5 ms·

It also looks like they are in the progress of adding Apple metal support, possibly for the M1: Part of 1.8 is this issue: https://github.com/pytorch/pytorch/

It also looks like they are in the progress of adding Apple metal support, possibly for the M1: Part of 1.8 is this issue: https://github.com/pytorch/pytorch/pull/47635 https://github.com/pytorch/pytorch/pull/47635

ipsum2 6y ago
What's the point? If you have enough money to buy a brand new Apple M1 laptop, you can afford a training rig or cloud credits. Any modern discrete GPU will blow away any M1 laptop for training. Is anyone training ML models on their ultra-thin laptop?
- upbeat_general 6y ago
  I don’t understand this logic. If someone has $1000 for an entry level m1 machine then they also have enough money for a separate rig with a GPU that’s probably another $600-1000 for something decent? Cloud GPUs are also pretty expensive. I don’t think anyone is seriously training their ML models on their ultra thin laptop but I think the ability to do so would make it easier for lots of people to get started with what they have. It might be that they don’t have lots of extra cash to throw around or it’s hard to justify on something they are just starting. Cloud training is also just less convenient and an extra hassle compared to doing things locally, even if it’s something like google collab which is probably the best option right now for training on thin laptops
  - ipsum2 6y ago
    If someone is shelling out for a brand new, early adopter product, then they probably have a decent amount of money. Even when TensorFlow and PyTorch implement training support on the M1, it will be useless for practically anything except training 2-3 layer models on MNIST. So why should valuable engineering time be spent on this?
    - tpetry 6y ago
      Because with support for M1 you can prototype your network on your local machine with „good“ performance. There are many cloud solutions etc. but for convenience nothing beats your local machine. You can use an IDE you like etc.
      - rfoo 6y ago
        Because contrary to what you believe, M1 simply is not performant enough to be used to "prototype" your network. NNs can't be simply scaled up and down. It is *NOT* like those web apps which you can run on potatoes just fine as long as nobody are hitting them heavily.
        rsfern 6y ago
        Sure you probably don’t want to do full training runs locally, but There’s a lot you can do locally that has a lot of added friction on a gpu cluster or other remote compute resource I like to start a new project by prototyping and debugging my training and cunning config code, setting up the data loading and evaluation pipeline, hacking around with some baseline models and making sure they can overfit some small subset of my data After all that’s done it’s finally time to scale out to the gpu cluster. But I still do a lot of debugging locally Maybe this kind of workflow isn’t as necessary if you have a task that’s pretty plug and play like image classification, but for nonstandard tasks I think there’s lots of prototyping work that doesn’t require hardware acceleration
        jefft255 6y ago
        Coding somewhat locally is a must for me too because the cluster I have access to has pretty serious wait times (up to a couple hours on busy days). Imagine only being able to run the code you’re writing a few times a day at most! Iterative development and doing a lot of mistakes is how I code; I don’t want to go back to punch card days where you waited and waited before you ended up with a silly error.
        tpetry 6y ago
- currymj 6y ago
  I use PyTorch for small-ish models, not really the typical enormous deep learning workload where you let it train for days. I still prefer to work on a GPU workstation because it's the difference between running an experiment in minutes vs hours, makes it easier to iterate. Some of that speed on a low-power laptop would be great. Much less friction.
- volta83 6y ago
  > Any modern discrete GPU will blow away any M1 laptop for training. Counter example: none of the AMD consumer GPUs can be used for training. So no, not any modern discrete GPU blows away the M1. The M1 might not be a great GPGPU system, but it is much better than many systems with discrete GPUs.

ipsum2 6y ago

What's the point? If you have enough money to buy a brand new Apple M1 laptop, you can afford a training rig or cloud credits. Any modern discrete GPU will blow away any M1 laptop for training. Is anyone training ML models on their ultra-thin laptop?

upbeat_general 6y ago
I don’t understand this logic. If someone has $1000 for an entry level m1 machine then they also have enough money for a separate rig with a GPU that’s probably another $600-1000 for something decent? Cloud GPUs are also pretty expensive. I don’t think anyone is seriously training their ML models on their ultra thin laptop but I think the ability to do so would make it easier for lots of people to get started with what they have. It might be that they don’t have lots of extra cash to throw around or it’s hard to justify on something they are just starting. Cloud training is also just less convenient and an extra hassle compared to doing things locally, even if it’s something like google collab which is probably the best option right now for training on thin laptops
- ipsum2 6y ago
  If someone is shelling out for a brand new, early adopter product, then they probably have a decent amount of money. Even when TensorFlow and PyTorch implement training support on the M1, it will be useless for practically anything except training 2-3 layer models on MNIST. So why should valuable engineering time be spent on this?
  - tpetry 6y ago
    Because with support for M1 you can prototype your network on your local machine with „good“ performance. There are many cloud solutions etc. but for convenience nothing beats your local machine. You can use an IDE you like etc.
    - rfoo 6y ago
      Because contrary to what you believe, M1 simply is not performant enough to be used to "prototype" your network. NNs can't be simply scaled up and down. It is *NOT* like those web apps which you can run on potatoes just fine as long as nobody are hitting them heavily.
      - rsfern 6y ago
        Sure you probably don’t want to do full training runs locally, but There’s a lot you can do locally that has a lot of added friction on a gpu cluster or other remote compute resource I like to start a new project by prototyping and debugging my training and cunning config code, setting up the data loading and evaluation pipeline, hacking around with some baseline models and making sure they can overfit some small subset of my data After all that’s done it’s finally time to scale out to the gpu cluster. But I still do a lot of debugging locally Maybe this kind of workflow isn’t as necessary if you have a task that’s pretty plug and play like image classification, but for nonstandard tasks I think there’s lots of prototyping work that doesn’t require hardware acceleration
        jefft255 6y ago
        Coding somewhat locally is a must for me too because the cluster I have access to has pretty serious wait times (up to a couple hours on busy days). Imagine only being able to run the code you’re writing a few times a day at most! Iterative development and doing a lot of mistakes is how I code; I don’t want to go back to punch card days where you waited and waited before you ended up with a silly error.
      - tpetry 6y ago
currymj 6y ago
I use PyTorch for small-ish models, not really the typical enormous deep learning workload where you let it train for days. I still prefer to work on a GPU workstation because it's the difference between running an experiment in minutes vs hours, makes it easier to iterate. Some of that speed on a low-power laptop would be great. Much less friction.
volta83 6y ago
> Any modern discrete GPU will blow away any M1 laptop for training. Counter example: none of the AMD consumer GPUs can be used for training. So no, not any modern discrete GPU blows away the M1. The M1 might not be a great GPGPU system, but it is much better than many systems with discrete GPUs.