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Numpy API

Light API

It is inspired from the `Reverse Polish Notation

<https://en.wikipedia.org/wiki/Reverse_Polish_notation>`_.

GraphBuilder API

Almost every converting library (converting a machine learned model to ONNX) is implementing

its own graph builder and customizes it for its needs.

It handles some frequent tasks such as giving names to intermediate

results, loading, saving onnx models. It can be used as well to extend an existing graph.

.. code-block:: python

Sources available on

`github/onnx-array-api <https://github.com/sdpython/onnx-array-api>`_.

GraphBuilder API

Almost every converting library (converting a machine learned model to ONNX) is implementing

its own graph builder and customizes it for its needs.

It handles some frequent tasks such as giving names to intermediate

results, loading, saving onnx models. It can be used as well to extend an existing graph.

See :ref:`l-graph-api`.

.. runpython::

:showcode:

import numpy as np

from onnx_array_api.graph_api import GraphBuilder

from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

g = GraphBuilder()

g.make_tensor_input("X", np.float32, (None, None))

g.make_tensor_input("Y", np.float32, (None, None))

r1 = g.make_node("Sub", ["X", "Y"]) # the name given to the output is given by the class,

# it ensures the name is unique

init = g.make_initializer(np.array([2], dtype=np.int64)) # the class automatically

# converts the array to a tensor

r2 = g.make_node("Pow", [r1, init])

g.make_node("ReduceSum", [r2], outputs=["Z"]) # the output name is given because

# the user wants to choose the name

g.make_tensor_output("Z", np.float32, (None, None))

onx = g.to_onnx() # final conversion to onnx

print(onnx_simple_text_plot(onx))

Light API

The syntax is inspired from the

`Reverse Polish Notation <https://en.wikipedia.org/wiki/Reverse_Polish_notation>`_.

This kind of API is easy to use to build new graphs,

less easy to extend an existing graph. See :ref:`l-light-api`.

.. runpython::

:showcode:

import numpy as np

from onnx_array_api.light_api import start

from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

model = (

start()

.vin("X")

.vin("Y")

.bring("X", "Y")

.Sub()

.rename("dxy")

.cst(np.array([2], dtype=np.int64), "two")

.bring("dxy", "two")

.Pow()

.ReduceSum()

.rename("Z")

.vout()

.to_onnx()

)

print(onnx_simple_text_plot(model))

Writing ONNX graphs requires to know ONNX syntax unless

it is possible to reuse an existing syntax such as :epkg:`numpy`.

This is what this API is doing.

This kind of API is easy to use to build new graphs,

almost impossible to use to extend new graphs as it usually requires

to know onnx for that. See :ref:`l-numpy-api-onnx`.