Lux.jl

Lux.jl is a lightweight and extensible deep learning framework in Julia designed for speed, composability, and clarity. Unlike traditional machine learning libraries that bundle training logic and models, Lux separates model definitions from training routines, encouraging modularity and ease of experimentation. It integrates seamlessly with SciML and other Julia packages, supporting neural differential equations and scientific machine learning workflows.

Features

Purely functional and composable model definitions
Decouples model creation from training logic
High-performance execution using GPU/CPU backends
Supports neural differential equations via SciML
Flexible architecture for custom layers and optimizers
Compatible with JAX-style transformations like Zygote and Optimisers.jl

Project Samples

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License

MIT License

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Julia

Related Categories

Julia Deep Learning Frameworks

Registered

2025-07-21

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