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9. GPU Support in PyTorch for NVIDIA and MacOs
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NVIDIA GPUs (CUDA): This code works for NVIDIA GPUs because it checks for CUDA availability using
torch.cuda.is_available(). If a CUDA-enabled GPU is available, it sets the device to"cuda", allowing PyTorch to use the GPU for tensor operations. -
Mac GPUs:
- Metal API: On macOS, GPUs are generally accessed via Apple’s Metal API. As of now, PyTorch does not natively support Metal. However, Apple Silicon (M1/M2 chips) can be utilized for accelerated computations through the
mps(Metal Performance Shaders) backend in PyTorch.
- Metal API: On macOS, GPUs are generally accessed via Apple’s Metal API. As of now, PyTorch does not natively support Metal. However, Apple Silicon (M1/M2 chips) can be utilized for accelerated computations through the
Cross-Platform Device Selection
If you want to write code that is cross-platform and can use CUDA on NVIDIA GPUs, mps on Apple Silicon, and CPU otherwise, you can do something like this:
Summary
cuda: Specific to NVIDIA GPUs with CUDA support.mps: Used for Apple Silicon GPUs via the Metal API.cpu: Used when no GPU is available or supported.