ONNX provides a definition of an extensible computation graph model, as well as definitions of built-in operators and standard data types. Each computation dataflow graph is structured as a list of nodes that form an acyclic graph. Nodes have one or more inputs and one or more outputs. Each node is a call to an operator.

ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.

Toggle Light / Dark / Auto color theme. Toggle table of contents sidebar. ONNX 1.19.0 documentation

Install the associated library, convert to ONNX format, and save your results. Reference tutorials

ONNX with Python¶ Next sections highlight the main functions used to build an ONNX graph with the Python API onnx offers. A simple example: a linear regression¶ The linear regression is the most simple model in machine learning described by the following expression \(Y = XA + B\).

Attributes¶. is_causal - INT (default is '0'):. If set to 1, the attention masking is a lower triangular matrix when the mask is a square matrix.The attention masking has the form of the upper left causal bias due to the alignment. kv_num_heads - INT:. Number of heads of key and value.

ONNX Concepts¶ ONNX can be compared to a programming language specialized in mathematical functions. It defines all the necessary operations a machine learning model needs to implement its inference function with this language. A linear regression could be represented in the following way:

This is the R Interface to Open Neural Network Exchange (ONNX). ONNX is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML.

ndonnx enables execution with an ONNX backend and instant ONNX export for Array API compliant code. This diminishes the need for dedicated converter library code since the same code used to implement most of a library can reused in ONNX conversion.

Lists out all the ONNX operators. For each operator, lists out the usage guide, parameters, examples, and line-by-line version history. This section also includes tables detailing each operator with its versions, as done in Operators.md .