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The four-line AMP training-loop change, the float16 vs bfloat16 decision, and the trade-offs that decide whether mixed precision actually speeds up your PyTorch training.
Keras mixed precision is a one-line change that can meaningfully speed up GPU training — but only with the right hardware, a float32 output layer, and loss scaling. Here's how to enable it and verify the win.
Enable mixed_float16 in Keras, keep the output layer in float32, handle loss scaling correctly for custom loops, and verify against a float32 baseline with clear recovery options.
pytorch
keras
tensorflow