Is there anybody with experience using FP16 in Tensorflow/Keras? Regarding some blogs it is just available using a self-built version of Tensorflow as FP16 requires CUDA 10 [1]. Graphic card benchmark tests show significant improvements [2]. Would you already “rely” on this FP16 possibility? Do we know that it is always better/faster? I hope Tensorflow supports CUDA 10 soon, so no own-built version needs to be used.

What do you think about it?

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Sources:

[1]: https://medium.com/@noel_kennedy/how-to-use-half-precision-float16-when-training-on-rtx-cards-with-tensorflow-keras-d4033d59f9e4

[2]: https://lambdalabs.com/blog/2080-ti-deep-learning-benchmarks/