How to Enable GPU-Accelerated Training on Apple Silicon in PyTorch

PyTorch 1.12 introduced experimental support for Apple Silicon M1 and M2 processors through its Metal Performance Shaders (MPS) backend, allowing compatible Macs to accelerate model training using their integrated Metal GPUs rather than CPUs. PyTorch Lightning 1.7 simplifies use of this backend with little or no code modification, while benchmarks from PyTorch and the community indicate potentially significant performance gains for workloads such as computer vision and natural-language processing. Users can verify compatibility through their Mac’s system information and must ensure that Python and Conda installations use ARM rather than Intel architecture. Apple Silicon combines CPU, GPU, shared memory, and other components into a tightly integrated system-on-a-chip, and MPS translates PyTorch operations into Metal shader kernels for parallel execution. Support remains experimental because some PyTorch operators are not yet available, though nightly PyTorch releases may provide broader compatibility as development continues.

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