Suddenly I noticed that the virtual memory usage is huge during my training. The size grows when the first Tensor is passed to GPU. Also, I noticed that using more GPU is much slower than training using one GPU. I am almost certain that the training time on 2 GPUs what almost 2 times faster than using one GPU.
I am using ubuntu, PyTorch 1.0.1 and CUDA 10.
Here I can show htop output of training on one GPU
Does anyone have the idea what’s going on?
Pytorch claims all the memory of your GPU and even when it is not using that memory, if you run nvidia-smi it would show, no memory is free.
Refer to memory management docs for more details.
Excuse me, I am not able to understand the answer. nvidia-smi behaves normally - some of my chosen GPU’s are occupied at the same level as before in terms of RAM.
The simplest code that creates 3 visible lines with more than 20GB virtual memory taken in htop is here:
cuda = torch.device('cuda')
A = torch.empty((100, 100), device=cuda).normal_(0.0, 1.0)
I’d like to bump this up because it seems strange to me as well. Why so much virtual memory?