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