ONNX - Introduction

ONNX (Open Neural Network Exchange) is an open-source format designed to represent machine learning models. Its main goal is to make it easier for developers to move models between different machine learning frameworks, ensuring compatibility and flexibility.

By providing a standardized format, ONNX allows developers to optimize their workflows, leverage various tools, and improve model interoperability.

The ONNX format supports a wide variety of operators, which are the fundamental building blocks of machine learning models. This broad support makes it easier to represent complex models and convert them between different frameworks, such as TensorFlow, PyTorch, and scikit-learn.

ONNX is widely adopted across the AI community and has become a key player in enhancing the efficiency and portability of machine learning models. At a high level, ONNX usage involves the following steps −

• Train your model using any popular framework, such as PyTorch, TensorFlow, or scikit-learn.
• Convert the model to the ONNX format, ensuring compatibility across different platforms.
• Load and run the model in ONNX Runtime for optimized inference.

This process ensures that machine learning models are portable, efficient, and ready for deployment across various environments. By using ONNX, developers can optimize workflows and ensure smooth integration of models across multiple frameworks.

What is Interoperability?

Interoperability in machine learning refers to the ability of different systems, tools, and frameworks to work together seamlessly. In the context of ONNX, interoperability means that a model trained in one machine learning framework can be used, modified, or deployed in another framework without the need for extensive adjustments.

History and Development of ONNX

ONNX was originally developed by the PyTorch team at Facebook under the name "Toffee". In September 2017, Facebook and Microsoft re-branded the project as ONNX and officially announced it.

The goal was to create an open standard for representing machine learning models that would foster greater collaboration and innovation. ONNX received broad support from major tech companies, including IBM, Huawei, Intel, AMD, Arm, and Qualcomm.

Key Features of ONNX

ONNX offers several key features and benefits that make it an attractive choice for AI developers −

• Standardization: ONNX provides a standardized format for machine learning models, making it easier to move models between different frameworks.
• Interoperability: With ONNX, models can be trained in one framework and then used in another, enhancing flexibility in model development and deployment. This interoperability is crucial for developers who want to experiment with different tools without being tied to a single ecosystem.
• Operators: ONNX supports a wide range of operators, allowing it to represent complex models accurately.
• ONNX Runtime: ONNX includes a high-performance runtime that can optimize and execute models across various hardware platforms, from powerful GPUs to small edge devices. This ensures that models run efficiently, regardless of the deployment environment.
• Community: ONNX is managed by a strong community of developers and major tech companies, ensuring continuous development and innovation. So that ONNX is regularly updated to include new features and improvements.