cuquantum.NetworkOptions

class cuquantum.NetworkOptions(compute_type: Optional[int] = None, device_id: Optional[int] = None, handle: Optional[int] = None, logger: Optional[logging.Logger] = None, memory_limit: Optional[Union[int, str]] = '80%', blocking: Literal[True, 'auto'] = True, allocator: Optional[cuquantum.cutensornet.memory.BaseCUDAMemoryManager] = None)

A data class for providing options to the cuquantum.Network object.

compute_type

CUDA compute type. A suitable compute type will be selected if not specified.

Type

cuquantum.ComputeType

device_id

CUDA device ordinal (used if the tensor network resides on the CPU). Device 0 will be used if not specified.

Type

Optional[int]

handle

cuTensorNet library handle. A handle will be created if one is not provided.

Type

Optional[int]

logger

Python Logger object. The root logger will be used if a logger object is not provided.

Type

logging.Logger

memory_limit

Maximum memory available to cuTensorNet. It can be specified as a value (with optional suffix like K[iB], M[iB], G[iB]) or as a percentage. The default is 80%.

Type

Optional[Union[int, str]]

blocking

A flag specifying the behavior of the execution methods Network.autotune() and Network.contract(). When blocking is True, these methods do not return until the operation is complete. When blocking is "auto", the methods return immediately when the input tensors are on the GPU. The execution methods always block when the input tensors are on the CPU. The default is True.

Type

Literal[True, ‘auto’]

allocator

An object that supports the BaseCUDAMemoryManager protocol, used to draw device memory. If an allocator is not provided, a memory allocator from the library package will be used (torch.cuda.caching_allocator_alloc() for PyTorch operands, cupy.cuda.alloc() otherwise).

Type

Optional[cuquantum.cutensornet.memory.BaseCUDAMemoryManager]