Jax.jit
jax.jit is a JAX transformation that compiles a function, typically through XLA, for optimized execution.
Deep Learning with R, Third Edition
jax$jit() is a JAX transformation that compiles a stateless function for optimized execution, typically through XLA.
- How it is used
- Accelerates stateless JAX functions by compiling them through XLA.
- How deep it goes
- Defines compilation behavior and state constraints, then stops short of explaining XLA internals.
- Examples
- Demonstrates JIT-compiling a ReLU dense layer using matrix multiplication, weights, bias, and explicit arguments.
- What you can do after
- Apply jax.jit() to accelerate a stateless JAX function while returning updated tensors explicitly.
Read how Deep Learning with R, Third Edition explains jax.jit →
Deep Learning with Python, Third Edition
A JAX decorator that compiles a function with XLA for faster execution.
- How it is used
- Compiles stateless JAX functions to accelerate repeated numerical execution.
- 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 you can do after
- Apply it via the @jax.jit decorator to compile a stateless JAX function.
Read how Deep Learning with Python, Third Edition explains jax.jit →
Related concepts
- tf.functionequivalent to · tf.function is a TensorFlow decorator that traces Python functions into executable TensorFlow graphs, optionally applying compilation optimizations.
- XLAimplements · XLA is a compiler that optimizes and compiles linear-algebra and array computations for supported target hardware.
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