Can this skill help identify memory leaks?

Yes, it includes techniques for using memory_profiler to track allocations and implementation patterns like __slots__ to significantly reduce the memory footprint of objects.

Can I use these techniques on a running production script?

Yes, the skill includes guidance on using py-spy, which allows for non-intrusive profiling of running Python processes without needing to restart the application.

Does this require external libraries like NumPy?

While it covers NumPy for numerical speedups, it also provides extensive optimizations for pure Python, such as local variable caching and efficient string concatenation.

How do I profile CPU usage in Python with this skill?

This skill provides specific patterns for using cProfile and py-spy to identify time-consuming functions and visualize execution paths using flame graphs.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

分析と監視

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory usage, and improve application latency.

概要

This skill provides comprehensive guidance for diagnosing and resolving performance issues in Python applications. It equips developers with patterns for CPU and memory profiling using industry-standard tools like cProfile and py-spy, alongside practical implementation strategies such as list comprehensions, generator expressions, and NumPy vectorization. Whether you are debugging a slow production process or optimizing a high-throughput data pipeline, this skill helps you implement high-performance algorithms and efficient resource management practices.

主な機能

Benchmarking workflows using timeit for precise execution measurement
Advanced optimization patterns including LRU caching and NumPy vectorization
Comprehensive CPU and memory profiling using cProfile, line_profiler, and py-spy
0 GitHub stars
Memory-efficient data handling techniques with generator expressions and __slots__
Identification and resolution of common bottlenecks like global variable access and call overhead

ユースケース

Refactoring legacy Python code to improve latency and system response times
Optimizing slow-running data processing pipelines and CPU-intensive operations
Diagnosing and fixing memory leaks in long-running production applications

概要

主な機能

Benchmarking workflows using timeit for precise execution measurement
Advanced optimization patterns including LRU caching and NumPy vectorization
Comprehensive CPU and memory profiling using cProfile, line_profiler, and py-spy
0 GitHub stars
Memory-efficient data handling techniques with generator expressions and __slots__
Identification and resolution of common bottlenecks like global variable access and call overhead

ユースケース

Refactoring legacy Python code to improve latency and system response times
Optimizing slow-running data processing pipelines and CPU-intensive operations
Diagnosing and fixing memory leaks in long-running production applications