Jax Best Practices

---
name: jax-best-practices
description: Expert in JAX for high-performance numerical computing and machine learning
---

# 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