Profiling Conclusion

Overview

Questions

• What has been learnt about profiling?

Objectives

• Review what has been learnt about profiling

This concludes the profiling portion of the course.

cProfile, snakeviz and line_profiler have been introduced, these are some of the most accessible Python profiling tools.

With these transferable skills, if necessary, you should be able to follow documentation to use more advanced Python profiling tools such as scalene.

Key Points

What profiling is:

• The collection and analysis of metrics relating to the performance of a program during execution .

Why programmers can benefit from profiling:

• Narrows down the costly areas of code, allowing optimisation to be prioritised or decided to be unnecessary.

When to Profile:

• Profiling should be performed on functional code, either when concerned about performance or prior to release/deployment.

What to Profile:

• The collection of profiling metrics will often slow the execution of code, therefore the test-case should be narrow whilst remaining representative of a realistic run.

How to function-level profile:

• Execute cProfile via python -m cProfile -o <output file> <script name> <arguments>
• Execute snakeviz via python -m snakeviz <output file>

How to line-level profile:

• Import profile from line_profiling
• Decorate targeted methods with @profile
• Execute line_profiler via python -m kernprof -lvr <script name> <arguments>