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LuxDL Docs

Elegant & Performant Scientific Machine Learning in JuliaLang

A Pure Julia Deep Learning Framework designed for Scientific Machine Learning

Tutorials
Ecosystem
API Reference 📚
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Lux.jl
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Fast & Extendible

Lux.jl is written in Julia itself, making it extremely extendible. CUDA and AMDGPU are supported first-class, with experimental support for Metal Hardware.

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SciML ❤️ Lux

Lux is the default choice for many SciML packages, including DiffEqFlux.jl, NeuralPDE.jl, and more.

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Uniquely Composable

Lux.jl natively supports Arbitrary Parameter Types, making it uniquely composable with other Julia packages (and even Non-Julia packages).

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Well Tested

Lux.jl tests every supported Automatic Differentiation Framework with every supported hardware backend against Finite Differences to prevent sneaky 🐛 in your code.

How to Install Lux.jl? ​

Its easy to install Lux.jl. Since Lux.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:

julia
julia> using Pkg
julia> Pkg.add("Lux")

If you want to use the latest unreleased version of Lux.jl, you can run the following command: (in most cases the released version will be same as the version on github)

julia
julia> using Pkg
julia> Pkg.add(url="https://github.com/LuxDL/Lux.jl")

Want GPU Support? ​

Install the following package(s):

julia
using Pkg
Pkg.add("LuxCUDA")
# or
Pkg.add(["CUDA", "cuDNN"])
julia
using Pkg
Pkg.add("AMDGPU")
julia
using Pkg
Pkg.add("Metal")
julia
using Pkg
Pkg.add("oneAPI")