Want to use NumPy? Better hope you have code that can be expressed as array computations (a tiny minority of performance sensitive tasks in my field of work).

Cython? Now you're just writing another language, and get the joy of having to distribute compiled code in your Python package - for example, like the very fun segfaults I experienced because Conda will automatically override the system linker, breaking all compilation including my Cython modules.

Numba? Hope you don't make custom classes - you know, one of the basic features of the language.

If you look at ML, Python is completely fine because all the processing that happens with matrix multiplication, even on CPUs, far, far, FAR outweighs all the setup stuff in volume of operations.

On the other hand, if majority of your application relies heavily on processing speed (i.e you need compare/jump operations rather just add/multiply/load/store of the GPUs), Python is going to be slow. In this case, if you want custom performant code, you write C extensions for the performant critical code, and launch them from higher level python code.

That being said, there is generally a library (like Taichi) that already does this for you.