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)[source]¶

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% of the device memory.

Type: Optional[Union[int, str]]

blocking¶

A flag specifying the behavior of the execution functions and methods, such as 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]