I have found lot of time the same behaviour using the same model initialization weights and using with tf.device("gpu"): or with tf.device("cpu"): I cannot get same results. Also there is huge difference of performances always in favor of cpu version. I have tf 2.10 devs 2.10 and mps 0.6 versions. unfortunately I cannot share the code.

Hello, I see exactly the same thing. On recent versions tf 2.7 / 2.8 / 2.9 / 2.10 & 2.11 generally the cpu version works without errors first. if there is no errors on GPU I generally see a bad performance on accuracy of my models. While those models are good using the same python code on CPU. this is not a isolated bug but a real trend. any idea on how to trigger the route cause and to have a fix ?

Hi all, Do you know if there is a clean release for tf 2.11 of the devs ? Official link https://developer.apple.com/metal/tensorflow-plugin/ is not updated properly since v2.9 0.5.0. Would be nice to properly document those in the official page so we don't need to search in many area, tutorial, etc.. to be sure tf:2.X goes with tf-devs:0.Y.y and tf-metal:0.Z.z