chaopengz/nni

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

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!  

• New release: v2.6 is available - released on Jan-19-2022
• New demo available: Youtube entry | Bilibili 入口 - last updated on May-26-2021
• New webinar: Introducing Retiarii: A deep learning exploratory-training framework on NNI - scheduled on June-24-2021
• New community channel: Discussions
• New emoticons release: nnSpider

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.

	Frameworks & Libraries	Algorithms	Training Services
Built-in	Supported Frameworks PyTorch Keras TensorFlow MXNet Caffe2 More... Supported Libraries Scikit-learn XGBoost LightGBM More... Examples MNIST-pytorch MNIST-tensorflow MNIST-keras Auto-gbdt Cifar10-pytorch Scikit-learn EfficientNet Kernel Tunning More...	Hyperparameter Tuning Exhaustive search Random Search Grid Search Batch Heuristic search Naïve Evolution Anneal Hyperband PBT Bayesian optimization BOHB TPE SMAC Metis Tuner GP Tuner DNGO Tuner Neural Architecture Search (Retiarii) ENAS DARTS SPOS ProxylessNAS FBNet Reinforcement Learning Regularized Evolution More... Model Compression Pruning AGP Pruner Slim Pruner FPGM Pruner NetAdapt Pruner SimulatedAnnealing Pruner ADMM Pruner AutoCompress Pruner More... Quantization QAT Quantizer DoReFa Quantizer BNN Quantizer Feature Engineering (Beta) GradientFeatureSelector GBDTSelector Early Stop Algorithms Median Stop Curve Fitting	Local Machine Remote Servers Hybrid mode AML(Azure Machine Learning) Kubernetes based services OpenPAI Kubeflow FrameworkController on K8S (AKS etc.) DLWorkspace (aka. DLTS) AdaptDL (aka. ADL)
References	Python API NNI Annotation Supported OS	CustomizeTuner CustomizeAssessor Install Customized Algorithms as Builtin Tuners/Assessors/Advisors Define NAS Model Space NAS/Retiarii APIs	Support TrainingService Implement TrainingService

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

python3 -m pip install --upgrade nni

Windows

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.

Note:

• If there is any privilege issue, add --user to install NNI in the user directory.
• Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
• 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 v2.6 https://github.com/Microsoft/nni.git

• Run the MNIST example.

  Linux or macOS

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

  Windows

  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: 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 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!

Feedback

• File an issue on GitHub.
• Open or participate in a discussion.
• Discuss on the NNI Gitter in NNI.

Join IM discussion groups:

Gitter		WeChat
	OR

Test status

Essentials

Type	Status
Fast test
Full linux
Full windows

Training services

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

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.

License

The entire codebase is under MIT license