What is the best way to optimize recursive functions?

It demonstrates the use of functools.lru_cache to implement memoization, which stores previous results and drastically reduces the execution time of repetitive recursive calls.

How do I identify slow functions in my Python code?

This skill provides patterns for using cProfile to generate detailed reports on function execution time, allowing you to identify exactly where your code spends the most time.

Does it support profiling production applications?

Absolutely. It features patterns for using py-spy, which allows you to profile running Python processes without restarting them or significantly impacting performance.

Can this skill help with memory leaks?

Yes, it includes guides for using memory_profiler to track memory allocation and identify objects that are consuming excessive RAM or failing to be garbage collected.

How can I speed up numerical computations?

The skill offers guidance on switching from pure Python loops to NumPy's vectorized operations, which can often result in 10x-100x speed improvements for mathematical tasks.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

0•

分析と監視

Identifies and resolves performance bottlenecks in Python code using advanced profiling tools and high-efficiency implementation patterns.

This skill provides a comprehensive toolkit for profiling, analyzing, and optimizing Python applications to ensure peak performance. It guides users through the entire optimization lifecycle, from identifying CPU and memory bottlenecks with tools like cProfile and py-spy to implementing low-level performance patterns like generator expressions and __slots__. Whether you are debugging a slow production API, reducing the memory footprint of a data pipeline, or accelerating numerical computations with NumPy, this skill provides the domain-specific best practices and implementation patterns needed to build lightning-fast Python software.

主な機能

01Line-by-line analysis with line_profiler to pinpoint specific slow operations

02Memory usage optimization using generators, __slots__, and memory_profiler

03Computation acceleration through NumPy vectorization and functools caching

04CPU profiling with cProfile and py-spy for production monitoring

05Benchmarking patterns for comparing execution times of different algorithms

060 GitHub stars

ユースケース

01Reducing latency in web applications and API response times

02Optimizing data processing pipelines and large-scale mathematical computations

03Detecting and fixing memory leaks in long-running Python processes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/hermetic-line python-performance-optimization

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