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Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
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Based on SOC occupation classification
| name | mixed-precision |
| description | Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance. |
| metadata | {"category":"tooling","trigger-keywords":"training,gpu,memory,speed,precision,fp16,bf16","applicable-stages":"10,12","priority":"5","version":"1.0","author":"researchclaw","references":"Micikevicius et al., Mixed Precision Training, ICLR 2018","code-template":"scaler = torch.cuda.amp.GradScaler()\nfor batch in dataloader:\n optimizer.zero_grad()\n with torch.cuda.amp.autocast():\n output = model(batch)\n loss = criterion(output, target)\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n"} |
Use torch.cuda.amp for automatic mixed precision:
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