jax

What problem does it solve?

This Skill enables high-performance scientific simulations and machine learning research by providing composable function transformations for automatic differentiation, JIT compilation, and parallelization across CPUs, GPUs, and TPUs.

Core Features & Use Cases

• Automatic Differentiation: Compute gradients, Hessians, and higher-order derivatives of complex functions.
• Just-In-Time (JIT) Compilation: Compile Python/NumPy code to highly optimized machine code for significant speedups.
• Vectorization (vmap): Easily apply functions across batches of data without manual reshaping.
• Parallelization (pmap): Distribute computations across multiple devices.
• Use Case: Accelerate a physics simulation by using jax.jit and jax.grad to compute derivatives and optimize parameters, or train a deep learning model faster on a TPU using jax.pmap.

Quick Start

Use the jax skill to define a function f(x) = jnp.sin(x) + x**2, then compute its gradient using grad(f).