Package: xnni
简体中文NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.NNIv0.7has been released!Supported FrameworksTuning AlgorithmsTraining ServicesPyTorchTensorFlowKerasMXNetCaffe2CNTK (Python language)ChainerTheanoTunerTPERandom SearchAnnealNaive EvolutionSMACBatchGrid SearchHyperbandNetwork MorphismENASMetis TunerBOHBAssessorMedian StopCurve FittingLocal MachineRemote ServersOpenPAIKubeflowFrameworkController on K8S (AKS etc.)Who should consider using NNIThose who want to try different AutoML algorithms in their training code (model) at their local machine.Those who want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud).Researchers and data scientists who want to implement their own AutoML algorithms and compare it with other algorithms.ML Platform owners who want to support AutoML in their platform.Related 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.
We encourage researchers and students leverage these projects to accelerate the AI development and research.Install & VerifyIf you choose NNI Windows local mode and you use powershell to run script for the first time, you need torun powershell as administratorwith this command first:Set-ExecutionPolicy-ExecutionPolicyUnrestrictedInstall through pipWe support Linux, MacOS and Windows(local mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the followingpip installin an environment that haspython >= 3.5.Linux and MacOSpython3-mpipinstall--upgradenniWindowspython-mpipinstall--upgradenniNote:--usercan be added if you want to install NNI in your home directory, which does not require any special privileges.Currently NNI on Windows only support local mode. Anaconda is highly recommanded to install NNI on Windows.If there is any error likeSegmentation fault, please refer toFAQInstall through source codeWe support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows local mode (10.1809) in our current stage.Linux and MacOSRun the following commands in an environment that haspython >= 3.5,gitandwget.gitclone-bv0.7https://github.com/Microsoft/nni.gitcdnnisourceinstall.shWindowsRun the following commands in an environment that haspython >=3.5,gitandpowershellgitclone-bv0.7https://github.com/Microsoft/nni.gitcdnnipowershell./install.ps1For the system requirements of NNI, please refer toInstall NNIFor NNI Windows local mode, please refer toNNI Windows local modeVerify installThe following example is an experiment built on TensorFlow. Make sure you haveTensorFlow installedbefore running it.Download the examples via clone the source code.gitclone-bv0.7https://github.com/Microsoft/nni.gitLinux and MacOSRun the mnist example.nnictlcreate--confignni/examples/trials/mnist/config.ymlWindowsRun the mnist example.nnictlcreate--confignni/examples/trials/mnist/config_windows.ymlWait for the messageINFO: Successfully started experiment!in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using theWeb 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 theWeb UI urlin your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below.Hereare more Web UI pages.DocumentationNNI overviewQuick startHow toInstall NNIUse command line tool nnictlUse NNIBoardHow to define search spaceHow to define a trialHow to choose tuner/search-algorithmConfig an experimentHow to use annotationTutorialsRun an experiment on local (with multiple GPUs)?Run an experiment on multiple machines?Run an experiment on OpenPAI?Run an experiment on Kubeflow?Try different tunersTry different assessorsImplement a customized tunerImplement a customized assessorUse Genetic Algorithm to find good model architectures for Reading Comprehension taskContributeThis project welcomes contributions and suggestions, we useGitHub issuesfor tracking requests and bugs.Issues with thegood first issuelabel are simple and easy-to-start ones that we recommend new contributors to start with.To set up environment for NNI development, refer to the instruction:Set up NNI developer environmentBefore start coding, review and get familiar with the NNI Code Contribution Guideline:ContributingWe are in construction of the instruction forHow to Debug, you are also welcome to contribute questions or suggestions on this area.LicenseThe entire codebase is underMIT license