I was under the impression that outside toy problems, you can't really get decent results unless you have a TPU or fleet of GPU's for training. If you just train on a laptop you're shooting yourself in the foot compared to someone who gets the same job done 10x faster in the cloud (usually with free credits).
Obviously inference is still possible on a laptop for many models... but not many 'hacker' tasks involve only inference.
Further, in general, I’m really charmed by the potential of having unified memory; the idea you can test some batch training iterations with a batch size that fills almost all 128MB is a unique capability!
Last, a large amount of unified memory allows to do inference and “prompt engineering” with very large models, locally. E.g. Using GPT-J like models (6B parameters).
(1) development - quickly train smaller networks locally to validate the code. Then offload to a big GPU machine. (2) Smaller convolutional networks and RNNs may be below SOTA, but can still provide a good performance-accuracy trade-off. You could already train these networks pretty fast on an M1 thanks to AMX, but his makes training such networks even faster.
This is most certainly not the case. It's impossible to achieve SOTA on most benchmarks but there's a lot more ML and data analysis done on smaller datasets where you don't particularly need a fleet of GPUs.
https://softology.pro/tutorials/tensorflow/tensorflow.htm
It handles (most) of the installation issues and gives you a GUI. You can however bypass the GUI. Every time you run a command the console shows you what the command line looks like so copy/paste into a terminal and away you go.
I don't know how much you've done with running ml jobs on cloud boxes but it can be very annoying to work with them when you are actively iterating. Especially if you are doing a lot of work outside the cloud box environment. Even turn key consumer products like Colab can fall over if you accidentally press "Back" in your browser... I once lost many hours of work because I inadvertently swiped left :)
Apple will continue improving their performance in this space and once jobs that run for hours can be run overnight... I suspect people will often choose to run overnight simply for the ergonomics benefit.
So absolutely not mature, but having PyTorch working better also on macs is only positive imho.