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[D] Training NNs with FP16 in Tensorflow

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?

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/

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.