How do you handle GPU memory limitations when training on high-resolution image datasets

Question

Can you suggest how I can handle GPU memory limitations when training on high-resolution image datasets?

Answer 1

In order to handle GPU memory limitations when training on high-resolution image datasets, you can use gradient accumulation, mixed precision training, and smaller batch sizes. Here is the code snippet you can refer to:

In the above code, techniques like Gradient Accumulation divide the effective batch size across multiple smaller iterations. Mixed precision training uses frameworks like a torch, cuda, and amp for reduced memory usage, and memory-efficient dataloaders are used to employ image resizing or on-the-fly data augmentation.

Hence, using these techniques, you can handle GPU memory limitations when training on high-resolution image datasets.