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According to: Introducing native PyTorch automatic mixed precision for faster training on NVIDIA GPUs

It's better to use AMP (with 16 Floating Point) due to:

  1. Shorter training time.
  2. Lower memory requirements, enabling larger batch sizes, larger models, or larger inputs.
  • So is there a reason not to work with FP16 ?
  • Which models / datasets / solutions we will need to use FP32 and not FP16 ?
  • Can I find an example in kaggle which we must use FP32 and not FP16 ?
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