data-loading

name	data-loading
description	Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
metadata	{"category":"tooling","trigger-keywords":"data,loading,dataloader,dataset,preprocessing,augmentation","applicable-stages":"10","priority":"6","version":"1.0","author":"researchclaw","references":"PyTorch Data Loading Tutorial, pytorch.org"}

Efficient Data Loading Best Practice

1. Use num_workers = min(8, os.cpu_count()) for DataLoader
2. Enable pin_memory=True when using GPU
3. Use persistent_workers=True to avoid re-spawning
4. Pre-compute and cache transformations when possible
5. For image data: use torchvision.transforms.v2 (faster)
6. For large datasets: consider memory-mapped files or WebDataset
7. Profile with torch.utils.bottleneck to find I/O bottlenecks