Overview
JAX is Autograd and XLA, brought together for high-performance machine learning research. It provides a familiar NumPy-like API but with the power to run on accelerators like GPUs and TPUs.
Features
- grad: Automatic differentiation of Python functions.
- jit: Just-in-time compilation to XLA for maximum performance.
- vmap: Automatic vectorization for batch processing.
Use Cases
- High-performance physical simulations.
- Modern deep learning research.
- Composable numerical computing.