Always verify CUDA availability before performing CUDA-specific operations to prevent runtime errors when code runs on systems without CUDA support. When working with device-specific code: 1. Add explicit checks with `torch.cuda.is_available()` before any CUDA-specific operations 2. Use appropriate device properties rather than just device names for better cross-platform compatibility 3. Handle device-like parameters consistently using proper abstractions Example: ```python # Bad - may fail on CPU-only systems if torch.cuda.is_current_stream_capturing(): # CUDA-specific code # Good - safely checks CUDA availability first if torch.cuda.is_available() and torch.cuda.is_current_stream_capturing(): # CUDA-specific code # Better device property handling - use specific properties instead of just names device = input_nodes[0].get_device() if device.type == "cuda": # Use specific device properties for hardware identification device_properties = torch.cuda.get_device_properties(device.index) # Use properties like device_properties.major, device_properties.minor, etc. ``` This approach ensures code works reliably across different hardware configurations and gracefully handles CPU-only environments.