gopy VS line_profiler

I've used gopy[0] recently to access a go library in Python. It surprisingly Just Worked, but I was disappointed by some performance issues, like converting lists to slices.

[0] https://github.com/go-python/gopy

the heavy lifting is done in native libraries and you get to experiment fast using an easy language. the combo is quite hard to beat. Now there is a missed opportunity to write such libraries in Go, but as I read here and there Go is hard to integrate well as a library. There is gopy but it's light years away from PyO3 for instance, I don't think it'll ever gain traction, but who knows.

> So if you learn Go, you'll never be able to use it to interoperate with e.g. your Python program to speed it up.

Never done it myself, but:

https://www.ardanlabs.com/blog/2020/07/extending-python-with...

https://github.com/go-python/gopy

Check out gopy for an easy way to extend your Python code with Go.

LineProfiler is the best tool to learn how to write performant Python and code optimization.

https://github.com/pyutils/line_profiler

You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.

For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.

Doing this for like 2 weeks, basically you can automatically recognized most bad code patterns in a glance.

You may want to try using a package like line_profiler to narrow down where the time is spent.

When tuning for performance, always measure. Never assume you know where the slow parts are. Run a line profiler and see where all the time is actually going.

Either way I would highly recommend running a profiler on your code to see where the program is spending most of its time. line_profiler is a very nice one, as it shows you execution time for each line.

There are dedicated tools like line_profiler for python - if this doesn't do exactly what you need it can be easily modified.

It's surprising to me that an sklearn pipeline and a spacy pipeline both doing simple regexing are vastly different in performance. I would go one layer deeper with measurement with something like line_profiler, which I've used to great effect to get line-by-line perf stats. This should illuminate why.

This looks really nice! Does it use line_profiler or is it a different implementation for the profiling? Either way the interface is fantastic!

# https://github.com/pyutils/line_profiler pip install line_profiler kernprof -l -v some-code.py # This might take a while... Wrote profile results to some-code.py.lprof Timer unit: 1e-06 s Total time: 13.0418 s File: some-code.py Function: exp at line 3 Line # Hits Time Per Hit % Time Line Contents ============================================================== 3 @profile 4 def exp(x): 5 1 4.0 4.0 0.0 getcontext().prec += 2 6 1 0.0 0.0 0.0 i, lasts, s, fact, num = 0, 0, 1, 1, 1 7 5818 4017.0 0.7 0.0 while s != lasts: 8 5817 1569.0 0.3 0.0 lasts = s 9 5817 1837.0 0.3 0.0 i += 1 10 5817 6902.0 1.2 0.1 fact *= i 11 5817 2604.0 0.4 0.0 num *= x 12 5817 13024902.0 2239.1 99.9 s += num / fact 13 1 5.0 5.0 0.0 getcontext().prec -= 2 14 1 2.0 2.0 0.0 return +s