prometeo VS line_profiler

Not impossible but I guess you might end up with an extra runtime layer and some more dynamic operations will not be very fast. Or you restrict it to a subset of Python like this project does: https://github.com/zanellia/prometeo

You could of course write a bytecode VM in Golang but I guess that defeats the purpose.

Honest question: How does pycom compare to similar tools like Nuitka, prometeo, or mypyc?

If you don't mind switching to a little different syntax of Python, then you also might want to take a look at prometeo - an embedded domain specific language based on Python, specifically aimed at scientific computing. Prometeo programs transpile to pure C code and its performance can be comparable with hand-written C code.

This is awesome! The direction of using a subset of python, while leveraging the user base and static typing to accomplish some other everyday task in a different language is very legit IMO.

I took a cursory look at:

https://github.com/zanellia/prometeo/blob/master/prometeo/cg...

It seems quite similar in spirit to

https://github.com/adsharma/py2many/blob/main/pyrs/transpile...

I'm not spending much time on py2many last few months (started a new job). Let me know if any of it sounds useful - especially the ability to transpile to 7-8 languages including Julia, C++ and Rust.

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