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

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Can you suggest how I can handle GPU memory limitations when training on high-resolution image datasets?
Nov 13 in Generative AI by Ashutosh
• 7,050 points
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1 answer to this question.

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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.

answered Nov 17 by Ashutosh
• 7,050 points

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