python-obj-system VS austin

i have a text on the python object system and metaclasss here https://github.com/MoserMichael/python-obj-system/blob/maste... as part of my free python course https://github.com/MoserMichael/python-obj-system/blob/maste...

it may be easy to get going with python, but it takes a non trivial amount of time to understand, what is going on. I have a advanced python3 course https://github.com/MoserMichael/python-obj-system that explains some of the more advanced concepts.

The course material is released under the MIT license, The course is written in the literate programming style. A python program exists for each lesson, it is run, and it's output is the markdown text that makes up the text of the lesson. The code and output of each python snippet is rendered as part of the lesson. This gives me some confidence in the presented material. The literate programming package is also presented in the github repository of the course.

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...)