All Projects → microsoft → Nni

microsoft / Nni

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

Programming Languages

python
139335 projects - #7 most used programming language
typescript
32286 projects
javascript
184084 projects - #8 most used programming language
SCSS
7915 projects
shell
77523 projects
CSS
56736 projects

Projects that are alternatives of or similar to Nni

Hyperactive
A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.
Stars: ✭ 182 (-98.3%)
Mutual labels:  data-science, hyperparameter-optimization, neural-architecture-search, feature-engineering, bayesian-optimization, automated-machine-learning
mindware
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Stars: ✭ 34 (-99.68%)
Mutual labels:  hyperparameter-optimization, feature-engineering, bayesian-optimization, automl, automated-machine-learning
Tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Stars: ✭ 8,378 (-21.69%)
Mutual labels:  data-science, automl, hyperparameter-optimization, feature-engineering, automated-machine-learning
Awesome Automl And Lightweight Models
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
Stars: ✭ 691 (-93.54%)
Mutual labels:  nas, automl, hyperparameter-optimization, neural-architecture-search, model-compression
Auto ml
[UNMAINTAINED] Automated machine learning for analytics & production
Stars: ✭ 1,559 (-85.43%)
Mutual labels:  data-science, automl, hyperparameter-optimization, feature-engineering, automated-machine-learning
Autogluon
AutoGluon: AutoML for Text, Image, and Tabular Data
Stars: ✭ 3,920 (-63.36%)
Mutual labels:  data-science, automl, hyperparameter-optimization, neural-architecture-search, automated-machine-learning
Mljar Supervised
Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Stars: ✭ 961 (-91.02%)
Mutual labels:  data-science, automl, hyperparameter-optimization, feature-engineering, automated-machine-learning
Hpbandster
a distributed Hyperband implementation on Steroids
Stars: ✭ 456 (-95.74%)
Mutual labels:  automl, hyperparameter-optimization, neural-architecture-search, bayesian-optimization, automated-machine-learning
Autodl
Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL [email protected]
Stars: ✭ 854 (-92.02%)
Mutual labels:  data-science, nas, automl, feature-engineering, automated-machine-learning
Featuretools
An open source python library for automated feature engineering
Stars: ✭ 5,891 (-44.93%)
Mutual labels:  data-science, automl, feature-engineering, automated-machine-learning
Lightautoml
LAMA - automatic model creation framework
Stars: ✭ 196 (-98.17%)
Mutual labels:  data-science, automl, feature-engineering, automated-machine-learning
Smac3
Sequential Model-based Algorithm Configuration
Stars: ✭ 564 (-94.73%)
Mutual labels:  automl, hyperparameter-optimization, bayesian-optimization, automated-machine-learning
Auto Sklearn
Automated Machine Learning with scikit-learn
Stars: ✭ 5,916 (-44.7%)
Mutual labels:  automl, hyperparameter-optimization, bayesian-optimization, automated-machine-learning
Lale
Library for Semi-Automated Data Science
Stars: ✭ 198 (-98.15%)
Mutual labels:  data-science, automl, hyperparameter-optimization, automated-machine-learning
Ray
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Stars: ✭ 18,547 (+73.37%)
Mutual labels:  data-science, automl, hyperparameter-optimization, distributed
Auptimizer
An automatic ML model optimization tool.
Stars: ✭ 166 (-98.45%)
Mutual labels:  data-science, automl, hyperparameter-optimization, automated-machine-learning
Hypernets
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
Stars: ✭ 221 (-97.93%)
Mutual labels:  hyperparameter-optimization, nas, automl, neural-architecture-search
Mlbox
MLBox is a powerful Automated Machine Learning python library.
Stars: ✭ 1,199 (-88.79%)
Mutual labels:  data-science, automl, automated-machine-learning, distributed
My Data Competition Experience
本人多次机器学习与大数据竞赛Top5的经验总结,满满的干货,拿好不谢
Stars: ✭ 271 (-97.47%)
Mutual labels:  data-science, automl, hyperparameter-optimization, feature-engineering
Machinejs
[UNMAINTAINED] Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml
Stars: ✭ 412 (-96.15%)
Mutual labels:  data-science, machine-learning-algorithms, automl, automated-machine-learning

MIT licensed Build Status Issues Bugs Pull Requests Version Join the chat at https://gitter.im/Microsoft/nni Documentation Status

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.

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

Installation

Install

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

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 v2.5 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.

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.

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

Join IM discussion groups:

Gitter WeChat
image OR image

Test status

Essentials

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.

License

The entire codebase is under MIT license

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].