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train.py tries to allocate too much RAM when running with multiple GPUs #43

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yesung31 opened this issue Oct 22, 2022 · 0 comments
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@yesung31
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I am now working on training the MAT network from scratch with a full Places dataset (512*512 resolution) and I'm now facing this error.
image
Above error was produced by followinig command.
python train.py --outdir=/home/work/outputs/MAT/baseline_places_full --gpus=2 --batch=8 --metrics=fid36k5_full --data=/home/work/Dataset/places/full/data_large --data_val=/home/work/Dataset/places/full/val_large --dataloader=datasets.dataset_512.ImageFolderMaskDataset --mirror=True --cond=False --cfg=places512 --aug=noaug --generator=networks.mat.Generator --discriminator=networks.mat.Discriminator --loss=losses.loss.TwoStageLoss --pr=0.1 --pl=False --truncation=0.5 --style_mix=0.5 --ema=10 --lr=0.001
However, this error does not occur when I only use one GPU with the same number of batch for each GPU.
image
Above result was produced by the following command.
python train.py --outdir=/home/work/outputs/MAT/baseline_places_full --gpus=1 --batch=4 --metrics=fid36k5_full --data=/home/work/Dataset/places/full/data_large --data_val=/home/work/Dataset/places/full/val_large --dataloader=datasets.dataset_512.ImageFolderMaskDataset --mirror=True --cond=False --cfg=places512 --aug=noaug --generator=networks.mat.Generator --discriminator=networks.mat.Discriminator --loss=losses.loss.TwoStageLoss --pr=0.1 --pl=False --truncation=0.5 --style_mix=0.5 --ema=10 --lr=0.001
I am now using 8 GPUs with 40GB of RAM for each.

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