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1.9 KiB
(l-python-onnx-api)=
API Reference
The [ir-py project](https://github.com/onnx/ir-py) provides alternative Pythonic APIs for creating and manipulating ONNX models without interaction with Protobuf.
Versioning
The following example shows how to retrieve onnx version,
the onnx opset, the IR version. Every new major release increments the opset version
(see {ref}l-api-opset-version).
.. exec_code::
from onnx import __version__, IR_VERSION
from onnx.defs import onnx_opset_version
print(f"onnx.__version__={__version__!r}, opset={onnx_opset_version()}, IR_VERSION={IR_VERSION}")
The intermediate representation (IR) specification is the abstract model for graphs and operators and the concrete format that represents them. Adding a structure or modifying one of them increases the IR version.
The opset version increases when an operator is added or removed or modified. A higher opset means a longer list of operators and more options to implement an ONNX functions. An operator is usually modified because it supports more input and output type, or an attribute becomes an input.
Data Structures
Every ONNX object is defined based on a protobuf message
and has a name ended with suffix Proto. For example, {ref}l-nodeproto defines
an operator, {ref}l-tensorproto defines a tensor. Next page lists all of them.
:maxdepth: 1
classes
serialization
Functions
An ONNX model can be created directly from the classes described in the previous section, but it is faster to create and verify a model with the following helpers.
:maxdepth: 1
backend
checker
compose
defs
external_data_helper
helper
inliner
mapping
model_container
numpy_helper
parser
printer
reference
shape_inference
tools
utils
version_converter