Make Python code 1000x Faster with Numba

In this video I introduce Numba which can make your python code 1000x faster. Numba is a just in time compiler for a subset of Python and Numpy. The first half of the video is dedicated to a basic intro and to highlighting a number of very common mistakes people make when using Numba. The remaining video show how to use Numba in a real world-ish simulation problem and shows the code running 1000x faster with Numba in both single and multithreaded cases, and concludes with a "reading list" for learning more about Numba.

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Deeper topics to discover here

Supported python and numpy features

Easily put your computation on a GPU!

#numba #programming #python

[gdsvsrv]

finally, my print("hello world") can run at the speed of light!

[MichaelKudlatheInterested]

Amazing, amazing video. As a PhD student, I'm sure you've saved me much more than 8 hrs of compute.

[klioseth]

Seriously this is brilliant.

By applying this to the 3 main functions of a 1500 lines project I went from a 24 minutes runtime to about 20 seconds. that's 70X faster for a day's work (it'd be faster now that I learned the more complicated details). The C++ equivalent of the code does it in about 15 seconds.

[tronali]

One thing to note is that Numba really optimizes nested loops. If you say have to call a function a 100 or million times, better to have an outerloop instead that iterates within the function. With parallel turned on, you can see a 1000x increase.

[id]

I nod my head with approving noises as if I understand what is going on.

[kaandonbekci]

Similar to other commenters, I have to say this is one of the best YouTube tutorials on writing fast Python code. Great buildup to the mic drop moment, amazing progression. Jack, I hope you make it big in the CS-tutorials YouTube space and have a great day.

[ger]

Numba really is great. Here is my first experience with it: I have some code that I estimated would take up to half a year to run. With some minor changes to the code for compatibility with Numba, the run time for the same conditions was up to 1.5 hours. This was a game-changer for me as I needed to run this script many times.
One of my colleagues also implemented it into his machine learning code and managed to obtain some significant speed bumps.

[irok]

Finally, this is what Python has needed

[jmnunezd]

One of the most awesome Python tutorials I have seen. The narratives, the examples and the way you explain it is just awesome, 5 starts to you man! keep on doing it!

[valente.victor]

This is one of the best programming-related video tutorials I've ever seen. No-nonsense style, very direct and still computationally rigorous. Awesome stuff!

[joshuatyler]

I implemented it in my code, and it took around an hour worth of computation and put it down to 30 seconds. Amazing stuff.

[frodobaggins]

11:45 - try this: np.zeros_like(input_list, dtype = np.int64, order='C')
My experience with numba - it works great, but types have to be very precisely defined for every single object. Otherwise nopython will fail, and where numba sucks is generating traceback messages - in the case of complex functions it will take forever to debug. So type whatever you can - it is not a 'Pythonic' way of thinking and writing, but in this particular case it is necessary. Anyway, great introduction!

[saurabhpareek]

I am in tears to see my code run so fast, thank you so much for this video

[MisterFresh]

What an amazing video. I'm a student in Computer Science and we just did a week ago an introduction to Numba.
And you sir not only explained to me the concepts in a positive astonishing way, but also gave me the desire and the excitement to apply it on all my old and future codes. Thanks you !
PS : Is there more videos where you're explaining the others topics ( convolution, neural networks .. )

[SomeFreakingCactus]

These are some seriously impressive results. I wonder if this could give Python a niche in indie game development.

[jongmassey]

Damnit, why did I only discover this 6 days before my PhD submission deadline?!

[preyasurakshit]

I cannot thank you enough for this video. I made a simulation of Gray Scott's Reaction diffusion in python, but it was painstakingly slow. After using numba, it now runs quite fast. Earlier it took nearly about 1 hour to render just 100 frames at a mere 400x400 resolution, now it renders 100 frames in 45 seconds, in 1280x720 resolution. Thank you very much for this video.

[brockobama]

There’s no way this delivers as promised. I’m about to implement it and will update soon.

[IndellableHatesHandles]

I bet my friend who called Python a "fake language" is soiling himself right now.

[RealLionTion]

YES, I LOVE PYTHON AND THIS IS PERFECT THANK YOU MATE