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Commitc7a5b6e

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‎_doc/api/docs.rst‎

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validation.docs
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===============
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make_euclidean
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++++++++++++++
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..autofunction::onnx_array_api.validation.docs.make_euclidean

‎_doc/api/index.rst‎

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tools
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profiling
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f8
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docs

‎_doc/api/light_api.rst‎

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..autoclass::onnx_array_api.light_api.Var
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:members:
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:inherited-members:
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Vars
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====
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..autoclass::onnx_array_api.light_api.Vars
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:members:
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:inherited-members:

‎_doc/conf.py‎

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"https://data-apis.org/array-api/",
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("2022.12/API_specification/generated/array_api.{0}.html",1),
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),
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"ast":"https://docs.python.org/3/library/ast.html",
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"cProfile.Profile":"https://docs.python.org/3/library/profile.html#profile.Profile",
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"DOT":"https://graphviz.org/doc/info/lang.html",
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"inner API":"https://onnx.ai/onnx/intro/python.html",
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"JIT":"https://en.wikipedia.org/wiki/Just-in-time_compilation",
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"onnx":"https://onnx.ai/onnx/",
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"onnx.helper":"https://onnx.ai/onnx/api/helper.html",
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"ONNX":"https://onnx.ai/",
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"ONNX Operators":"https://onnx.ai/onnx/operators/",
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"onnxruntime":"https://onnxruntime.ai/",
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"onnxruntime-training":"https://onnxruntime.ai/docs/get-started/training-on-device.html",
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"numpy":"https://numpy.org/",
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"numba":"https://numba.pydata.org/",
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"onnx-array-api": ("https://sdpython.github.io/doc/onnx-array-api/dev/"),
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"onnxscript":"https://github.com/microsoft/onnxscript",
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"pyinstrument":"https://github.com/joerick/pyinstrument",
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"python":"https://www.python.org/",
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"pytorch":"https://pytorch.org/",
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"reverse Polish notation":"https://en.wikipedia.org/wiki/Reverse_Polish_notation",
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"scikit-learn":"https://scikit-learn.org/stable/",
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"scipy":"https://scipy.org/",
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"sklearn-onnx":"https://onnx.ai/sklearn-onnx/",
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"spox":"https://github.com/Quantco/spox",
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"sphinx-gallery":"https://github.com/sphinx-gallery/sphinx-gallery",
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"tensorflow":"https://www.tensorflow.org/",
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"tensorflow-onnx":"https://github.com/onnx/tensorflow-onnx",
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"torch":"https://pytorch.org/docs/stable/torch.html",
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"torch.onnx":"https://pytorch.org/docs/stable/onnx.html",
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#
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"C_OrtValue": (
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"http://www.xavierdupre.fr/app/onnxcustom/helpsphinx/"

‎_doc/tutorial/index.rst‎

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..toctree::
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:maxdepth:1
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overview
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onnx_api
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light_api
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numpy_api
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benchmarks

‎_doc/tutorial/light_api.rst‎

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.. _l-light-api:
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==========================================
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Light API for ONNX: everything in one line
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==========================================
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It is inspired from the:epkg:`reverse Polish notation`.
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Following example implements the euclidean distance.
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This API tries to keep it simple and intuitive to short functions.
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..runpython::
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:showcode:
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import numpy as np
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from onnx_array_api.light_api import start
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from onnx_array_api.plotting.text_plot import onnx_simple_text_plot
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model = (
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start()
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.vin("X")
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.vin("Y")
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.bring("X", "Y")
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.Sub()
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.rename("dxy")
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.cst(np.array([2], dtype=np.int64), "two")
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.bring("dxy", "two")
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.Pow()
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.ReduceSum()
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.rename("Z")
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.vout()
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.to_onnx()
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)
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print(onnx_simple_text_plot(model))
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There are two kinds of methods, the graph methods, playing with the graph structure,
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and the methods for operators starting with an upper letter.
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Graph methods
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=============
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Any graph must start with function:func:`start <onnx_array_api.light_api.start>`.
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It is usually following by `vin` to add an input.
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* bring (:meth:`cst <onnx_array_api.light_api.Var.bring>`,:meth:`cst <onnx_array_api.light_api.Vars.bring>`):
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assembles multiple results into a set before calling an operator taking mulitple inputs,
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* cst (:meth:`cst <onnx_array_api.light_api.Var.cst>`,:meth:`cst <onnx_array_api.light_api.Vars.cst>`):
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adds a constant tensor to the graph,
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* rename (:meth:`cst <onnx_array_api.light_api.Var.rename>`,:meth:`cst <onnx_array_api.light_api.Vars.rename>`):
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renames or give a name to a variable in order to call it later.
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* vin (:meth:`cst <onnx_array_api.light_api.Var.vin>`,:meth:`cst <onnx_array_api.light_api.Vars.vin>`):
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adds an input to the graph,
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* vout (:meth:`cst <onnx_array_api.light_api.Var.vout>`,:meth:`cst <onnx_array_api.light_api.Vars.vout>`):
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declares an existing result as an output.
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These methods are implemented in class:class:`onnx_array_api.light_api.var.BaseVar`
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Operator methods
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================
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They are described in:epkg:`ONNX Operators` and redefined in a stable API
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so that the definition should not change depending on this opset.
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:class:`onnx_array_api.light_api.Var` defines all operators taking only one input.
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:class:`onnx_array_api.light_api.Vars` defines all other operators.
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Numpy methods
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=============
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Numpy users expect methods such as `reshape`, property `shape` or
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operator `+` to be available as well and that the case. They are
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defined in class:class:`Var <onnx_array_api.light_api.Var>` or
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:class:`Vars <onnx_array_api.light_api.Vars>` depending on the number of
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inputs they require. Their name starts with a lower letter.

‎_doc/tutorial/overview.rst‎renamed to ‎_doc/tutorial/numpy_api.rst‎

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.. _l-numpy-api-onnx:
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==================
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Numpy API for ONNX
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==================

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