Learn JAX with Real Code Examples

JIT-compiled functions are orders of magnitude faster than pure Python

Vectorization with `vmap` eliminates explicit Python loops

Parallelization with `pmap` scales efficiently across devices

Avoid frequent device-host memory transfers for efficiency

Profiling with `jax.profiler` helps identify bottlenecks

Ensure data privacy before sending to GPU/TPU

Avoid using untrusted code in JIT-compiled functions

Validate inputs for scientific simulations to prevent invalid states

Follow secure ML pipeline best practices

Encrypt sensitive datasets if using cloud TPUs

Log training and simulation metrics

Monitor computation performance with profiling tools

Track device utilization for GPU/TPU

Visualize loss and accuracy curves

Audit function outputs for correctness

Use functional, side-effect-free functions

Document all transformation pipelines

Test gradients and compiled functions

Version scripts and parameters

Ensure reproducibility with PRNG keys