Re:Accelerating TensorFlow Performance on Mac
by syntaxing on Wednesday November 18, 2020 @04:13PM
(#25142507)
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.
Re:Accelerating TensorFlow Performance on Mac
by FPGAhacker on Wednesday November 18, 2020 @04:24PM
(#25142634)
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?
Re:Accelerating TensorFlow Performance on Mac
by meowphius on Wednesday November 18, 2020 @06:48PM
(#25144291)
The API of the neural engine is closed for some reason.
Re:Accelerating TensorFlow Performance on Mac
by therealmarv on Wednesday November 18, 2020 @08:00PM
(#25144965)
really? So WHO can use it then when not normal developers? Only Apple?
Re:Accelerating TensorFlow Performance on Mac
by my123 on Thursday November 19, 2020 @07:34AM
(#25148836)
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.
Re:Accelerating TensorFlow Performance on Mac
by skwb on Wednesday November 18, 2020 @07:35PM
(#25144747)
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.
Re:Accelerating TensorFlow Performance on Mac
by wffurr on Wednesday November 18, 2020 @08:31PM
(#25145192)
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...
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.