Data loading
Skill thada2402/AutoResearchClaw/researchclaw/skills/builtin/tooling/data-loading
Generate research papers autonomously by chatting with OpenClaw, using Python 3.11+, with a self-evolving framework and extensive test coverage.
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Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
SKILL.md
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Efficient Data Loading Best Practice
- Use num_workers = min(8, os.cpu_count()) for DataLoader
- Enable pin_memory=True when using GPU
- Use persistent_workers=True to avoid re-spawning
- Pre-compute and cache transformations when possible
- For image data: use torchvision.transforms.v2 (faster)
- For large datasets: consider memory-mapped files or WebDataset
- Profile with torch.utils.bottleneck to find I/O bottlenecks