ONNX Documentation

Description

The ONNX 1.23.0 documentation serves as a comprehensive resource for understanding and utilizing the Open Neural Network Exchange format. It begins with an introduction to ONNX and its fundamental concepts, guiding users through its integration with Python. A detailed API reference is provided, encompassing versioning strategies, data structures, functions, and a thorough listing of ONNX operators.

Technical details are elaborated upon, including the specifics of float storage in 8 bits, and the implementation of 4-bit and 2-bit integer types. The documentation also addresses the ONNX Repository, offering guidance on adding function bodies for operators and the process of introducing new operators or functions to the ONNX ecosystem. Security assurance cases are also a part of the documentation, highlighting ONNX's commitment to robust development.

Further technical sections delve into broadcasting mechanisms within ONNX, a guide on differentiability tags, dimension denotation, and handling external data. The Open Neural Network Exchange Intermediate Representation (ONNX IR) Specification is presented, alongside instructions for implementing an ONNX backend. Managing experimental operators and metadata is also covered, as are ONNX types and the ONNX Backend Test suite.

Information on official ONNX releases and operator conventions provides context for the framework's evolution. The Python API overview and methods for verifying PyPI releases with Sigstore attestations are included for developers. Finally, the documentation touches upon external data security, ONNX shape inference, the ONNX textual syntax, type denotation, the ONNX version converter, and ONNX versioning, making it a complete reference for ONNX users and contributors.

ONNX Documentation's Core Features

• Introduction to ONNX concepts

• ONNX with Python integration guide

• Comprehensive API Reference

• Detailed explanation of ONNX Versioning

• Documentation on ONNX Data Structures

• Coverage of ONNX Functions

• In-depth ONNX Operators documentation

• Technical details on data types and storage (e.g., 8-bit float, 4-bit integer)

• ONNX Repository guidelines

• Operator and Function addition procedures

• ONNX Security Assurance Case

• Broadcasting in ONNX

• Differentiability Tag guide

• Dimension Denotation and Type Denotation

• External Data handling

• ONNX IR Specification

• ONNX Backend implementation guidance

• Experimental Operators management

• ONNX Metadata

• ONNX Types

• ONNX Backend Test suite

• Official ONNX Releases

• Operator Conventions Overview

• Python API Overview

• Sigstore Attestations for PyPI releases

• ONNX Shape Inference

• ONNX Textual Syntax

• ONNX Version Converter

Getting Started with ONNX Documentation

1. 1

   Understand ONNX Concepts: Read the introductory sections to grasp the fundamental principles of ONNX.

2. 2

   Integrate with Python: Follow the ONNX with Python guide for practical implementation.

3. 3

   Explore API Reference: Utilize the API Reference for detailed information on functions, operators, and data structures.

4. 4

   Implement ONNX Backend: Consult the guides on implementing an ONNX backend for custom solutions.

5. 5

   Add New Operators/Functions: Refer to the documentation for procedures on adding new operators or functions.

6. 6

   Manage Experimental Features: Learn how to manage and utilize experimental operators.

7. 7

   Verify Releases: Understand the process of verifying ONNX PyPI releases using Sigstore attestations.

ONNX Documentation's Use Cases

• Model Interoperability
• Hardware Acceleration
• Model Optimization
• Research and Development
• Production Deployment

FAQ from ONNX Documentation

• What is ONNX and what does its documentation cover?

  ONNX (Open Neural Network Exchange) is a format for representing machine learning models. Its documentation covers core concepts, Python integration, API references, versioning, data structures, operators, technical details, security, and implementation guides.

• Where can I find information on ONNX operators?

  The ONNX 1.23.0 documentation includes a dedicated section for ONNX Operators, detailing their specifications and usage.

• How does ONNX handle different data types and storage?

  The technical details section of the ONNX documentation explains float storage in 8 bits and various integer types like 4-bit and 2-bit integer types.

• What is the purpose of the ONNX IR Specification?

  The ONNX IR (Intermediate Representation) Specification defines the structure and semantics of ONNX models, enabling interoperability between different frameworks.

• Can I implement my own ONNX backend?

  Yes, the documentation provides guidance on implementing an ONNX backend, allowing for custom hardware or software integrations.

• How is versioning handled in ONNX?

  ONNX has a dedicated section on Versioning, explaining how different versions of the format are managed and how to use the ONNX Version Converter.

• What are the security considerations for ONNX?

  The documentation includes information on ONNX Security Assurance Cases and external data security, highlighting measures to ensure model integrity and safety.

• How can I add new operators or functions to ONNX?

  The ONNX Repository documentation provides step-by-step instructions for adding a function body definition for an operator and for adding new operators or functions to ONNX.

• What is ONNX Shape Inference?

  ONNX Shape Inference is a process described in the documentation that determines the output shapes of operators based on their input shapes.

• Does the documentation cover how to use ONNX with Python?

  Yes, there is a specific section titled 'ONNX with Python' that guides users on integrating ONNX models into their Python workflows.