Py Performance Tuning

Advanced techniques for profiling and optimizing Python code for speed and memory.

jcorpac a4bf953 1012 B Updated

File contents

Python Performance Tuning

Optimization should always follow measurement. This skill focuses on a data-driven approach to making Python go fast.

The Performance Loop

  1. Measure: Profile with cProfile or Pyinstrument.
  2. Analyze: Identify bottlenecks (CPU-bound vs. I/O-bound).
  3. Optimize: Apply targeted fixes.
  4. Verify: Re-profile to confirm gains.

Optimization Strategies

  • Vectorization: Use NumPy/Pandas to replace explicit loops.
  • Just-in-Time (JIT): Use Numba for numerical heavy-lifting.
  • Compiled Extensions: Write performance-critical modules in Cython or Rust (PyO3).
  • Algorithmic Fixes: Check O(n) complexity before micro-optimizing.

Tools

  • timeit: For micro-benchmarking snippets.
  • memory_profiler: For tracking memory leaks and peaks.
  • scalene: A high-performance CPU, GPU, and memory profiler.

jcorpac/ai-skills-library/tree/main/py/py-performance-tuning commit a4bf953b72

Frequently asked questions

npx skillmds@latest add jcorpac/py-performance-tuning