ma-xu / pointMLP-pytorch

[ICLR 2022 poster] Official PyTorch implementation of "Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework"

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about multi gpu support

Emenent758 opened this issue · comments

Hello !
Thank you for really nice work.
Please tell me how to run your code on a multi gpu machine? secondly, how much gpu memory is needed for segmentation's task? I have 12 gb gpu memory but when I put the model on training it gives cuda out of memory error right in the beginning, although the classification model trains fine on my machine,
Please guide.
Thanks

commented

Hi, Thanks for your interest.
I assume you can directly run on multi-gpu machine. Please let me know if any problems.
Should be around 20 GB (not very sure). You can reduce the batch size to fit your gpu.

when on try to run on multi gpu machine, it gets stucks both for classification and part segmentation, it doesnt show any error but doesnt proceed further.
image
for segmentation
image
Also please find the screenshot for memory usgae when model is made to run.
image
Please guide.
Thank you for your time

commented

@Emenent758 Sorry for the later reply. I have never met this issue previously. Does it work well on one gpu?

commented

@Emenent758 I will close this issue since no further discussions. Feel free to reopen it if necessary.