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 documentation ¶ Introduction to ONNX ONNX Concepts ONNX with Python Converters API Reference Versioning Data Structures Functions ONNX Operators Technical Details Float stored in 8 …

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

An ONNX tensor is a dense full array with no stride. Element Type ¶ ONNX was initially developed to help deploying deep learning model. That’s why the specifications were initially designed for floats …

ONNX with Python ¶ Tip Check out the ir-py project for an alternative set of Python APIs for creating and manipulating ONNX models. The ir-py project provides a more modern and ergonomic interface …

ONNX is the first step in enabling more of these tools to work together by allowing them to share models. Our goal is to make it possible for developers to use the right combinations of tools for their …

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 …

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.

[docs] def load_tensor_from_string( s: bytes, format: _SupportedFormat = _DEFAULT_FORMAT, # noqa: A002 ) -> TensorProto: """Loads a binary string (bytes) that contains serialized TensorProto. …

Python API Overview ¶ The full API is described at API Reference. Loading an ONNX Model ¶