Data Loading
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
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Install
$ agentstack add skill-thada2402-autoresearchclaw-data-loading ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Efficient Data Loading Best Practice
- Use numworkers = min(8, os.cpucount()) 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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: thada2402
- Source: thada2402/AutoResearchClaw
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.