Leaf is a open machine learning framework 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 book for more.
Thanks to Leaf's architecture and Rust, it is already one of the fastest machine intelligence frameworks available.
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 Parenchyma and Rust.
Leaf is part of the 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, visualization and monitoring, network distribution, automated preprocessing or scaleable production deployment easily accessible for everyone.
Disclaimer: Leaf is currently in an early stage of development. If you are experiencing any bugs with features that have been implemented, feel free to create a issue.
To learn how to build classical, deep or hybrid machine learning applications with Leaf, check out the Leaf - Machine Learning for Hackers book.
For additional information see the Rust API documentation.
Or start by running the Leaf examples.
We are providing a Leaf examples repository, 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 comes with an examples directory as well, which features popular neural networks (e.g. Alexnet, Overfeat, VGG). To run them on your machine, just follow the install guide, clone this repoistory and then run
# The examples currently require CUDA or OpenCL support.
cargo run --release --no-default-features --features cuda --example benchmarks alexnetLeaf is built in Rust. If you are new to Rust you can install Rust. We also recommend taking a look at the official Rust - Getting Started Guide.
To start building a machine learning application (Rust only for now but wrappers are welcome) and
you are using Cargo, just add Leaf to your Cargo.toml:
[dependencies]
leaf = "<version>"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.
We also has a near real-time collaboration culture, which happens here on Github and on the Leaf Gitter channel.
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.
We designed Leaf and other crates to be as modular and extensible as possible. More helpful crates you can use with Leaf:
CuticulaParenchyma-tr: Preprocessing Framework for Machine LearningCollenchymaParenchyma: Portable, HPC-Framework on any hardware with CUDA, OpenCL, Rust
- With a bit of luck, you can find us online on the #rust-machine-learning IRC at irc.mozilla.org,
- but we are always approachable on Gitter
- For bugs and feature request, you can create a Github issue
- For more private matters, send us email straight to our inbox
You can find the release history at the CHANGELOG.md. We are using Clog, the Rust tool for auto-generating CHANGELOG files.
Licensed under either of
- Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
- MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)
at your option.