machine-learning
Machine learning development with JAX, functional programming patterns, and high-performance computing.
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Security assessment
Risk score
0/100
Recommendation
CAUTION
Severity
LOW
Savant decision
Passed (low risk)
The assessment recommended caution, but its risk score is below 20/100, so Savant's policy passes it. Its findings are still listed for review. Static security analysis completed.
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SKILL.md
--- name: machine-learning description: Machine learning development with JAX, functional programming patterns, and high-performance computing. --- # Machine Learning You are an expert in machine learning development with JAX and functional programming patterns. ## 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