Numba speeds up Python with ONE DECORATOR

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Numba is a one-decorator solution to amazingly fast numerical operations in Python. Hell, if you're gonna go Pythonic, might as well go all the way!
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Опубликовано:

30 сен 2024

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Комментарии : 16

@DrTodd13 4 месяца назад

Whoever wrote that automatic parallelization stuff is a freaking genius.

@JJayaraj-fs1di 3 месяца назад

Hi. Im having a normal python dictionary which has values a list of numbers. Is there a way to convert this dictionary into a numba typed dictionary?

@idk____idk6530 2 года назад

Please next video on -( Ctypes ) importing C compiled function in python :)

@2600D4u 2 года назад

Yes please!

@Carberra 2 года назад

I've dabbled with ctypes before, but never really looked into it. Could be an interesting thing to look into!

@idk____idk6530 2 года назад

@@Carberra if you try to find prime form 0-500k in Python using efficient algorithm it ma take 76-50s but i tried same algorithm in C and import in python using Ctypes -it take 0.98s 🥵 that's the power!

@ritikranjan7192 2 года назад

naise

@TheBuilder Год назад

wow, amazing

@charlesspringer4709 Год назад

Factorial is usually implemented with recursion - because computer science people love recursion. Maybe Numba can't use recursion? I would place money on it.

@DrTodd13 4 месяца назад

Factorial is often taught as a recursive algorithm just to teach programmers about recursive algorithms. Real factorial implementations can just use a simple loop. And of course Numba does support recursion.

@Gh0sTG0 Год назад

Hi. Thanks for video! One small offtopic question: I see there that .venv folder with virtual environment, in vscode. From where it appears? Did you create project with pycharm? Or it was some manual command to set venv?

@Carberra Год назад

Manual command. Python has a built-in venv module, though I use pyenv nowadays.

@hhhtocode651 2 года назад

should numba not have a matmul function like numpy and jax? i noticed it is very hard to write one as efficient as numpy has from scratch and then apply jit to it.

@Carberra 2 года назад

Using the decorator rewrites the methods for you, so a matmul should exist if you call Numpy's one from inside a decorated function. Though granted not everything is available, so some things may be missing.