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:
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:
torch.cuda.is_available() before any CUDA-specific operationsExample:
# 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.
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