icecream VS py-spy

Hey, very useful. Thanks! Similar to ic() for python, but with the nice ability to be used inline.

https://github.com/gruns/icecream

Icecream is great for this. Just calling ic(foo) gives you the same thing on stderr.

I get around this by using loguru (a wrapper around python's logger), so I get information like the calling function and line number with my debugging statements. I don't use it these days (and actually built something extremely similar around the same time), but icecream is another alternative that facilitates debugging-by-print

Most of the time I simply use icecream (a much better version of print()), and sometimes, I use pudb (a visual debugger) for tougher/trickier bugs.

We also have ice cream

I found icecream in a post on this subreddit and still use it as an alternative to print for debugging.

In the future using something like icecream might be interesting as well.

For CPU cycles, py-spy[0] is getting more and more used. For RAM, I would like to known too...

[0] -- https://github.com/benfred/py-spy

Theres also Py Spy, a profiling tool that can generate flame charts containing a mix of python and C (or C++) calls.

https://github.com/benfred/py-spy

It's worked really well for my needs

You should profile your consumer to check the bottlenecks. You can use the excellent py-spy(written in Rust). IMO a few usage of Numba there and there should solve your performance issues.

So as a first step you'll want to profile your program to figure out where it's slow, and hopefully that'll also tell you why it's slow. I'm the (biased) author of the Sciagraph profiler which is designed for this sort of application (https://sciagraph.com) but you can also try py-spy, which isn't as well designed for data processing/analysis applications (e.g. it won't visualize parallelism at all) but can still be informative (https://github.com/benfred/py-spy). Both are written in Rust ;)

Any advantages/disadvantages compared to py-spy [1]?

[1]: https://github.com/benfred/py-spy

Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.

A good profiler would be https://github.com/benfred/py-spy . If you run your app/benchmark with it, it should be able to draw a flamegraph telling you where the majority of time is spent. The info here is quite fine grained so it would already tell you where the bottleneck is. Without a full-fledged profiler you can also measure the timings in various parts of the code to understand where the bottleneck is.

Might be worth raising an issue on py-spy (a python profiler written in rust which "supports profiling native python extensions written in languages like C/C++ or Cython" to see if that can close the loop.