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Comment by tpetry | Hacker News Reader

Seems i am not the only one who wants to know whether the M1 neural engine is performant enough for prototyping :)

It will be interesting how long the other deep learning frameworks will need to support the M1. Pytorch has not yet achieved comparable performance on a TPU compared to tensorflow.

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It would be pretty awesome to write an app, train the network, and deploy it using the same computer! I sometimes try to use google Colab to train some fun stuff (like style transfer) but sometimes it gives me a headache (grateful it is free though!!) It's hard to predict if the session will just stop and persistent data is a pain. Having an affordable (ironic using this word with Apple) for ML prototyping would be amazing.

You know, they don’t mention the neural engine. I know very little of ML but maybe the neural engine isn’t helpful on the training side?

The API of the neural engine is closed for some reason.

really? So WHO can use it then when not normal developers? Only Apple?

It is not. See https://developer.apple.com/documentation/coreml and https://github.com/apple/coremltools.

The neural engine on the Apple A11 wasn't exposed to apps at least at launch, but that's no longer a thing on A12 onwards.

My gut instincts (based off of nothing but pure speculation and being a ML developer for several years) says no.

For one, the M1 engine has 16 cores, vs over 1,500 for a 1660 series NVIDA card. I know it may not be apples to apples, but I have a very hard time believing it would be able to keep up with even the most marginal card for training.

I don’t know about the neural engine, but the GPU on the M1 has 128 EUs in each of the 8 cores, making for 1024 EUs total. It’s not quite in the same league as a 1660 Ti but it’s close: https://www.anandtech.com/show/16252/mac-mini-apple-m1-teste...

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