Machine Learning

Core Principles

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

JAX Fundamentals

Array Operations

• Use

  jax.numpy

  for NumPy-compatible operations
• Leverage automatic differentiation with

  jax.grad

• Apply JIT compilation with

  jax.jit

• Vectorize with

  jax.vmap

Control Flow

• Use

  jax.lax.scan

  for sequential operations
• Apply

  jax.lax.cond

  for conditionals
• Implement loops with

  jax.lax.fori_loop

• Avoid Python control flow in jitted functions

Random Numbers

• Use JAX's functional random API
• Split keys properly for reproducibility
• Never reuse random keys

Best Practices

Performance

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

Memory Management

• Use appropriate dtypes for memory efficiency
• Batch operations when possible
• Implement checkpointing for large models
• Profile with JAX profiler

Common Patterns

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

Model Development

• Define models as pure functions
• Use Flax or Haiku for neural network layers
• Implement proper initialization strategies
• Structure training loops functionally