As a heavy numba user: Huge thanks for working on that and my congratulations for that huge success with such a small team! Numba is a game changer in writing fast python code!
I never understood how to utilize Numba. I've seen videos showing the speed-up but I'm having it hard to use it with various packages ..so I definately need to learn more. Or any of the JITs available in such as the one in PyTorch. As for GPUs, have you tried CuPy ? Basically NumPy and SciPy for GPUs. You can try it out by simply importing cupy as np. That is, write your code using numpy, for example: np.linalg.svd() once you're done. Just change import numpy as np to import cupy as np and everything should work. (Check compatibility with your GPU ofc)
Why use python? There are C++ interpreters, which would permit you to execute it interactively. Then when you are ready, you compile whatever you were writing. C++ is a "real" language, you can write crazy things in C++ using objects, you can get extremely abstract and efficient. Because C++ is supposed to be "difficult"? Is it that more difficult than correspondingly written python? And what is the difference between what is described here and say Julia?
@@clipboarder_official Why Python is faster to develop? And which market segment has more Python software than binary software from C/C++/... if time to market is more important?
@@zzip0 Python being a higher level language than c++ makes it faster to develop, think about all the features that come along with higher level languages compared to c++. Countless companies run on python based backends, data science departments often leverage python aswell. I think the overall argument is that there already are great data science tools built for python, so making it faster is an easier task than transferring all those tools to c++.
This dude is insanely smart its kinda crazy all the great python projects he has been apart of.
As a heavy numba user: Huge thanks for working on that and my congratulations for that huge success with such a small team! Numba is a game changer in writing fast python code!
Numba has helped me enormously. It’s put gpu acceleration in the hands of hobbyists. Thanks you very much.
I never understood how to utilize Numba. I've seen videos showing the speed-up but I'm having it hard to use it with various packages ..so I definately need to learn more. Or any of the JITs available in such as the one in PyTorch.
As for GPUs, have you tried CuPy ? Basically NumPy and SciPy for GPUs. You can try it out by simply importing cupy as np.
That is, write your code using numpy, for example: np.linalg.svd()
once you're done. Just change import numpy as np to import cupy as np
and everything should work. (Check compatibility with your GPU ofc)
@@Woollzable I wouldn’t say it was easy but it easier than cuda in C. I was able to work through the documentation and adapt it to my purposes.
Just use PyPy lmao
If I said I understood 1% of that video. That would be generous to me. Still interesting.
He did a numba on them
I know you can write python plugins with C code to make it faster, is this something different?
Yes. Now you write fast code in python and just put "@jit" in front of it.
Well, how about Julia?
I love julia it is fast syntax is really easy... If python will not become faster or something then julia is the future I think.
Python packages have really stupid names.
Lucky 7
*House Numba*
~Stephen King
Micro python thoughts anyone?
I don't want any man I have a men that I love.
I srsly hate Numba, PyPy works just fine for me tbh. Maybe one day Numba miggt actually speed up my code lol
Why use python? There are C++ interpreters, which would permit you to execute it interactively. Then when you are ready, you compile whatever you were writing. C++ is a "real" language, you can write crazy things in C++ using objects, you can get extremely abstract and efficient.
Because C++ is supposed to be "difficult"? Is it that more difficult than correspondingly written python?
And what is the difference between what is described here and say Julia?
Julia is a chad language
Python is objectively faster to develop. On most real-world business cases, time-to-market beats time-to-run.
@@clipboarder_official Why Python is faster to develop? And which market segment has more Python software than binary software from C/C++/... if time to market is more important?
@@ciarfah Sorry, not sure what this "Chad" means in this case. Is it this one?
en.m.wikipedia.org/wiki/Chad_(slang)
@@zzip0 Python being a higher level language than c++ makes it faster to develop, think about all the features that come along with higher level languages compared to c++. Countless companies run on python based backends, data science departments often leverage python aswell. I think the overall argument is that there already are great data science tools built for python, so making it faster is an easier task than transferring all those tools to c++.
Yeet
6th!!
first comment again!