You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I got a question about the training time is too long and the GPU memory is too high. I use GTX 3090 24G, but the batch size I can set just 1, if setting 2 I'll get cuda out of memory. So I wanna ask to how I can speed up my training time. About the GPU memory, whether the input image can be blocked and then entered to increase the batch size. Can you share some details about training? Thank you so much.
The text was updated successfully, but these errors were encountered:
High, thanks for your question! The training time is inherently long due to the large datasets and unrolled networks. In terms of GPU memory, in our paper, we used the batchsize of 1. To increase the batch size, I would recommend using average gradients, or GPU parrallization.
I got a question about the training time is too long and the GPU memory is too high. I use GTX 3090 24G, but the batch size I can set just 1, if setting 2 I'll get cuda out of memory. So I wanna ask to how I can speed up my training time. About the GPU memory, whether the input image can be blocked and then entered to increase the batch size. Can you share some details about training? Thank you so much.
The text was updated successfully, but these errors were encountered: