[TuringNYC]

We've followed five different instructional and documentation pages to make it happen and none seem to consistently install. Throw in a corporate system where you need IT for root access to make changes and it is game over. So i've got an M1-max fully loaded and cant get TF running on it.

Now i've got a team of data scientists in a fully MBP shop and we're holding off upgrades to M1 until this all gets resolved.

On my personal M1, I managed to make it work, but its hard to know the layers of changes made and what exactly allowed it to work.

[zamalek]

You can get off this GPU circus and simply go with purpose-built AI solutions.

You can buy single tensor accelerators from Google: https://www.coral.ai/products/

You can buy a bunch of those integrated into a single PCI-E card. https://iot.asus.com/products/AI-accelerator/AI-Accelerator-...

Cheap too. Some of these work with Mac. More of them work for PC, because the hardware interface is outside of Apple's thin vertical slice/garden.

[dwroberts]

These are devices for Tensorflow Lite which is more appropriate for IoT etc. not doing the intensive initial training of a complex model

[fouc]

Could be worth tracking what you did and make a new set of instructions, and trying to reproduce with a fresh install.

[freemint]

This is something Apple should pay people for.

[hedgehog]

Deep learning support for Mac is not going to happen at a level of quality you can rely on for research & dev work (like PyTorch + TensorFlow). The underlying problem is no big company cares about Mac platform and the work to maintain framework support for a specific piece of hardware is way beyond a hobby project. If you want your own on-prem hardware just buy Nvidia.