description JAX Overview
JAX is a high-performance numerical computing library developed by Google Research. It combines the composability of NumPy with Just-In-Time (JIT) compilation via XLA, automatic differentiation, and vectorization. JAX is designed for high-performance machine learning research, allowing users to write pure Python/NumPy code that executes efficiently on GPUs and TPUs. It has become a favorite for training large-scale models where custom gradients or complex transformations are required.
help JAX FAQ
What does JAX add beyond NumPy?
JAX gives NumPy-like code automatic differentiation, JIT compilation through XLA, and transformations such as vmap and pmap. That makes it useful for machine-learning research where the same math must run fast on CPU, GPU, or TPU.
What is jax.grad used for?
jax.grad creates derivative functions automatically, which is central to training neural networks and optimizing numerical functions. It is one of the reasons researchers use JAX instead of plain NumPy.
Which libraries are commonly used with JAX?
Flax, Haiku, Optax, and Equinox are common parts of the JAX ecosystem. JAX itself gives the transformations and array system, while these libraries provide neural-network modules and optimizer utilities.
Is JAX only for deep learning?
No. JAX is used for deep learning, but also for scientific computing, differentiable simulation, probabilistic modeling, and optimization.
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