Overview
JAX is a high-performance Python library for accelerator-oriented array computation and composable transformations (automatic differentiation, JIT compilation, and vectorization). It enables researchers and engineers to scale NumPy-style code to GPUs and TPUs, providing a powerful foundation for model research and high-performance numerical computing.
Key features
- Automatic differentiation (grad) and higher-order derivatives.
- JIT compilation via XLA for efficient GPU/TPU execution.
- Vectorization (vmap), batching and sharding primitives for scaling.
- NumPy-compatible APIs and an ecosystem with jaxlib, Flax, and Optax.
Use cases
- Research and experimentation in optimization, training algorithms and scientific computing.
- Accelerating NumPy workloads on modern accelerators.
- Building differentiable systems and high-performance numerical pipelines.
Technical notes
JAX centers on composable function transformations. Combining grad, jit and vmap yields concise, high-performance implementations. It uses XLA as the backend and is widely adopted in research and production for workloads requiring fine-grained control over performance.