Avoid hardcoding specific device types like ‘cuda’ in AI code. Instead, use device-agnostic approaches such as device type variables or accelerator detection functions. This ensures code runs efficiently across different hardware accelerators (CUDA, ROCm, XPU, etc.) without modification.
Examples:
# Instead of this:
x = torch.randn(100, 100, device='cuda')
# Do this:
x = torch.randn(100, 100, device=GPU_TYPE)
# Or for more dynamic detection:
device = torch.accelerator.current_accelerator().type if torch.accelerator.current_accelerator() else "cpu"
x = torch.randn(100, 100, device=device)
This approach helps maintain compatibility with various AI hardware acceleration platforms and simplifies testing across multiple device types. For common test cases, prefer using constants like GPU_TYPE defined in testing utilities, and for production code, use the accelerator API to detect available devices at runtime.
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