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.
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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.
- CategoryMLOps & Model Deployment
- Pricing ModelFree
- DeploymentCloud
- PlatformsWeb
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
Understand ONNX Concepts: Read the introductory sections to grasp the fundamental principles of ONNX.
Integrate with Python: Follow the ONNX with Python guide for practical implementation.
Explore API Reference: Utilize the API Reference for detailed information on functions, operators, and data structures.
Implement ONNX Backend: Consult the guides on implementing an ONNX backend for custom solutions.
Add New Operators/Functions: Refer to the documentation for procedures on adding new operators or functions.
Manage Experimental Features: Learn how to manage and utilize experimental operators.
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
