Which Python profiling tools does this skill support?

The skill provides patterns for cProfile, line_profiler, memory_profiler, and py-spy, covering CPU, memory, and line-level analysis.

Can this skill help with Python memory leaks?

Yes, it includes specific patterns for tracking memory allocation over time and using memory-efficient structures like generators and __slots__ to reduce overhead.

How do I optimize numerical calculations using this skill?

The skill demonstrates how to replace standard Python loops with vectorized NumPy operations, which can result in significant speedups for mathematical tasks.

Does it offer advice on algorithmic improvements?

Absolutely. It compares various data structures and implementation patterns, such as dictionary lookups versus list searches, to ensure O(1) complexity where possible.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

분석 및 모니터링

Profiles and optimizes Python codebases to eliminate execution bottlenecks and reduce resource consumption.

소개

This skill provides a comprehensive toolkit for identifying and resolving performance issues within Python applications. It guides users through various levels of analysis, from high-level CPU profiling with cProfile to granular line-by-line and memory usage tracking. By implementing specialized patterns such as LRU caching, generator expressions, and NumPy vectorization, this skill enables developers to significantly reduce latency and memory footprints in data pipelines, web services, and computationally intensive scripts.

주요 기능

Production-safe profiling using py-spy for running processes
Line-by-line execution analysis for granular bottleneck detection
Memory optimization strategies including __slots__ and generator expressions
Numerical performance gains via NumPy vectorization and native extensions
Comprehensive CPU and memory profiling with cProfile and memory_profiler
0 GitHub stars

사용 사례

Optimizing memory-heavy data processing and ETL pipelines
Identifying and fixing memory leaks in long-running background tasks
Reducing response times and latency in high-traffic Python APIs

소개

주요 기능

Production-safe profiling using py-spy for running processes
Line-by-line execution analysis for granular bottleneck detection
Memory optimization strategies including __slots__ and generator expressions
Numerical performance gains via NumPy vectorization and native extensions
Comprehensive CPU and memory profiling with cProfile and memory_profiler
0 GitHub stars

사용 사례

Optimizing memory-heavy data processing and ETL pipelines
Identifying and fixing memory leaks in long-running background tasks
Reducing response times and latency in high-traffic Python APIs