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(GPU\u002FCPU)","",null,"Rust",5540,269,178,26,0,1,38.29,"Apache License 2.0",false,"master",true,[],"2026-06-12 02:01:13","# Leaf • [![Join the chat at https:\u002F\u002Fgitter.im\u002Fautumnai\u002Fleaf](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fgitter-join%20chat-brightgreen.svg)](https:\u002F\u002Fgitter.im\u002Fautumnai\u002Fleaf?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Build Status](https:\u002F\u002Ftravis-ci.org\u002Fautumnai\u002Fleaf.svg?branch=master)](https:\u002F\u002Ftravis-ci.org\u002Fautumnai\u002Fleaf) [![Crates.io](http:\u002F\u002Fmeritbadge.herokuapp.com\u002Fleaf)](https:\u002F\u002Fcrates.io\u002Fcrates\u002Fleaf) [![License](https:\u002F\u002Fimg.shields.io\u002Fcrates\u002Fl\u002Fleaf.svg)](LICENSE)\n\n## Introduction\n\nLeaf is a open Machine Learning Framework for hackers to build classical, deep\nor hybrid machine learning applications. It was inspired by the brilliant people\nbehind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings\nmodularity, performance and portability to deep learning.\n\nLeaf has one of the simplest APIs, is lean and tries to introduce minimal\ntechnical debt to your stack.\n\nSee the [Leaf - Machine Learning for Hackers][leaf-book] book for more.\n\nLeaf is a few months old, but thanks to its architecture and Rust, it is already\none of the fastest Machine Intelligence Frameworks available.\n\n\n> See more Deep Neural Networks benchmarks on [Deep Learning Benchmarks][deep-learning-benchmarks-website].\n\nLeaf is portable. Run it on CPUs, GPUs, and FPGAs, on machines with an OS, or on\nmachines without one. Run it with OpenCL or CUDA. Credit goes to\n[Collenchyma][collenchyma] and Rust.\n\nLeaf is part of the [Autumn][autumn] Machine Intelligence Platform, which is\nworking on making AI algorithms 100x more computational efficient.\n\nWe see Leaf as the core of constructing high-performance machine intelligence\napplications. Leaf's design makes it easy to publish independent modules to make\ne.g. deep reinforcement learning, visualization and monitoring, network\ndistribution, [automated preprocessing][cuticula] or scaleable production\ndeployment easily accessible for everyone.\n\n[caffe]: https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\n[rust]: https:\u002F\u002Fwww.rust-lang.org\u002F\n[tensorflow]: https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\n[benchmarks]: #benchmarks\n[leaf-examples]: #examples\n[documentation]: http:\u002F\u002Fautumnai.github.io\u002Fleaf\n\n> Disclaimer: Leaf is currently in an early stage of development.\n> If you are experiencing any bugs with features that have been\n> implemented, feel free to create a issue.\n\n## Getting Started\n\n### Documentation\n\nTo learn how to build classical, deep or hybrid machine learning applications with Leaf, check out the [Leaf - Machine Learning for Hackers][leaf-book] book.\n\nFor additional information see the [Rust API Documentation][documentation] or the [Autumn Website][autumn].\n\nOr start by running the **Leaf examples**.\n\nWe are providing a [Leaf examples repository][leaf-examples], where we and\nothers publish executable machine learning models build with Leaf. It features\na CLI for easy usage and has a detailed guide in the [project\nREADME.md][leaf-examples].\n\nLeaf comes with an examples directory as well, which features popular neural\nnetworks (e.g. Alexnet, Overfeat, VGG). To run them on your machine, just follow\nthe install guide, clone this repoistory and then run\n\n```bash\n# The examples currently require CUDA support.\ncargo run --release --no-default-features --features cuda --example benchmarks alexnet\n```\n\n[leaf-examples]: https:\u002F\u002Fgithub.com\u002Fautumnai\u002Fleaf-examples\n\n### Installation\n\n> Leaf is build in [Rust][rust]. If you are new to Rust you can install Rust as detailed [here][rust_download].\nWe also recommend taking a look at the [official Rust - Getting Started Guide][rust_getting_started].\n\nTo 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`:\n\n```toml\n[dependencies]\nleaf = \"0.2.1\"\n```\n\n[rust_download]: https:\u002F\u002Fwww.rust-lang.org\u002Fdownloads.html\n[rust_getting_started]: https:\u002F\u002Fdoc.rust-lang.org\u002Fbook\u002Fgetting-started.html\n[cargo-edit]: https:\u002F\u002Fgithub.com\u002Fkillercup\u002Fcargo-edit\n\nIf you are on a machine that doesn't have support for CUDA or OpenCL you\ncan selectively enable them like this in your `Cargo.toml`:\n\n```toml\n[dependencies]\nleaf = { version = \"0.2.1\", default-features = false }\n\n[features]\ndefault = [\"native\"] # include only the ones you want to use, in this case \"native\"\nnative  = [\"leaf\u002Fnative\"]\ncuda    = [\"leaf\u002Fcuda\"]\nopencl  = [\"leaf\u002Fopencl\"]\n```\n\n> More information on the use of feature flags in Leaf can be found in [FEATURE-FLAGS.md](.\u002FFEATURE-FLAGS.md)\n\n### Contributing\n\nIf you want to start hacking on Leaf (e.g.\n  [adding a new `Layer`](http:\u002F\u002Fautumnai.com\u002Fleaf\u002Fbook\u002Fcreate-new-layer.html))\nyou should start with forking and cloning the repository.\n\nWe have more instructions to help you get started in the [CONTRIBUTING.md][contributing].\n\nWe also has a near real-time collaboration culture, which happens\nhere on Github and on the [Leaf Gitter Channel][gitter-leaf].\n\n> 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.\n\n[contributing]: CONTRIBUTING.md\n[gitter-leaf]: https:\u002F\u002Fgitter.im\u002Fautumnai\u002Fleaf\n[mj]: https:\u002F\u002Ftwitter.com\u002Fmjhirn\n[hobofan]: https:\u002F\u002Ftwitter.com\u002Fhobofan\n[irc]: https:\u002F\u002Fchat.mibbit.com\u002F?server=irc.mozilla.org&channel=%23rust-machine-learning\n\n## Ecosystem \u002F Extensions\n\nWe designed Leaf and the other crates of the [Autumn Platform][autumn] to be as modular\nand extensible as possible. More helpful crates you can use with Leaf:\n\n- [**Cuticula**][cuticula]: Preprocessing Framework for Machine Learning\n- [**Collenchyma**][collenchyma]: Portable, HPC-Framework on any hardware with CUDA, OpenCL, Rust\n\n[cuticula]: https:\u002F\u002Fgithub.com\u002Fautumnai\u002Fcuticula\n[collenchyma]: https:\u002F\u002Fgithub.com\u002Fautumnai\u002Fcollenchyma\n\n## Support \u002F Contact\n\n- With a bit of luck, you can find us online on the #rust-machine-learning IRC at irc.mozilla.org,\n- but we are always approachable on [Gitter\u002FLeaf][gitter-leaf]\n- For bugs and feature request, you can create a [Github issue][leaf-issue]\n- For more private matters, send us email straight to our inbox: developers@autumnai.com\n- Refer to [Autumn][autumn] for more information\n\n[leaf-issue]: https:\u002F\u002Fgithub.com\u002Fautumnai\u002Fleaf\u002Fissues\n\n## Changelog\n\nYou can find the release history at the [CHANGELOG.md][changelog]. We are using [Clog][clog], the Rust tool for auto-generating CHANGELOG files.\n\n[changelog]: CHANGELOG.md\n[Clog]: https:\u002F\u002Fgithub.com\u002Fclog-tool\u002Fclog-cli\n\n## License\n\nLicensed under either of\n\n * Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0)\n * MIT license ([LICENSE-MIT](LICENSE-MIT) or http:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT)\n\nat your option.\n","Leaf 是一个为开发者设计的开源机器智能框架，支持构建经典、深度或混合型机器学习应用。该项目采用 Rust 语言编写，具有简洁的 API 和高性能特性，能够运行在 CPU、GPU 甚至 FPGA 上，并且支持 OpenCL 或 CUDA 加速计算。Leaf 的设计注重模块化和可移植性，适合需要快速开发高效机器学习模型的场景，如深度强化学习、可视化监控及大规模生产部署等。尽管 Leaf 目前仍处于早期开发阶段，但其架构优势使其成为构建高性能机器智能应用的理想选择之一。",2,"2026-06-11 03:04:36","top_language"]