Package: nni
NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in ourofficial documentation(简体中文版点这里).What's NEW!New release:v3.0 preview is available-released on May-5-2022New demo available:Youtube entry|Bilibili 入口-last updated on June-22-2022New research paper:SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute-published in OSDI 2022New research paper:Privacy-preserving Online AutoML for Domain-Specific Face Detection-published in CVPR 2022Newly upgraded documentation:Doc upgradedInstallationSee theNNI installation guideto install from pip, or build from source.To install the current release:$ pip install nniTo update NNI to the latest version, add--upgradeflag to the above commands.NNI capabilities in a glanceHyperparameter TuningNeural Architecture SearchModel CompressionAlgorithmsExhaustive searchGrid SearchRandomHeuristic searchAnnealEvolutionHyperbandPBTBayesian optimizationBOHBDNGOGPMetisSMACTPEMulti-trialGrid SearchPolicy Based RLRandomRegularized EvolutionTPEOne-shotDARTSENASFBNetProxylessNASSPOSPruningLevelL1 NormTaylor FO WeightMovementAGPAuto CompressMore...QuantizationNaiveQATLSQObserverDoReFaBNNSupported FrameworksTraining ServicesTutorialsSupportsPyTorchTensorFlowScikit-learnXGBoostLightGBMMXNetCaffe2More...Local machineRemote SSH serversAzure Machine Learning (AML)Kubernetes BasedOpenAPIKubeflowFrameworkControllerAdaptDLPAI DLCHybrid training servicesHPOPyTorchTensorFlowNASHello NASNAS BenchmarksCompressionPruningPruning SpeedupQuantizationQuantization SpeedupResourcesNNI Documentation HomepageNNI Installation GuideNNI ExamplesPython API ReferenceReleases (Change Log)Related Research and PublicationsYoutube Channel of NNIBilibili Space of NNIWebinar of Introducing Retiarii: A deep learning exploratory-training framework on NNICommunity DiscussionsContribution guidelinesIf you want to contribute to NNI, be sure to review thecontribution guidelines, which includes instructions of submitting feedbacks, best coding practices, and code of conduct.We useGitHub issuesto track tracking requests and bugs.
Please useNNI Discussionfor general questions and new ideas.
For questions of specific use cases, please go toStack Overflow.Participating discussions via the following IM groups is also welcomed.GitterWeChatOROver the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors.
We appreciate all contributions from community to make NNI thrive.Test statusEssentialsTypeStatusFast testFull test - HPOFull test - NASFull test - compressionTraining servicesTypeStatusLocal - linuxLocal - windowsRemote - linux to linuxRemote - windows to windowsOpenPAIFrameworkcontrollerKubeflowHybridAzureMLRelated ProjectsTargeting 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.LicenseThe entire codebase is underMIT license.