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https://github.com/Microsoft/ELL

Embedded Learning Library
https://github.com/Microsoft/ELL

Last synced: 4 months ago
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Embedded Learning Library

Host: GitHub
URL: https://github.com/Microsoft/ELL
Owner: microsoft
License: other
Created: 2016-09-27T19:12:29.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2022-08-30T19:36:19.000Z (almost 2 years ago)
Last Synced: 2024-03-10T02:25:44.151Z (4 months ago)
Language: C++
Homepage: https://microsoft.github.io/ELL
Size: 31.9 MB
Stars: 2,241
Watchers: 164
Forks: 302
Open Issues: 32
Metadata Files:
- Readme: README.md
- Changelog: History.md
- License: LICENSE.txt
- Security: SECURITY.md

Lists

awesome-emdl - Microsoft - ELL - allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit. (Libraries / Inference Framework)
Awesome-Embedded - Embedded Learning Library (ELL) - Microsoft's library to deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers. (Machine Learning & AI on MCU / USB)
awesome-stars - ELL - Embedded Learning Library (C++)
embedded-ai.bi-weekly - Microsoft Embedded Learning Library
awesome-edge-machine-learning - https://github.com/Microsoft/ELL
awesome-iot-master - ELL - allows you to build and deploy machine-learned pipelines onto embedded platforms, like Raspberry Pis, Arduinos, micro:bits, and other microcontrollers. (AI / Tutorial)
awesome_iot - ELL ★ 1859 ⧗ 0 - allows you to build and deploy machine-learned pipelines onto embedded platforms, like Raspberry Pis, Arduinos, micro:bits, and other microcontrollers. (AI / Tutorial)
Awesome-Embeded-AI - Embedded Learning Library (ELL) - Microsoft's library to deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers. (微控制器 MCU 端 / Awesome-Embedded Repository)
edge-ai - Embedded Learning Library (ELL) - Microsoft's library to deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers. (Uncategorized / Uncategorized)
awesome-iot - ELL ★ 1859 ⧗ 0 - allows you to build and deploy machine-learned pipelines onto embedded platforms, like Raspberry Pis, Arduinos, micro:bits, and other microcontrollers. (AI / Tutorial)

README

        # Embedded Learning Library

The Embedded Learning Library (ELL) allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit. The deployed models run locally, without requiring a network connection and without relying on servers in the cloud. ELL is an early preview of the embedded AI and machine learning technologies developed at Microsoft Research.

Go to our [website](https://microsoft.github.io/ELL) for tutorials, instructions, and a gallery of pretrained ELL models for use in your projects.

ELL is a work in progress, and we expect it to change rapidly, including breaking API changes. Despite this code churn, we welcome you to try it and give us feedback.

## License

See [LICENSE.txt](LICENSE.txt).

## Code of conduct

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information on this code of conduct, see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [[email protected]](mailto:[email protected]) with any additional questions or comments.

## Build and Installation Instructions

[![Build Status](https://travis-ci.org/Microsoft/ELL.svg?branch=master)](https://travis-ci.org/Microsoft/ELL)

* [Windows](INSTALL-Windows.md)

* [Ubuntu Linux](INSTALL-Ubuntu.md)

* [Mac OS X](INSTALL-Mac.md)

## Technical Documentation

* [Design overview of `data` library](./libraries/data/doc/README.md)

* [Design overview of `math` library](./libraries/math/doc/README.md)

* [Design overview of `model` and `nodes` libraries](./libraries/model/doc/README.md)

* [Definition of the generalized sparse data format](./libraries/data/doc/GeneralizedSparseFormat.md)

* [Description of the training algorithms implemented in `trainers`](./libraries/trainers/doc/README.md)

* [Style Guide for ELL source code](./StyleGuide.md)