Plateform interoperability

Numpy interoperability

Thanks to PyBind11, there is a seemless interoperability with Numpy arrays, achieved by defining a buffer_protocol in the binding of AIDGE Tensor. This allows to use data loaded from an other frameworks which are compatible with Numpy.

PyTorch interoperability

AIDGE is interoperable with PyTorch and allows:

• Creating an AIDGE Tensor from a PyTorch Tensor

• Running an AIDGE graph view within the PyTorch environment.

PyTorch model interoperability

AIDGE is interoperable with PyTorch and allows running an AIDGE computational graph within the PyTorch environment. Model interoperability consists in wrapping a Torch module around an AIDGE graph view, which is done through a conversion step involving an ONNX file.

Here is a schema on how the interoperability works:

This feature requires a dependency to:

• torch

• torchvision

• onnx-simplifier

• onnx

PyTorch tensor interoperability

AIDGE can convert Torch tensors by using the Numpy interoperability. When a Torch Tensor is defined on GPU, it can be converted to AIDGE Tensor without copy on the device, if a AIDGE CUDA backend is available. This is possible because a pointer to the memory of the data on the GPU is accessible by the PyTorch API.

Keras interoperability

Keras model interoperability

Similarly to the the PyTorch interoperability, AIDGE defines an interoperability with Keras. AIDGE allows creating a wrapper from a Keras Model by performing a conversion step via an ONNX file.

This feature recquires a dependency to:

• tensorflow

• tf2onnx

• protobuff

Keras Tensor interoperability

AIDGE can convert Keras tensors by using the Numpy interoperability.