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 …

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 …

Export to ONNX Format The process to export your model to ONNX format depends on the framework or service used to train your model.

This documentation describes the ONNX concepts (Open Neural Network Exchange). It shows how it is used with examples in python and finally explains some of challenges faced when moving to ONNX …

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 …

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 …

The ONNX community provides tools to assist with creating and deploying your next deep learning model. Use the information below to select the tool that is right for your project.

ONNX Operators ¶ 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 …

If the external data is under another directory, use load_external_data_for_model() to specify the directory path and load after using onnx.load()

ONNX IR ¶ An in-memory IR that supports the full ONNX spec, designed for graph construction, analysis and transformation. Features ¶ Full ONNX spec support: all valid models representable by …