I haven’t tried out the performance yet but it appears PyTorch supports the apple silicon processors now as a separate device named ‘mps’ similar to cuda for Nvidia gpus. There is also a tensor flow plugin that can be separately installed to take advantage of the apple chips

So there’s a few ways to think about this and some things to know

A) Apple Neural Engine is designed for inference workloads and not back prop or training as far as I’m aware.

B) This means only GPU or CPU for training for DL

C) You can get partial GPU accceleration using pytorch and tensorflow but neither are fully optimized or really competitive.

D) you can accept the training wheels (pun intended) and train simple models using createML GUI which has about as good as you’ll get M series support for GPU but is woefully out of date for many classes of problems and doesn’t support arbitrary layers, losses,optimizers etc. it’s a black box.

E) You can use createML api to get a tad more control but not much more.

If you’re interested in coreML for inference I will say from experience model conversion is non trivial if you want performance as some layers don’t always convert appropriately and shapes can’t always be deduced depending on the models source code.

Also CoreML inference in python doesn’t properly support batching. I’m not joking.

All in all if you get simple shit working it’s fast, but if you want anything remotely nuanced or not out of the box you’re fucked unless you want to write custom metal re-implementations of things like NMS do you can get access to outputs Apples layers don’t supply.

Source: banging my head against fucking wall

It's not that worth it.

People with RTX cards will often rent cloud GPU instances for training when inference/training requires more than 24GB VRAM which it often does. Also, sometimes we just need to shorten the training time with 8x A100 so... yeah renting seems to be the only way to go.

I’ve found tensor flow to work on the GPU if you install everything correctly. PyTorch didn’t work on the GPU for my project because some functions aren’t supported yet. It does work fine with the cpu though.

ML is easy since it's mostly on the CPU. DL still remains shit, unless your definition of prototyping is verifying that the shapes match and that the network can do backprop and save weights at the end of an epoch.

Things are not going to change fast unless Macs start coming with Nvidia CUDA capable GPUs.

Depends on exactly what “prototyping” means to you.

For just pushing layers around and stuff it’s fine because you can just use CPU and verify that your model compiles and batches flow, etc etc.

For like “train for 5 epochs and tweak hyperparams” it’s tough. You can wait out CPU-only training. Or sometimes you can use the GPU in pytorch and that’s great when it works. But like, the pytorch LSTM layer is literally implemented wrong on MPS (that’s what the M1 GPU is called, equivalent to “CUDA”). So you’ll get shape errors lol.

Basically it’s fairly unstable to train on anything but CPU.

TBH the value of the M1 Macs is the RAM. Because CPU, GPU, and RAM are all on the same chip, you don’t have VRAM - just RAM. Not such a big deal on an M1 with 16 Gb, but perhaps something to consider when thinking about the kinds of models you can build on the M1 Max with 64 Gb or M1 Ultra with 128 Gb. The unified memory framework Apple uses is weird, and I haven’t seen aggressive testing of its limits. For example - does the M1 Ultra have 128 GB for GPUs, or the 64 Gb of each M1 Max component? Either way, if it is slower than a 3090, just use absurd batch sizes paired with a slightly faster learning rate.

That said, the big problem I’ve found is with OpenMP. It doesn’t adjust well to the dynamic CPU use Apple wants, and this leads to frequent crashes and/or low thread counts on anything more complex than an M1/M2 . With the RAM - CPU link XGBoost is an absolute beast until it crashes within a few minutes. There may be some leeway in setting threads lower, but hopefully development will catch up with the new chipsets, particularly since Intel is also pursuing the efficiency/performance core division.

It depends. I use pytorch and mostly it works. But sometimes I see a cool library and download it and try to run it and I get a C level error where it says "unsupported hardware" -- in which case I need to run that code on linux.

I think it should be fine since your laptop should just be a client for doing deep learning, and not the server. So whenever you have problems you can just test on a linux machine.

I've personally never written code myself that throws the unsupported hardware error. So it must be some specialty accelerated code that only works with intel or whatever. But yeah this hasn't been an issue for writing code, only for trying to use other people's code (and even then it's pretty rare, usually only happens when you clone from like nvidia or something).

This is not my field but I find this question genuinely surprising.

Why would one even consider this unless prototyping from the actual jungle?

In any other situation where you even just have a 3G connection then delegating to the cloud (or your own on premise machines available online behind a VPN) seems much more efficient as soon as you have any inference and even more so training to run.

Why do I feel the question itself to be surprising? Well because ML is a data based field so the question can be answered with a spreadsheet. Namely your "model" would be optimizing for faster feedback in order to learn better about your problem with as costs your hardware but also your time. If you do spend X hours tinkering with an M1 (or M2 or even "just" a 4090) versus A100 in that or a random cloud e.g AWS or a local OVH by booting on a generalist distribution like Ubuntu versus dedicated setups like lambdalabs.com or coreweave.com or even higher level like HuggingFace on their own infrastructure then IMHO that does give you some insight.

Everything else seems anecdotal because others might not have your workflow.

TL;DR: no unless they are minuscule models and of course if you use it to ssh on remote machines but IMHO you have to figure it out yourselves as we all have different needs.

PS: to clarify and not to sound like an opinionated idiot, even though it's not my field I did run and trains dozens of models locally and remotely.

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There's Apple's "Tensorflow Metal Plugin", which allows for running Tensorflow on Apple Silicon's graphics chip. However, it's basically unusably buggy; I'd recommend you to stay away from it:

For example, tf.sort only sorts up to 16 values and overwrites the rest with -0. Apparently not fixed for over one year: https://developer.apple.com/forums/thread/689299

Also, tf.random always returns the same random numbers: https://developer.apple.com/forums/thread/697057

I'm a little confused by some of the answers here, not sure if it's because this sub skews towards academia/early career or maybe I'm just out of touch.

Pretty much anywhere in the industry your $3000 M1 Mac is going to be used for either opening up a tab in Chrome or, at most, the rigorous task of SSHing into a Linux VM. There are basically two reasons:

• Large, public companies typically don't allow their user/production data to exist on any machine that has internet access--you can get in very deep shit or in some cases even fired for having it stored locally--so that's game over for a MBP automatically.
• Most companies of any size will have some need for distributed training. That means you need a platform to schedule training jobs, or else your GPUs will sit idle and you'll go bankrupt. That means maintaining compatible versions of TF/PT, CUDA, and GCC, which eventually means building their own distribution of TF/PT. They're not going to bother building a separate distro for two different flavors of MBP floating around in addition to production. Often, your model code doesn't work in the absence of the platform SDKs, because, for example, they need to be able to authenticate with S3 or HDFS or wherever the data is stored.

I'm not sure that any company besides Apple will ever invest in Apple's M1 shit specifically, but nobody uses TPUs and that doesn't stop Google from pushing it every chance they get. However, a lot of the industry is getting more and more pissed at NVIDIA, which may in turn open things up to local development in the future.

Tensorflow works quite well on Apple silicon since you can use the GPU

Keras works well with my M1 Max Mac Studio. Hasn’t struggled with anything I throw at it

As someone who actually owns an M1 and has a job running large models in the cloud - it's not nearly as bad as I was expecting. mps support in pytorch is growing every day, most recently I have been able to finetune various sentence transformers and GPT-J at reasonable speeds (before pushing to gpus in the cloud). If I was choosing the laptop I would go with linux + gpu obviously, but our mostly clueless executive chose the M1. The upside with the M1 is that I can use the 64gb of system memory for loading models whereas the most gpu memory I could get in a nvidia laptop is 16-24.

I tried fine tuning gpt2 on 1000 examples on my M1, I think it was supposed to be 10 hours versus 30 minutes on a V100 on Gcloud. Inference was comparable. I think there is a way out by installing the right drivers, but honestly, what would be the point in that for someone who works in industry? I am not planning to run production code trained on a MBP, so might as well develop cloud first.

I have a Mac Studio with the M1 max and it is like a 2x boost over my Intel i5 MBP on CPU. Couldn't get metal to work with TF, think it's half precision only and I didn't want to convert my model.

I think getting a M1 laptop and using Colab is probably a good choice. I could justify the paid subscription version if I needed the GPU compute.

Tensorflow models seem to work well on GPU (M1 Pro 32 GB memory). With Pytorch I have had difficulty getting the model to train on the GPU. CPU training for Pytorch models has been very slow.

The M series is a generation or two slower compared to the Nvidia GPU

From my experience:

Inference is good enough, I am using mac mini m1 for it.
Training is hell.
Also a lot of things are still not working, in for example in PyTorch, so be prepared to fight hard with it.
It is cheaper and better to go along with some serious well supported hardware.

My current setup - I have "expensive" local server for ML and stuff, I am connecting to it and work remotely, while my laptops are light and cheap.

I’ve been using an M1 for prototyping and found a couple of issues with some PyTorch models. It’s a buggy mess at times and even their fallback doesn’t work at times. Here are a few things not implemented yet - these do show up decently often as you see in that GitHub issue

I'm prototyping on an M1 mac for work and it's not great. One of the main issues is if you work in a team with multiple chip architectures, it adds a whole layer of complexity to dependency management just to collaborate.