arraybridge
ArrayBridge provides explicit conversion and shared lifecycle utilities for NumPy, CuPy, PyTorch, TensorFlow, JAX, and pyclesperanto arrays.
Core dependencies are NumPy and metaclass-registry. Other frameworks are optional.
Quick start
import numpy as np
from arraybridge import convert_memory, detect_memory_type
value = np.arange(6).reshape(2, 3)
assert detect_memory_type(value) == "numpy"
copy = convert_memory(
value,
source_type="numpy",
target_type="numpy",
gpu_id=0,
)
convert_memory requires the declared source type, target type, and device id.
It uses the registered converter for the source framework. The device id is
required even for CPU conversions so call sites have one stable signature.
Declarative decorators
from arraybridge import numpy
@numpy
def normalize(image):
return image / max(float(image.max()), 1.0)
The framework decorators attach input_memory_type and output_memory_type
metadata, provide dtype/slice runtime parameters, and add framework-specific
stream/OOM handling where supported. They do not convert inputs or outputs
between frameworks and do not accept a gpu_id argument. A host runtime must
call convert_memory at the boundary it plans.
Stack utilities
import numpy as np
from arraybridge import stack_slices, unstack_slices
slices = [np.zeros((8, 8)), np.ones((8, 8))]
stack = stack_slices(slices, memory_type="numpy", gpu_id=0)
restored = unstack_slices(stack, memory_type="numpy", gpu_id=0)
stack_slices requires non-empty 2D inputs. unstack_slices requires a 3D
array. Both validate shape and use explicit target memory/device declarations.
Installation
pip install arraybridge
pip install "arraybridge[torch]"
pip install "arraybridge[cupy]"
Documentation: https://arraybridge.readthedocs.io
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