Numba makes your code FASTER with ONE decorator

Опубликовано: 17 Февраль 2025
на канале: Carberra

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There has been plenty of discussion around Python's new JIT since it dropped, but did you know there are other ways of getting a JIT into Python? One of the simpler ways is by using Numba, which allows you to JIT functions by using a single decorator! The results can be very impressive too, especially for numerical or statistical operations!

00:00 - Intro
01:42 - Installing Numba + explaning context [1-2]
03:54 - Using the @jit and @njit decorators [3-4]
05:59 - Monte Carlo benchmarks [5]
08:00 - Why is Numba not always faster? [6]
09:44 - Euclidean distance benchmarks [5]
10:34 - Euclidean distance benchmarks (ft. NumPy) [5]
11:38 - The other problem with Numba [5, 7-8]
15:21 - Some other cool Numba features [8-9]
16:24 - Outro

[1] https://www.ibm.com/topics/monte-carl...
[2] https://risk-engineering.org/notebook...
[3] https://numba.readthedocs.io/en/stabl...
[4] https://www.freecodecamp.org/news/jus...
[5] https://github.com/Carberra/python-bu...
[6] https://www.cuemath.com/euclidean-dis...
[7] https://neptune.ai/blog/k-means-clust...
[8] https://github.com/numba/numba
[9] https://numba.pydata.org

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