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.

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...

> Nevertheless, this is pretty neat minus the RAM implications because realistically, I'm guessing you can use 5GB of RAM at most for training

This will only be an issue for the first few months of the transition to Apple Silicon. Upcoming updates to the rest of the lineup will undoubtedly have more unified memory

I'm torn because of this. The mac mini just got on sale at Costco for $649. Really tempted to purchase it now but intuition is telling me to wait for the 16GB RAM.

You can already spec the M1 with 16GB, it’s just pretty pricey.

I don’t even want to think what the 32GB and above will cost.

I mean, I got the 15" 2018 MBP with 32GB i9 500GB w/ dedicated graphics for $3,899.00. A MBP 13" with 16GB and 1TB is only $1,899.00. I'd fall over myself to buy a M2/M1Z (whatever they call it) with 32-64GB ram and 1TB ssd for as much as I paid for my last MBP. I honestly couldn't be happier that the new 13 MBP doesn't fit my needs or I'd be very tempted to get one. Thankfully now I can wait for the virtualization story to stabilize, wait for more ram, and wait for more external display support (all of which I need).

I know that Apple charges a premium for their upgrades but from everything I've seen so far from the new 13" MBP I'll be happy to pay that premium this time around. I have always found the comparisons of ram/ssd in recent Apple MBP's to generic parts to be slightly disingenuous. Raw speeds don't really matter as much as how well everything works together in my experience and the new AS computers seem to take that to an even higher level.

It's not quite what you're asking for, but there's a comparison to the 1050Ti: https://www.macrumors.com/2020/11/16/m1-beats-geforce-gtx-10...

Does graphic performance indicated ML training performance (to a certain degree)? Is it fair to say that if graphically it performs better, the ML training will be better with the proper software support?