We will discuss the {func}`jax.jit` transformation, which will perform *Just In Time* (JIT) compilation of a JAX Python function so it can be executed efficiently in XLA. ## How JAX transformations work In the previous section, we discussed that JAX allows us to transform Python functions.

Custom derivative rules for JAX-transformable Python functions; Control autodiff’s saved values with jax.checkpoint (aka jax.remat) How JAX primitives work; Writing custom Jaxpr interpreters in JAX; Custom operations for GPUs with C++ and CUDA; Generalized Convolutions in JAX; Developer Documentation. Contributing to JAX; Building from source ...

263: JAX PRNG Design; 2026: Custom JVP/VJP rules for JAX-transformable functions; 4008: Custom VJP and `nondiff_argnums` update; 4410: Omnistaging; 9263: Typed keys & pluggable RNGs; 9407: Design of Type Promotion Semantics for JAX; 9419: Jax and Jaxlib versioning; 10657: Sequencing side-effects in JAX; 11830: `jax.remat` / `jax.checkpoint` new ...

To capture a device memory profile to disk, use {func}`jax.profiler.save_device_memory_profile`.

JAX debugging flags; GPU performance tips; Persistent compilation cache; Pytrees; Errors; Ahead-of-time lowering and compilation; Exporting and serialization. Exporting and serializing staged-out computations; Shape polymorphism; Interoperation with TensorFlow; Type promotion semantics; Transfer guard; External callbacks; Pallas: a JAX kernel ...

18137: Scope of JAX NumPy & SciPy Wrappers; 25516: Effort-based versioning; Extension guides. Writing custom Jaxpr interpreters in JAX; Custom operations for GPUs with C++ and CUDA; jax.extend module. jax.extend.core module; jax.extend.ffi module; jax.extend.linear_util

Custom derivative rules for JAX-transformable Python functions; Control autodiff’s saved values with jax.checkpoint (aka jax.remat) Generalized convolutions in JAX; Developer notes. Contributing to JAX; Building from source; Investigating a regression; Autodidax: JAX core from scratch; JAX Enhancement Proposals (JEPs) 263: JAX PRNG Design

Given a Python function that evaluates $f$, JAX's `jvp` is a way to get a Python function for evaluating $(x, v) \\mapsto (f(x), \\partial f(x) v)$."

How JAX primitives work; Understanding Jaxprs; Writing custom Jaxpr interpreters in JAX; Custom operations for GPUs with C++ and CUDA; jax.extend module. jax.extend.ffi module; jax.extend.linear_util module; jax.extend.mlir module; jax.extend.random module; Building on JAX; Notes. API compatibility; Python and NumPy version support policy; jax ...

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