Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
PyTorch
Avoid common PyTorch mistakes — train/eval mode, gradient leaks, device mismatches, and checkpoint gotchas.
skill openclawclawhub Free
Avoid common PyTorch mistakes — train/eval mode, gradient leaks, device mismatches, and checkpoint gotchas.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Train vs Eval Mode
model.train() enables dropout, BatchNorm updates — default after init model.eval() disables dropout, uses running stats — MUST call for inference Mode is sticky — train/eval persists until explicitly changed model.eval() doesn't disable gradients — still need torch.no_grad()
Gradient Control
torch.no_grad() for inference — reduces memory, speeds up computation loss.backward() accumulates gradients — call optimizer.zero_grad() before backward zero_grad() placement matters — before forward pass, not after backward .detach() to stop gradient flow — prevents memory leak in logging
Device Management
Model AND data must be on same device — model.to(device) and tensor.to(device) .cuda() vs .to('cuda') — both work, .to(device) more flexible CUDA tensors can't convert to numpy directly — .cpu().numpy() required torch.device('cuda' if torch.cuda.is_available() else 'cpu') — portable code
DataLoader
num_workers > 0 uses multiprocessing — Windows needs if __name__ == '__main__': pin_memory=True with CUDA — faster transfer to GPU Workers don't share state — random seeds differ per worker, set in worker_init_fn Large num_workers can cause memory issues — start with 2-4, increase if CPU-bound
Saving and Loading
torch.save(model.state_dict(), path) — recommended, saves only weights Loading: create model first, then model.load_state_dict(torch.load(path)) map_location for cross-device — torch.load(path, map_location='cpu') if saved on GPU Saving whole model pickles code path — breaks if code changes
In-place Operations
In-place ops end with _ — tensor.add_(1) vs tensor.add(1) In-place on leaf variable breaks autograd — error about modified leaf In-place on intermediate can corrupt gradient — avoid in computation graph tensor.data bypasses autograd — legacy, prefer .detach() for safety
Memory Management
Accumulated tensors leak memory — .detach() logged metrics torch.cuda.empty_cache() releases cached memory — but doesn't fix leaks Delete references and call gc.collect() — before empty_cache if needed with torch.no_grad(): prevents graph storage — crucial for validation loop
Common Mistakes
BatchNorm with batch_size=1 fails in train mode — use eval mode or track_running_stats=False Loss function reduction default is 'mean' — may want 'sum' for gradient accumulation cross_entropy expects logits — not softmax output .item() to get Python scalar — .numpy() or [0] deprecated/error
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md