austin VS just

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...)

I don't like makefiles, but I've been enjoying justfiles: https://github.com/casey/just

I started using just [0] on my projects and have been very happy so far. It is very similar to make but focused on commands rather than build outputs.

Define your recipes and then you can compose them as needed.

[0] https://github.com/casey/just

just - https://github.com/casey/just

Welp there is absolute chaos in that thread -- guess it's not an April Fools joke.

I wonder if relying on CI for anything other than provisioning machines is a mistake -- maybe we should have never moved from doing things from local scripts written in $LANGUAGE.

That said, I'm probably biased since I'm a massive fan of things like `make` and more appropriately for the current age, `just`[0]

[0]: https://github.com/casey/just

> When a command has some cognitive requirements I create a script with some ${1:-default} values and I store them all in $PATH enabled local/bin

I would consider using just for this:

https://github.com/casey/just

Your coworker's experience is more principled: Make is a mediocre tool for executing commands. It wasn't ever designed for that. Although it is pretty common to see what you are mentioning in projects because it doesn't require installing a dependency.

For a repo where an easy to install (single binary) dependency is a non-issue, consider using just. [1] You get `just -l` where you can see all the command available, the ability to use different languages, and overall simpler command writing.

[1] https://github.com/casey/just

This is fantastic, but I'd say that this solution is somewhat in response to this open issue from 2019:

https://github.com/casey/just/issues/429

I really wish just was included as a package in distributions.

So far, I didn't work on new features at all but on stabilizing the ground for further development: 1. CMake lists and modules were rewritten a lot, now managing builds and their configurations is much lesser pain. 2. Brought in Justfile for regular tasks, and it's great, no less. 3. Linters, formatters, analyzers for almost all the code (except for Janet for now, as because of it being a niche and young technology, it didn't get enough attention yet). 4. ECS stub. Now runtime class doesn't look like a god object. 5. Started writing unit tests which didn't happen with my personal projects before and maybe indicates how serious am I about this one :D 6. Some of previously hardcoded data has been moved to INI files. Now, if I release the game in 10 years, and in 10 more years some eccentric person decides to make a variant of it, it will be slightly simpler.

i've grown to like this for my personal projects. https://github.com/casey/just