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GPU-Acceleration Comes to PyTorch on M1 Macs
How do the new M1 chips perform with the new PyTorch update?
The release of M1 Macs in November 2020 marked a significant step up in the processing power of Apple machines [1]. Unfortunately, these new features were not integrated into PyTorch until now.
Today’s deep learning models owe a great deal of their exponential performance gains to ever increasing model sizes. Those larger models require more computations to train and run.
These models are simply too big to be run on CPU hardware, which performs large step-by-step computations. Instead, they need massively parallel computations, like those performed by GPUs.
GPUs use a highly parallel structure, originally designed to process images for visual heavy processes. They became essential components in gaming for rendering real-time 3D images.
That ability to render 3D images works well with the multi-dimensional computations required in deep learning models. Naturally, GPUs became the go to architecture for model…
