Embedded Learning Library-嵌入式机器学习库-微软2

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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.

We built ELL for makers, technology enthusiasts, students, entrepreneurs, and developers who aspire to build intelligent devices and AI-powered gadgets. Our tools, our code, and all of the other resources available on this website are free for anyone to adapt and use (for details, see licensing below). Just keep in mind that ELL is a work in progress and that we expect it change rapidly, including breaking API changes.

ELL is a software library and an accompanying set of software tools, written in modern C++, with an optional inte**ce in Python. Download ELL from our GitHub repository, either as a zip file, or with the following command:

git clone https://github.com/Microsoft/ELL.git
While the goal of ELL is to deploy software onto resource constrained platforms and small single-board computers, most of the interaction with ELL occurs on a laptop or desktop computer (Windows, Ubuntu Linux, or macOS). Technically, you can think of ELL as a cross-compiler for embedded intelligence - the compiler itself runs on your laptop or desktop computer and the machine code that it generates runs on your single-board computer.

Installation and Setup
Install ELL on a Windows, Ubuntu Linux, or macOS laptop or desktop computer. If you intend to deploy models onto a Raspberry Pi, follow our instruction on Raspberry Pi Setup.

Getting Started
A great place to start is our tutorials section. As we develop and release new functionality in ELL, we also publish tutorials that showcase that functionality. Currently, our tutorials are focused on ** embedded computer vision tasks on Raspberry Pi, but we expect the scope to grow with time. Have fun!

The ELL Gallery
Our gallery is a collection of bits and pieces that you can download and use in your projects. Currently, the gallery includes a handful of pre-trained computer vision models, some keyword spotting audio models, and instructions for 3D printing an active cooling attachment for your Raspberry Pi.

License
The ELL code and sample code in our tutorials are released under the MIT Open Source License. Some of the other content on this website, such as the 3D models in our gallery, are released under the Creative Commons Attribution 4.0 license.

Conduct and Privacy
ELL has adopted the Microsoft Open Source Code of Conduct. For more information on this code of conduct, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments. Read Microsoft’s statement on Privacy & Cookies.

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嵌入式学习库(ELL)允许您在资源受限的平台和小型单板计算机(如Raspberry Pi、Arduino和micro:bit)上设计和部署智能机器学习模型。部署的模型在本地运行，不需要网络连接，也不依赖云中的服务器。ELL是微软研究院开发的嵌入式人工智能和机器学习技术的早期预览版。
我们为制造商、技术爱好者、学生、企业家和渴望构建智能设备和人工智能驱动设备的开发人员构建了ELL。我们的工具、代码和本网站上的所有**资源都是免费的，任何人都可以修改和使用(有关详细信息，请参阅下面的许可协议)。请记住，ELL是一项正在进行的工作，我们希望它能够快速更改，包括打破API更改。
ELL是一个软件库和一组附带的软件工具，用现代c++编写，带有Python的可选接口。下载ELL从我们的GitHub存储库，要么作为zip文件，要么使用以下命令:
git clone https://github.com/Microsoft/ELL.git

虽然ELL的目标是将软件部署到资源受限的平台和小型单板计算机上，但与ELL的大部分交互发生在笔记本或台式机(Windows、Ubuntu Linux或macOS)上。从技术上讲，您可以将ELL看作是嵌入式智能的交叉编译器——编译器本身运行在您的笔记本电脑或台式机上，而它生成的机器码运行在您的单板计算机上。