Leaf: Open Machine Intelligence Framework for Hackers

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.

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 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.

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"]

Read more about it.

Ehacking Staff
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