I am experimenting with the following repository. GitHub - 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