uneven GPU memory caused by multi-gpu training

[lqzhao]

Hi, Alex, Thanks for your nice work. I'm facing the problem of uneven GPU memories when training the model with multiple GPUs. It costs much more memory on GPU#0 than others. I think the main reason is that DataParallel can only compute losses on GPU#0. Would you give some advice to balance the GPU memory? Thanks in advance.

[alexklwong]

Strange, I never had that problem before. What sort of GPUs are you using? What about batch size? Is the imbalance causing an issue?

It could be related to this: https://discuss.pytorch.org/t/dataparallel-imbalanced-memory-usage/22551/9

But then moving loss computation into the nn.Module doesn't quite make logical sense (since loss requires multiple images and their relative pose to compute) and also add overhead.

[lqzhao]

I use four 2080Ti GPUs with a batch size of 24. It's a nonnegligible issue when using a large batch size. Please see this.

[alexklwong]

I think if you can also do something like this in your bash:

export CUDA_VISIBLE_DEVICES=0,1

if you want to use a smaller batch size and it should allocate them onto a second GPU.

[lqzhao]

Thanks, you mean like this? export CUDA_VISIBLE_DEVICES=0,1; bash bash/kitti/train_kbnet_kitti.sh I tried this but it didn't work...

[lqzhao]

I separated the function of computing_loss as a Module and Dataparallel it. When I train with the same settings, the GPU memories are like this: It seems to be slightly alleviated...But not very promising.

[alexklwong]

I did some digging. I think this is the nature of PyTorch where it replicates anything that is on data parallel across GPUs. But the first GPU is still the ``master'' so it needs to hold optimizer, parameters, any operation that is not parallelized.

[alexklwong]

Thanks, you mean like this? export CUDA_VISIBLE_DEVICES=0,1; bash bash/kitti/train_kbnet_kitti.sh I tried this but it didn't work...

As for the above, I think you'll need to replace the export statement in the bash file https://github.com/alexklwong/calibrated-backprojection-network/blob/master/bash/kitti/train_kbnet_kitti.sh#L3

[lqzhao]

I did some digging. I think this is the nature of PyTorch where it replicates anything that is on data parallel across GPUs. But the first GPU is still the ``master'' so it needs to hold optimizer, parameters, any operation that is not parallelized.

Thanks for your reply. I also found that Dataparallel would cause very low training efficiency due to the cross-GPU interactions. Thus I use 2 GPUs to keep the balance between efficiency and batch size. I can easily reproduce the results of your paper. Thanks again.

[alexklwong]

Great, thanks, closing this issue