References
## **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**. ## **Installation** ### **Install** NNI supports and is tested on Ubuntu >= 18.04, Windows 10 >= 21H2, and macOS >= 11. Simply run the following `pip install` in an environment that has `python 64-bit >= 3.7`. Linux or macOS ```bash python3 -m pip install --upgrade nni ``` Windows ```bash python -m pip install --upgrade nni ``` If you want to try latest code, please [install NNI](https://nni.readthedocs.io/en/stable/installation.html) from source code. For detail system requirements of NNI, please refer to [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationLinux.html#system-requirements) for Linux & macOS, and [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#system-requirements) for Windows.
Installation FAQ * If there is any privilege issue, add `--user` to install NNI in the user directory. * Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html). * If there is any error like `Segmentation fault`, please refer to [FAQ](https://nni.readthedocs.io/en/stable/Tutorial/FAQ.html). For FAQ on Windows, please refer to [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#faq).
### **Run your first example**
set up and run the example * Download the examples via clone the source code. ```bash git clone -b v2.6 https://github.com/Microsoft/nni.git ``` * Run the MNIST example. Linux or macOS ```bash nnictl create --config nni/examples/trials/mnist-pytorch/config.yml ``` Windows ```powershell nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml ``` * Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`. ```text INFO: Starting restful server... INFO: Successfully started Restful server! INFO: Setting local config... INFO: Successfully set local config! INFO: Starting experiment... INFO: Successfully started experiment! ----------------------------------------------------------------------- The experiment id is egchD4qy The Web UI urls are: http://223.255.255.1:8080 http://127.0.0.1:8080 ----------------------------------------------------------------------- You can use these commands to get more information about the experiment ----------------------------------------------------------------------- commands description 1. nnictl experiment show show the information of experiments 2. nnictl trial ls list all of trial jobs 3. nnictl top monitor the status of running experiments 4. nnictl log stderr show stderr log content 5. nnictl log stdout show stdout log content 6. nnictl stop stop an experiment 7. nnictl trial kill kill a trial job by id 8. nnictl --help get help information about nnictl ----------------------------------------------------------------------- ``` * Open the `Web UI url` in your browser, you can view detailed information of the experiment and all the submitted trial jobs as shown below. [Here](https://nni.readthedocs.io/en/stable/Tutorial/WebUI.html) are more Web UI pages.
webui ## **Releases and Contributing** NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by [filling an issue](https://github.com/microsoft/nni/issues/new/choose). We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions. 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](https://nni.readthedocs.io/en/stable/contribution.html). We appreciate all contributions and thank all the contributors! ## **Feedback** * [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub. * Open or participate in a [discussion](https://github.com/microsoft/nni/discussions). * Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI. Join IM discussion groups: |Gitter||WeChat| |----|----|----| |![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)| ## Test status ### Essentials | Type | Status | | :---: | :---: | | Fast test | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/fast%20test?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master) | | Full linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?repoName=microsoft%2Fnni&branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&repoName=microsoft%2Fnni&branchName=master) | | Full windows | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20windows?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=63&branchName=master) | ### Training services | Type | Status | | :---: | :---: | | Remote - linux to linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master) | | Remote - linux to windows | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20windows?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=67&branchName=master) | | Remote - windows to linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20windows%20to%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=68&branchName=master) | | OpenPAI | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20openpai%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master) | | Frameworkcontroller | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20frameworkcontroller?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master) | | Kubeflow | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20kubeflow?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master) | | Hybrid | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20hybrid?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master) | | AzureML | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20aml?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master) | ## Related Projects Targeting at openness and advancing state-of-art technology, [Microsoft Research (MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/) had also released few other open source projects. * [OpenPAI](https://github.com/Microsoft/pai) : 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](https://github.com/Microsoft/frameworkcontroller) : an open source general-purpose Kubernetes Pod Controller that orchestrate all kinds of applications on Kubernetes by a single controller. * [MMdnn](https://github.com/Microsoft/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](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) is an open source library for large scale vector approximate nearest neighbor search scenario. * [nn-Meter](https://github.com/microsoft/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. ## **License** The entire codebase is under [MIT license](LICENSE)