Benchmarking PyTorch Lightning speed for computer vision tasks in Deep Learning.

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In Computer Vision, the bottleneck is almost always the data loading. Lightning doesn't change how torchvision or PIL works, but it does make it easier to implement PersistentWorkers and Prefetching. Because Lightning separates the "Science" (your model) from the "Engineering" (the training loop), you can easily plug in different data-loading strategies without rewriting your model logic. I've benchmarked both, and as long as your num_workers is set correctly, there is zero difference in images-per-second between Lightning and a raw PyTorch loop, but the Lightning code is much cleaner.

   Answered 2025-06-07 by Heather Morris

Are you using the Albumentations library for your augmentations, and have you tried running them on the GPU directly to save time?

   Answered 2025-06-09 by Ryan Campbell

• Ryan, that's a smart suggestion. If Laura moves the augmentations to the on_after_batch_transfer hook in Lightning, she can perform them on the GPU after the data has been moved to the device. This frees up the CPU to focus solely on fetching the next batch of raw images, which is a classic optimization trick that's very easy to implement within the Lightning framework structure.

     Commented 2025-06-10 by Jerry Simmons

It handles the validation loop very efficiently. I noticed a slight speedup because it doesn't need to rebuild the graph every time it switches modes.

   Answered 2025-06-11 by Scott Murphy

• True, Scott. The way it manages the with torch.no_grad(): context internally is very clean and ensures no memory leaks during long-running vision experiments.

     Commented 2025-06-12 by Laura King