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Comment by docfort | Hacker News Reader

You might be interested in this for your M1 MBA: https://github.com/apple/tensorflow_macos

That was actually what I was referring to! It's super weird. I like it, and it's what I use primarily, but only because no other version of tensorflow will install.

Their compiler is closed source, so it's almost impossible to tell what's going on. But, when I connect using tf.Session(), then .list_devices() shows only CPU available.

However, when I enable the TF_MLC_LOGGING=1 magic variable, it does seem to be printing out messages that indicates it's doing some kind of graph substitution under the hood. Therefore, I assume that this is the intended usage mode.

In other words, there seems to be zero difference between the "CPU" and the "GPU". Normally you can say "Do this on the CPU" while "do that on the GPU." But not with this.

Hopefully they'll open source the code sometime this century so that it's clearer what the heck it's doing. For now, though, it's reasonably fast in whatever this "CPU" mode is -- I only need to run unit tests on my laptop anyway, since all training happens on TPUs. So I ended up happy.

(For the first day or so, I was panicking that I was going to have no working tensorflow whatsoever on my M1 laptop, which would've necessitated a swift return + substitution.)

That seems very apple honestly, they’d rather you emit pure metal primitives (is that the right term? Idk) and then let their backend schedule on their soc- because for all we know, they have some special asic ip that they added/or will add and can take advantage.

With the disclaimer that I know nothing about this: doesn't the M1 have a separate "Neural Engine" for this? So it's using neither the CPU nor the GPU cores for TensorFlow?

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