Intel-based is also supported. However, you have to specify the python version as the following instructions.
`NOTE: If using conda environment built against pre-macOS 11 SDK use:
SYSTEM_VERSION_COMPAT=0 python -m pip install tensorflow-macos
otherwise you will get errors like : “not a supported wheel on this platform”`
hawkiyc
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Hi, @Frameworks Engineer, the latest version of TF-macos and TF-metal result in my IDE crash on my device. And I have reported it on FB9691835; however, there is no answer at all.
Hi, @afaq187. Sorry, this thread is about Intel-based Mac devices, so I can't do anything more for you. However, from my experience, the latest version will cause kernel crashes while training. So my solution is to downgrade to the previous version. I hope this can help.
Hi, @EugeneZhidkov. The latest version can't work at all. And I have already reported this issue at FB9691835. Could you please look into it and solve this issue? Thank you very much.
It's weird, I can run models with ADAM without any issue for the previous version.
That's weird. The previous version is fine for me. Here is my env file in the .yaml form. Maybe you can try it.
https://www.icloud.com/iclouddrive/0Yhr444NAqu6oD8qxOfKriY-A#env%5Fmac%5Ftf
Hi, @SvenEck. No, the IDE kernel always crashes when I am trying to train any model.
Hi, @itsloudc. According to Apple, this GPU assignment issue happens while you are using TF-hub and/or huggingface for the NLP tasks and there is no known workaround right now. It looks like that we can only wait for the new version of TF-macos and TF-metal.
Thank you very much for your time. I am looking forward to the new version.
Hi, @Frameworks Engineer. I appreciate your time and looking forward to the new version of TF-metal / TF-macos.
Hi EugeneZhidkov. May I follow up on this bug? Will this issue be addressed in the new version of macOS or TF-macOS / TF-metal?
I have tried to make a whole new backup with another HDD, but not work at all this time.
According to Apple, you shall update to Monterey.
Hi, Eugene. I made a new test, fine-tuned BERT without the TF-hub package, yet it still not work. It looks like that neither TF-hub nor TF_model_official gets the assignment of the device correctly. I also updated the feedback with the test .py file and hope this could help.
Dear Eugene,
The feedback has been submitted last night, and the no. is FB9220496. I have also attached the requirements.txt file of my env and 2 py. files of NLP models in the feedback. Those files shall help you to reproduce this issue more easily.
Sincerely,
hawkiyc