backend-python-performance

Profile Python backend code with cProfile, line_profiler, and memory_profiler.

Updated Nov 11, 2025

Search Tags:#performance #optimization #backend #memory #profiling #FastAPI #Python #CPU

Ready to run

npx skills add https://github.com/shredbx/demo-3d-model --skill backend-python-performance

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backend-python-performance
Source: https://github.com/shredbx/demo-3d-model/tree/main/.claude/skills/backend-python-performance
Command: npx skills add https://github.com/shredbx/demo-3d-model --skill backend-python-performance

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires cProfile, pstats, timeit, line-profiler, memory-profiler, py-spy, numpy, pytest-benchmark.

What problem does it solve?

This Skill equips developers with tools and patterns to identify and eliminate performance bottlenecks in Python applications, reducing latency, memory consumption, and improving overall efficiency.

Core Features & Use Cases

CPU & Memory Profiling: Use cProfile, line_profiler, and memory_profiler to pinpoint slow functions and memory leaks.
Optimization Patterns: Apply techniques like list comprehensions, generators, and lru_cache for significant speedups.
Use Case: A FastAPI endpoint is responding slowly. Use this Skill to profile the endpoint with cProfile, identify the most time-consuming function, and then apply a suitable optimization pattern to improve its performance.

Quick Start

Use the backend-python-performance skill to profile a given Python script using cProfile and identify the top 10 most time-consuming functions.

Frequently Asked Questions about backend-python-performance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I profile a slow Python backend to find performance bottlenecks?▼

Profile your Python backend using cProfile to identify time-consuming functions, then memory-profiler and line-profiler for detailed analysis. Run cProfile on your script to generate call statistics, examine the results with pstats, and focus optimization efforts on the highest-impact functions.

Can I profile FastAPI endpoints to debug slow response times?▼

Yes, profile FastAPI endpoints by wrapping them with cProfile or using py-spy for production profiling. Identify which functions consume the most CPU time, then apply optimization patterns like caching with lru_cache, generators, or list comprehensions to reduce latency.

What's the best way to detect memory leaks in a Python application?▼

Use memory-profiler to track memory consumption line-by-line and identify where memory usage spikes. Combine it with cProfile call graphs to pinpoint functions holding onto memory, then refactor to release resources or optimize data structures.

How do I optimize CPU and memory efficiency in production Python code?▼

Use py-spy for production profiling without code changes, then apply optimization patterns: replace loops with list comprehensions, use generators for large datasets, and cache expensive function calls with lru_cache. Benchmark improvements with pytest-benchmark.

Do I need to modify my code to profile it with these tools?▼

Most tools like cProfile, memory-profiler, and line-profiler run without code changes—invoke them from the command line or import decorators. py-spy attaches to running processes, making it ideal for production. Some tools offer optional decorators for targeted profiling.

How do I benchmark performance improvements to verify they work?▼

Use pytest-benchmark to measure function execution time before and after optimization. Write benchmark tests, run them repeatedly to get stable results, and compare output metrics. This validates that optimization patterns like caching or generators actually improve throughput.