Thank you @ptrblck.
As I’m working on aws services, I found that when I moved all my dataset to my EC2 instance, where I have my PyTorch/Cuda optimized notebooks, the GPU started to be used, which speeded it up.
Though, the usage of the GPU is still low, and doesn’t reach 80% unless I input big load of images, and big batch_size.
Hence, I presume the data loading being the origin of my issues.
I’ll test pinning memory, using multiple workers, and let you know.
Thank you very much
Habib