Let's try easy multi-GPU training with pytorch.
Just adding:
if device == 'cuda':
net = torch.nn.DataParallel(net) # make parallel
cudnn.benchmark = True
will enable parallel GPU usage in Pytorch! :)
Training dataset: CIFAR10
DeepLearningFramework: Pytorch
Should have more than 2GPUs to test it out.
Should scale more than 2 GPUs!
https://qiita.com/arutema47/items/2b92f94c734b0a11609d
git clone https://github.com/kentaroy47/pytorch-mgpu-cifar10.git
cd pytorch-mgpu-cifar10
export CUDA_VISIBLE_DEVICES=0,1 # parallel training with GPUs 0 and 1.
python train_cifar10.py
# parallel training with GPUs 0-3.
export CUDA_VISIBLE_DEVICES=0,1,2,3
python train_cifar10.py
# デフォルトの設定はResnet18となっています。
# Res101で学習する場合
python train_cifar10.py --net res101
# Res50で学習する場合
python train_cifar10.py --net res50