leaf - Open Machine Intelligence Framework for Hackers. (

项目说明

Leaf • ![Join the chat at ]() ![Build Status]() ![Crates.io]() 

Introduction

Leaf is a open Machine Learning 框架 for hackers to build classical, deep
or hybrid machine learning applications. It was inspired by the brilliant people
behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings
modularity, performance and portability to deep learning.

Leaf has one of the simplest APIs, is lean and tries to introduce minimal
technical debt to your stack.

See the [Leaf - Machine Learning for Hackers][leaf-book] book for more.

Leaf is a few months old, but thanks to its architecture and Rust, it is already
one of the fastest Machine Intelligence Frameworks a.

> See more Deep Neural Networks benchmarks on [Deep Learning Benchmarks][deep-learning-benchmarks-website].

Leaf is portable. Run it on CPUs, GPUs, and FPGAs, on machines with an OS, or on
machines without one. Run it with OpenCL or CUDA. Credit goes to
[Collenchyma][collenchyma] and Rust.

Leaf is part of the [Autumn][autumn] Machine Intelligence Platform, which is
working on making AI algorithms 100x more computational efficient.

We see Leaf as the core of constructing high-performance machine intelligence
applications. Leaf's design makes it easy to publish independent modules to make
e.g. deep reinforcement learning, and monitoring, 网络
distribution, [automated preprocessing][cuticula] or scaleable production
deployment easily accessible for e.

[caffe]:
[rust]:
[tensorflow]:
[benchmarks]: #benchmarks
[leaf-examples]: #examples
[documentation]:

> Disclaimer: Leaf is currently in an early stage of de.
> If you are experiencing any bugs with features that have been
> implemented, feel free to create a issue.

Getting Started

Documentation

To learn how to build classical, deep or hybrid machine learning applications with Leaf, check out the [Leaf - Machine Learning for Hackers][leaf-book] book.

For additional information see the [Rust API Documentation][documentation] or the [Autumn Website][autumn].

Or start by running the Leaf examples.

We are pro a [Leaf examples repository][leaf-examples], where we and
others publish executable machine learning models build with Leaf. It features
a CLI for easy usage and has a detailed guide in the [project
README.md][leaf-examples].

Leaf comes with an examples directory as well, which features popular neural
networks (e.g. Alexnet, O, VGG). To run them on your machine, just follow
the install guide, clone this repoistory and then run

[leaf-examples]:

Installation

> Leaf is build in [Rust][rust]. If you are new to Rust you can install Rust as detailed [here][rust_download].
We also recommend taking a look at the [official Rust - Getting Started Guide][rust_getting_started].

To start building a machine learning application (Rust only for now. Wrappers are welcome) and you are using Cargo, just add Leaf to your Cargo.toml:

[rust_download]:
[rust_getting_started]:
[cargo-edit]:

If you are on a machine that doesn't ha for CUDA or OpenCL you
can selectively enable them like this in your Cargo.toml:

> More information on the use of feature flags in Leaf can be found in FEATURE-FLAGS.md

Contributing

If you want to start hacking on Leaf (e.g.
adding a new Layer)
you should start with forking and cloning the repository.

We have more instructions to help you get started in the [CONTRIBUTING.md][contributing].

We also has a near real-time collaboration culture, which happens
here on Github and on the [Leaf Gitter Channel][gitter-leaf].

> Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as below, without any additional terms or conditions.

[contributing]: CONTRIBUTING.md
[gitter-leaf]:
[mj]:
[hobofan]:
[irc]:

Ecosystem / Extensions

We designed Leaf and the other crates of the [Autumn Platform][autumn] to be as modular
and extensible as possible. More helpful crates you can use with Leaf:

[cuticula]:
[collenchyma]:

Support / Contact

[leaf-issue]:

Changelog

You can find the release history at the [CHANGELOG.md][changelog]. We are using [Clog][clog], the Rust 工具 for auto-generating CHANGELOG files.

[changelog]: CHANGELOG.md
[Clog]:

License

Licensed under either of

* Apache License, 2.0, (LICENSE-APACHE or )
* MIT license (LICENSE-MIT or )

at your option.

项目信息

开发语言：Rust

Star 数：5540

技术要点

该项目属于黑客技术方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Open Machine Intelligence 框架 for Hackers. (GPU/CPU)展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。