pyhelios.runtime.gpu_detector Namespace Reference

Functions
bool	is_gpu_runtime_available ()
	Check if GPU hardware and drivers are actually available at runtime.
Dict[str, any]	get_gpu_runtime_info ()
	Get detailed information about GPU runtime capabilities.
Dict[str, any]	_check_cuda_runtime ()
	Check CUDA runtime availability by trying to initialize CUDA.
bool	_check_opencl_runtime ()
	Check OpenCL runtime availability.

Variables
	logger = logging.getLogger(__name__)

Function Documentation

_check_cuda_runtime()

Dict[str, any] pyhelios.runtime.gpu_detector._check_cuda_runtime ( )

protected

Check CUDA runtime availability by trying to initialize CUDA.

Returns

Dict with CUDA runtime information

Definition at line 80 of file gpu_detector.py.

_check_opencl_runtime()

bool pyhelios.runtime.gpu_detector._check_opencl_runtime ( )

protected

Check OpenCL runtime availability.

Returns

bool True if OpenCL is available, False otherwise

Definition at line 192 of file gpu_detector.py.

get_gpu_runtime_info()

Dict[str, any] pyhelios.runtime.gpu_detector.get_gpu_runtime_info

(

)

Get detailed information about GPU runtime capabilities.

Returns

Dict containing GPU runtime information:

• cuda_runtime_available: bool
• cuda_device_count: int
• cuda_version: str
• opencl_available: bool
• platform: str
• error_message: str (if any errors)

Definition at line 46 of file gpu_detector.py.

is_gpu_runtime_available()

bool pyhelios.runtime.gpu_detector.is_gpu_runtime_available

(

)

Check if GPU hardware and drivers are actually available at runtime.

This is different from build-time CUDA availability - this checks if the GPU can actually be used for computation at runtime.

Returns

bool True if GPU is available for runtime use, False otherwise

Definition at line 25 of file gpu_detector.py.

Variable Documentation

logger

pyhelios.runtime.gpu_detector.logger = logging.getLogger(__name__)

Definition at line 13 of file gpu_detector.py.