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API Summary

Summary of public functions and classes exposed in ONNX Runtime.

IOBinding

By default, ONNX Runtime always places input(s) and output(s) on CPU, which is not optimal if the input or output is consumed and produced on a device other than CPU because it introduces data copy between CPU and the device. ONNX Runtime provides a feature, IO Binding, which addresses this issue by enabling users to specify which device to place input(s) and output(s) on. Here are scenarios to use this feature.

(In the following code snippets, model.onnx is the model to execute, X is the input data to feed, and Y is the output data.)

Scenario 1:

A graph is executed on a deivce other than CPU, for instance CUDA. Users can use IOBinding to put input on CUDA as the follows.

#X is numpy array on cpu
session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
io_binding.bind_cpu_input('input', X)
io_binding.bind_output('output')
session.run_with_iobinding(io_binding)
Y = io_binding.copy_outputs_to_cpu()[0]

Scenario 2:

The input data is on a device, users direclty use the input. The output data is on CPU.

session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
io_binding.bind_input(name='input', device_type=X.device.type, device_id=0, element_type=np.float32, shape=list(X.size()), buffer_ptr=X.data_ptr())
io_binding.bind_output('output')
session.run_with_iobinding(io_binding)
Y = io_binding.copy_outputs_to_cpu()[0]

Scenario 3:

The input data on a dveice, users directly use the input and also place output on the device:

session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
io_binding.bind_input(name='input', device_type=X.device.type, device_id=0, element_type=np.float32, shape=list(X.size()), buffer_ptr=X.data_ptr())
io_binding.bind_output(name='output', device_type=Y.device.type, device_id=0, element_type=np.float32, shape=list(Y.size()), buffer_ptr=Y.data_ptr())
session.run_with_iobinding(io_binding)

Device

The package is compiled for a specific device, GPU or CPU. The CPU implementation includes optimizations such as MKL (Math Kernel Libary). The following function indicates the chosen option:

.. autofunction:: onnxruntime.get_device

Examples and datasets

The package contains a few models stored in ONNX format used in the documentation. These don't need to be downloaded as they are installed with the package.

.. autofunction:: onnxruntime.datasets.get_example

Load and run a model

ONNX Runtime reads a model saved in ONNX format. The main class InferenceSession wraps these functionalities in a single place.

.. autoclass:: onnxruntime.ModelMetadata
    :members:


.. autoclass:: onnxruntime.InferenceSession
    :members:


.. autoclass:: onnxruntime.NodeArg
    :members:


.. autoclass:: onnxruntime.RunOptions
    :members:


.. autoclass:: onnxruntime.SessionOptions
    :members:

Backend

In addition to the regular API which is optimized for performance and usability, ONNX Runtime also implements the ONNX backend API for verification of ONNX specification conformance. The following functions are supported:

.. autofunction:: onnxruntime.backend.is_compatible


.. autofunction:: onnxruntime.backend.prepare


.. autofunction:: onnxruntime.backend.run


.. autofunction:: onnxruntime.backend.supports_device