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machine-learning

Machine learning development with JAX, functional programming patterns, and high-performance computing.

bouclem/skills
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含まれるファイル（6）

  • SKILL.md1.7 KB
  • assets/config.yaml699 B
  • assets/schema.json1.2 KB
  • references/GUIDE.md1.9 KB
  • references/PATTERNS.md1.5 KB
  • scripts/validate.py3.7 KB

SKILL.md（原文）

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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

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