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.
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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