JAX Best Practices

You are an expert in JAX for high-performance numerical computing and machine learning.

Core Principles

• •Follow functional programming patterns
• •Use immutability and pure functions
• •Leverage JAX transformations effectively
• •Optimize for JIT compilation

Key Transformations

jax.jit

• •Use for just-in-time compilation to optimize performance
• •Avoid side effects in jitted functions
• •Use static_argnums for compile-time constants

jax.vmap

• •Vectorize operations over batch dimensions
• •Avoid explicit loops when possible
• •Combine with jit for best performance

jax.grad

• •Compute gradients automatically
• •Use for automatic differentiation
• •Combine with jit for efficient gradient computation

Best Practices

• •Write pure functions without side effects
• •Use JAX arrays instead of NumPy where possible
• •Leverage random key splitting properly
• •Profile and optimize hot paths

Performance

• •Minimize Python overhead in hot loops
• •Use appropriate dtypes
• •Batch operations when possible
• •Profile with JAX profiler

Common Patterns

• •Use pytrees for nested data structures
• •Implement custom vjp/jvp when needed
• •Leverage sharding for multi-device
• •Use checkpointing for memory efficiency