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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
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Categories
Deep Learning FrameworksLicense
MIT LicenseFollow Lux.jl
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