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Tweetorial: using mixed precision training with Keras models. All you need to do is set the dtype policy via `keras.mixed_precision.set_global_policy()`. Example: training an ImageNet model on Colab's TPUv2 (for free!) with bfloat16 mixed precision: https://colab.research.google.com/drive/1DPj1f_vdjGfJDCCl0XBtGkueW9AEkotB?usp=sharing …pic.twitter.com/K0AJWbxbMA
Use "mixed_float16" as the policy value on GPU, and "mixed_bfloat16" on TPU. You can also configure the dtype policy on a per-layer basis using the `dtype` layer constructor argument. Useful to keep some layers running in float32 when necessary!
In our example, we kept the data augmentation stage in float32 (created before setting the global policy) since it's meant to be run on CPU as part of the TF data pipeline.pic.twitter.com/2nRbBleVaZ
Using mixed precision can improve training performance by over 3x on modern NVIDIA GPUs, and 60% on TPUs, at no loss of accuracy.
wait keras hosts imagenet in a bucket now? how long has this been going on?
You don't have access to the actual data -- you'd have to edit the data location to your own hosting.
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