question: GPU usage does not remain high for lightweight models when loaded CIFAR-10 as a custom dataset
context: I am experimenting with the following repository. Keiku/PyTorch-Lightning-CIFAR10: "Not too complicated" training code for CIFAR-10 by PyTorch Lightning
  I have implemented two methods, one is to load CIFAR-10 from torchvision and the other is to load CIFAR-10 as a custom dataset. Also, I have implemented two models: a lightweight model (eg scratch resnet18, timm MobileNet V3, etc.) and a relatively heavy model (eg scratch resnet50, timm resnet152).
  After some experiments, I found the following.
  
  GPU usage remains high (nearly 100%) on any model when loading CIFAR-10 with torchvision
  When loading CIFAR-10 as a custom dataset, GPU usage remains relatively high (still temporarily zero) for heavy models
  When loading CIFAR-10 as a custom dataset, GPU usage remains low (going back and forth between 0% and 100%) for lightweight models (resnet18, MobileNetV3)
  
  In this situation, is there a problem with the implementation code of the custom dataset? Also, please let me know if there is a way to increase GPU usage even for lightweight models.
  I am experimenting in the following EC2 g4dn.xlarge environment.
  ⋊> ~ lsb_release -a                                                    (base) 21:45:51
  No LSB modules are available.
  Distributor ID: Ubuntu
  Description:    Ubuntu 18.04.5 LTS
  Release:        18.04
  Codename:       bionic
  ⋊> ~ nvidia-container-cli info                                         (base) 21:48:20
  NVRM version:   450.80.02
  CUDA version:   11.0
  
  Device Index:   0
  Device Minor:   0
  Model:          Tesla T4
  Brand:          Tesla
  GPU UUID:       GPU-ba54be15-066e-e7e5-87d0-84b8ac2672c6
  Bus Location:   00000000:00:1e.0
  Architecture:   7.5
answer: I got a replay from ptrblck.
  https://discuss.pytorch.org/t/gpu-usage-does-not-remain-high-for-lightweight-models-when-loaded-cifar-10-as-a-custom-dataset/125738