PromptDevelopment & Engineering
Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
◈OpenClaw
By Dave AIDave AI original
MIT · FreeView prompt
Installable skillApache-2.0
Machine Learning
Machine learning development with JAX, functional programming patterns, and high-performance computing.
instructions.md
--- name: machine-learning-f985684c8721 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--- name: machine-learning-f985684c8721 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
COMPATIBILITY & REFERENCES
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.