How can I use GPU instead of multiprocessing or scoop in DEAP?

[ppseverin]

Hi! I'm new to using GPU to speed up computations, so this might be a silly question. I have a script that uses DEAP and actually it uses multiprocessing. I would like to use my GPU (it's a RTX 3080 TI, in case it's important to know) to speed up calculations and the whole process. I googled for some examples of how to do it, but couldn't find any :(. Is this possible? How can I achieve this?

[fmder]

Hi, no silly questions! You'll need to code your evaluation function in a GPU compatible langage such as Numba or CuPy or Pytorch core. However, moving from cpu to gpu has a lot of overhead, you may need to group the evaluation of your whole population together to observe speedup.

[Mujtaba1986]

Last I checked about this a year ago, I found DEAP does not support this.

[fmder]

Your evaluation function must enable it. Le jeu. 1 févr. 2024, 10 h 03, Mujtaba1986 ***@***.***> a écrit :

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Last I checked about this a year ago, I found DEAP does not support this. — Reply to this email directly, view it on GitHub <#684 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAHKXQXF2HUKITFHOU5WIF3YROVDDAVCNFSM6AAAAAAUAKJ4AKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRRGUZTMNZZGY> . You are receiving this because you commented.Message ID: ***@***.***>

[Leon-Qiao]

I also encountered a similar situation. The main task of my evaluation function was to make predictions through a pre-trained deep-learning model. I gave up using the map function, directly fed the list of individuals into the evaluate function, and converted it into a NumPy array. The return value of the evaluate function was also a batch of fitnesses. By implementing batch calculations instead of using the map to iterate over a list with multiple single-sample calls, the efficiency of my genetic algorithm dramatically improves.