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Jax.jit

As explained in Deep Learning with Python, Third Edition

A JAX decorator that compiles a function with XLA for faster execution.

Use @jax.jit when a stateless JAX function needs faster execution. The decorator turns the function into XLA-compiled code, typically providing a considerable speedup. section 3.5.3

Compile a dense layer

@jax.jit def dense(inputs, W, b): return jax.nn.relu(jnp.matmul(inputs, W) + b)

Keep one rule in mind:

  • Decorate only stateless functions; tensors updated inside the function must appear among its return values. section 3.5.3

How Deep Learning with Python, Third Edition covers it

How deep it goes
Explains the decorator’s purpose and state constraint, but does not detail XLA compilation internals.
Examples
Uses a jitted dense layer combining matrix multiplication, bias addition, and ReLU.
What it assumes
Requires recognizing stateless functions and returning any tensors updated during execution.
What you can do after
Apply it via the @jax.jit decorator to compile a stateless JAX function.
How it connects
Connects JAX functions with XLA and TensorFlow’s tf.function(jit_compile=True).
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