Machine Learning Framework PyTorch Enabling GPU-Accelerated Training on Apple Silicon Macs

In collaboration with the Metal engineering team at Apple, PyTorch today announced that its open source machine learning framework will soon support GPU-accelerated model training on Apple silicon Macs powered by M1, M1 Pro, M1 Max, or M1 Ultra chips.

Until now, PyTorch training on the Mac only leveraged the CPU, but an upcoming version will allow developers and researchers to take advantage of the integrated GPU in Apple silicon chips for "significantly faster" model training.

A preview build of PyTorch version 1.12 with GPU-accelerated training is available for Apple silicon Macs running macOS 12.3 or later with a native version of Python. Performance comparisons and additional details are available on PyTorch's website.

Tag: Metal

Top Rated Comments

innominato5090 Avatar
14 months ago
So unbelievably excited about this. Being able to test GPU-accelerated code locally (and even train some smaller networks) rather than having to rely on a unix-server will speed up ML development significantly.

Can't wait for it to become mature enough to be merged in a stable branch.
Score: 13 Votes (Like | Disagree)
mukiex Avatar
14 months ago
Personally I'm hoping to see support for the neural engine in the future. When they added NE support to Topaz, my M1 Macbook Air suddenly started performing on par with my desktop GTX 1080.
Score: 5 Votes (Like | Disagree)
14 months ago
I hope to be blown away with what wwdc has to announce
Score: 4 Votes (Like | Disagree)
adamw Avatar
14 months ago
Always good to see move native support for Metal in MacOS on M1 family of chips...
Score: 3 Votes (Like | Disagree)
seek3r Avatar
14 months ago
Nice step in the direction of finding out how capable these GPUs are for this kind of workload, may also give an idea if they work well enough for GPGPU and ML work to infer if Apple is likely to develop their own GPU for the eventual AS Mac Pro
Score: 3 Votes (Like | Disagree)
Luca1995it Avatar
14 months ago
I started collecting benchmarks of M1 Max on PyTorch here:
Score: 2 Votes (Like | Disagree)