austin VS textual

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...)

The Textual project has a lot of screenshots in its documentation. These screenshots are built with the docs, so they are always up to date.

https://textual.textualize.io/

Textual[0] does this for CLI apps. That’s not for full GUI apps, but it’s very DOM-like, uses CSS selectors, etc. and a cool option when it meets your needs.

[0] https://github.com/Textualize/textual

For future projects that need a TUI beyond normal printing to a terminal, I'd recommend taking a look at Textual.

https://jupyterbook.org/en/stable/content/code-outputs.html#...

`less -R` is not the default.

FWIW, textual (and urwid) does ANSII escape codes well: https://github.com/Textualize/textual

  touch file$'\n'name

After installing logmerger, you can run a self-contained demo by running logmerger --demo, to view two log files before and after they are merged, and to play with the user-interface features provided by textual.

I think it just survived naturally, filling in the cracks left by Java / C++.

And not the era of Textual (https://textual.textualize.io/) is here, python may get the spotlight even more.