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Update api_summary.rst (#6038)

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Hariharan Seshadri 2020-12-04 17:59:56 -08:00 committed by GitHub
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@ -11,14 +11,14 @@ in *ONNX Runtime*.
OrtValue
=========
*ONNX Runtime* works with native Python data structures which are mapped into ONNX ONNX data formats :
*ONNX Runtime* works with native Python data structures which are mapped into 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.
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:
@ -99,7 +99,7 @@ The input data and output data are both on a device, users directly use the inpu
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()` API to get the *OrtValue*(s) corresponding to the allocated output(s).
Users can use the *get_outputs()* API to get access to the *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
@ -118,7 +118,7 @@ Users can thus consume the *ONNX Runtime* allocated memory for the output as an
Scenario 5:
Users can bind *OrtValue*(s) directly.
Users can bind *OrtValue* (s) directly.
.. code-block:: python