leaf

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.

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 or the [Autumn Website][autumn].

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 support.
cargo run --release --no-default-features --features cuda --example benchmarks alexnet

Installation

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

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:

[dependencies]
leaf = "0.2.1"

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

[dependencies]
leaf = { version = "0.2.1", default-features = false }

[features]
default = ["native"] # include only the ones you want to use, in this case "native"
native  = ["leaf/native"]
cuda    = ["leaf/cuda"]
opencl  = ["leaf/opencl"]

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.

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.

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: Preprocessing Framework for Machine Learning
• Collenchyma: Portable, HPC-Framework on any hardware with CUDA, OpenCL, Rust

Support / Contact

• 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/Leaf
• For bugs and feature request, you can create a Github issue
• For more private matters, send us email straight to our inbox: developers@autumnai.com
• Refer to [Autumn][autumn] for more information

Changelog

You can find the release history at the CHANGELOG.md. We are using Clog, the Rust tool for auto-generating CHANGELOG files.

License

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.