All Tools
E
Dev ToolsFreeOpen Source
EQUINOX
Elegant neural networks and scientific computing in JAX
Apache-2.0
ABOUT
JAX is powerful for high-performance numerical computing but its functional programming model makes building neural networks complex — requiring manual parameter management, custom training loops, and deep understanding of JAX transforms. Equinox provides a PyTorch-like API on top of JAX with clean parameter handling, composable modules, and seamless support for JIT compilation, vmap, and grad — making JAX accessible for both deep learning research and scientific machine learning without sacrificing performance or flexibility.
INSTALL
pip install equinoxINTEGRATION GUIDE
1. Build and train custom neural network architectures in JAX with clean, maintainable module code
2. Implement scientific machine learning models combining neural networks with differential equations
3. Accelerate ML research with composable modules that leverage JAX's JIT, autodiff, and hardware acceleration
4. Create reproducible experiments with Equinox's serialization and parameter management system
5. Develop production ML pipelines that require the performance of JAX with the ergonomics of PyTorch-style APIs
TAGS
pythonjaxdeep-learningneural-networksscientific-computingmlgpu