Profile Code

Use profiling tools to measure CPU time, memory allocation, and identify performance bottlenecks in code.

When to Use

• Finding performance bottlenecks
• Measuring CPU vs memory tradeoffs
• Understanding where code spends most time
• Optimizing hot code paths

Quick Reference

# Python CPU profiling with cProfile
python3 -m cProfile -s cumulative script.py | head -30

# Memory profiling
pip install memory-profiler
python3 -m memory_profiler script.py

# Detailed call graph
pip install graphviz
python3 -m pstats /tmp/profile.prof

Workflow

1. Select profiler: Choose appropriate tool (cProfile for CPU, memory_profiler for memory)
2. Run with instrumentation: Execute code with profiling enabled
3. Capture metrics: Record timing and memory data
4. Analyze output: Identify top time consumers and memory hogs
5. Report findings: Document bottlenecks with before/after context

Output Format

Profiling report:

• Top functions by execution time
• Call count for each function
• Memory allocation per function
• Call graph/call tree
• Percentage of total time/memory per function
• Recommendations for optimization

References

• See suggest-optimizations skill for improvement recommendations
• See benchmark-functions skill for measuring improvements
• See CLAUDE.md > Performance for optimization guidelines