ReddRadar
Agent skill
mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
Install this agent skill to your Project
npx add-skill https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/mixed-precision
Metadata
Additional technical details for this skill
- author
- researchclaw
- version
- 1.0
- category
- tooling
- priority
- 5
- references
- Micikevicius et al., Mixed Precision Training, ICLR 2018
- code template
- scaler = torch.cuda.amp.GradScaler() for batch in dataloader: optimizer.zero_grad() with torch.cuda.amp.autocast(): output = model(batch) loss = criterion(output, target) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update()
- trigger keywords
- training,gpu,memory,speed,precision,fp16,bf16
- applicable stages
- 10,12
SKILL.md
Mixed Precision Training Best Practice
Use torch.cuda.amp for automatic mixed precision:
- Wrap forward pass in torch.cuda.amp.autocast()
- Use GradScaler for loss scaling
- BF16 preferred over FP16 on Ampere+ GPUs (RTX 3xxx, A100, RTX 4xxx)
- Watch for NaN gradients — reduce learning rate if needed
- Do NOT use amp with custom CUDA kernels unless tested
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