poly-match VS scalene

Not my experience at all. At work we rewrote a small bit of hotspot python in Rust with no issues. This was what we primarily followed: https://ohadravid.github.io/posts/2023-03-rusty-python/

Take at look at this example, it still uses Python as an interface to Rust code. Maybe you can do something similar to still achieve performance improvements without changing the entire codebase.

People are probably downvoting because it's needlessly hyperbolic and argumentative. Nobody is saying that python isn't faster to iterate with, but they're arguing that it would take months to get negligable performance gains in a lower level language, meanwhile here is a recent post from a company that increased the execution of they're python code by 100x with less than 100 lines of Rust. They also claim that nobody cares if something runs a few milliseconds faster, when we're talking about game dev, where games are frequently judged on how many milliseconds it takes to run game logic between frames.

Semi Vectorized code:

https://github.com/ohadravid/poly-match/blob/main/poly_match...

Expecting Python engineers unable to read defacto standard numpy code but meanwhile expect everyone can read Rust.....

Not to mention that the semi-vectorized code is still suboptimal. Too many for loops despite the author clearly know they can all be vectorized.

For example instead the author can just write something like:

   np.argmin(

The article links to a full implementation, so you should be able to test this.

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)

Use this my fren: https://github.com/plasma-umass/scalene

You should take a look at Scalene - it's even better.

https://github.com/plasma-umass/scalene

I like seeing another Python profiler. The one I've been playing with is Scalene (GitHub). It does some fun things related to letting you see how much things are moving across the system Python memory boundary.

Ai vazut "Python Performance Matters" by Emery Berger (Strange Loop 2022)? E in principiu o prezentare si demo cu Scalene.

I tried out some features of machine learning models suggesting optimisations on code profiled by scalene and pretty much all of them would make the code less efficient, both time and memory wise. I am not worried. The devil is in the details and ML will not replace all of us anytime soon