subb VS austin

I wrote this to simplify just that for my own tools; the subprocess module that comes with the batteries has a very general interface, i think that it is a bit complex for a quick script.

https://github.com/MoserMichael/subb

https://pypi.org/project/subb/

Python doesn't have the problem of shell scripting language, it doesn't get impractical, as the program is getting more complex. In bash you have arrays, and even maps, but these aren't pretty. Also the shell scripting language is being evaluated by an parse tree/AST interpreter, that's significantly slower than even python, in it's byte code interpreted form.

My objective was to get an abstraction, for a one line process run and extraction of the result, similar to what we had in Perl5 with the system library function. https://perldoc.perl.org/functions/system

Also the shell is impractical, when it comes to slightly more complex programs. There is a limit on what you can do with pipes. Maybe that's the reason why perl is that flexible, as they tried to bridge both realms: Perl had to be useful as a replacement for the quick shell like script, and to be useful as a general purpose programming language.

i have a subprocess wrapper, might also be of help: https://github.com/MoserMichael/subb

I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md

Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).

I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.

I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.

There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).

Somehow this experience so far was very disappointing.

(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)

Just wanted to add Austin: Python frame stack sampler for CPython written in pure C (https://github.com/P403n1x87/austin)

If you follow the call stack carefully you should be able to get to the point where sklearn calls ddot_kernel_8 (indirectly in this case). Austin(p) reports source files as well, so that shouldn't be a problem (provided all the debug symbols are available). If you're collecting data with austinp, don't forget to resolve symbol names with the resolve.py utility (https://github.com/P403n1x87/austin/blob/devel/utils/resolve..., see the README for more details: https://github.com/P403n1x87/austin/blob/devel/utils/resolve...)