2018-11-20 00:48:22 +00:00
===========
API Summary
===========
Summary of public functions and classes exposed
in *ONNX Runtime* .
.. contents ::
:local:
2020-10-07 04:14:41 +00:00
OrtValue
=========
*ONNX Runtime* works with native Python data structures which are mapped into ONNX ONNX data formats :
Numpy arrays (tensors), dictionaries (maps), and a list of Numpy arrays (sequences).
The data backing these are on CPU.
*ONNX Runtime* supports a custom data structure that supports all ONNX data formats that allows users
to place the data backing these on a device, for example, on a CUDA supported device. This allows for
interesting *IOBinding* scenarios (discussed below). In addition, *ONNX Runtime* supports directly
working with *OrtValue* (s) while inferencing a model if provided as part of the input feed.
Below is an example showing creation of an *OrtValue* from a Numpy array while placing its backing memory
on a CUDA device:
.. code-block :: python
#X is numpy array on cpu, create an OrtValue and place it on cuda device id = 0
ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
ortvalue.device_name() # 'cuda'
ortvalue.shape() # shape of the numpy array X
ortvalue.data_type() # 'tensor(float)'
ortvalue.is_tensor() # 'True'
np.array_equal(ortvalue.numpy(), X) # 'True'
#ortvalue can be provided as part of the input feed to a model
ses = onnxruntime.InferenceSession('model.onnx')
res = sess.run(["Y"], {"X": ortvalue})
2020-07-08 10:48:22 +00:00
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:
2020-10-07 04:14:41 +00:00
A graph is executed on a device other than CPU, for instance CUDA. Users can
2020-07-08 10:48:22 +00:00
use IOBinding to put input on CUDA as the follows.
.. code-block :: python
#X is numpy array on cpu
session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
2020-10-07 04:14:41 +00:00
# OnnxRuntime will copy the data over to the CUDA device if 'input' is consumed by nodes on the CUDA device
2020-07-08 10:48:22 +00:00
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:
2020-10-07 04:14:41 +00:00
The input data is on a device, users directly use the input. The output data is on CPU.
2020-07-08 10:48:22 +00:00
.. code-block :: python
2020-10-07 04:14:41 +00:00
#X is numpy array on cpu
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
2020-07-08 10:48:22 +00:00
session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
2020-10-07 04:14:41 +00:00
io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
2020-07-08 10:48:22 +00:00
io_binding.bind_output('output')
session.run_with_iobinding(io_binding)
Y = io_binding.copy_outputs_to_cpu()[0]
Scenario 3:
2020-10-07 04:14:41 +00:00
The input data and output data are both on a device, users directly use the input and also place output on the device.
.. code-block :: python
#X is numpy array on cpu
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
Y_ortvalue = onnxruntime.OrtValue.ortvalue_from_shape_and_type([3, 2], np.float32, 'cuda', 0) # Change the shape to the actual shape of the output being bound
session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
io_binding.bind_output(name='output', device_type=Y_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=Y_ortvalue.shape(), buffer_ptr=Y_ortvalue.data_ptr())
session.run_with_iobinding(io_binding)
Scenario 4:
Users can request *ONNX Runtime* to allocate an output on a device. This is particularly useful for dynamic shaped outputs.
Users can use the `get_outputs()` *OrtValue* (s) corresponding to the allocated output(s).
Users can thus consume the *ONNX Runtime* allocated memory for the output as an *OrtValue* .
.. code-block :: python
#X is numpy array on cpu
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
io_binding.bind_input(name='input', device_type=X_ortvalue.device_name(), device_id=0, element_type=np.float32, shape=X_ortvalue.shape(), buffer_ptr=X_ortvalue.data_ptr())
#Request ONNX Runtime to bind and allocate memory on CUDA for 'output'
io_binding.bind_output('output', 'cuda')
session.run_with_iobinding(io_binding)
# The following call returns an OrtValue which has data allocated by ONNX Runtime on CUDA
ort_output = io_binding.get_outputs()[0]
Scenario 5:
Users can bind *OrtValue* (s) directly.
2020-07-08 10:48:22 +00:00
.. code-block :: python
2020-10-07 04:14:41 +00:00
#X is numpy array on cpu
#X is numpy array on cpu
X_ortvalue = onnxruntime.OrtValue.ortvalue_from_numpy(X, 'cuda', 0)
Y_ortvalue = onnxruntime.OrtValue.ortvalue_from_shape_and_type([3, 2], np.float32, 'cuda', 0) # Change the shape to the actual shape of the output being bound
2020-07-08 10:48:22 +00:00
session = onnxruntime.InferenceSession('model.onnx')
io_binding = session.io_binding()
2020-10-07 04:14:41 +00:00
io_binding.bind_ortvalue_input('input', X_ortvalue)
io_binding.bind_ortvalue_output('output', Y_ortvalue)
2020-07-08 10:48:22 +00:00
session.run_with_iobinding(io_binding)
2018-11-20 00:48:22 +00:00
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 <https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md> `_
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
