ONNX - Ecosystem

The ONNX (Open Neural Network Exchange) ecosystem is a collection of tools, platforms, and services designed to facilitate the development, deployment, and optimization of machine learning models using ONNX as a standard format. ONNX provides an open format for representing machine learning models, enabling interoperability between different frameworks and tools.

In general terms, an ecosystem refers to a complex network or interconnected system of components that interact with each other within a particular environment. The ONNX ecosystem is designed to enhance interoperability, optimize performance, and simplify the deployment of machine learning models across various environments and applications.

Key Components of the ONNX Ecosystem

Following are the key components of the ONNX Ecosystem −

ONNX Runtime

ONNX Runtime, is a high-performance engine designed to efficiently run ONNX models. It is a tool that helps run machine learning models faster and more efficiently. ONNX Runtime supports models from popular frameworks like PyTorch, TensorFlow, and scikit-learn, making it easy to move models between different environments.

Model Conversion and Export Tools

ONNX provides various tools available to work with −

• ONNX Exporters: Tools that convert models from popular frameworks (like PyTorch, TensorFlow, and scikit-learn) into the ONNX format, allowing for model interoperability and deployment.
• ONNX Importers: Tools that enable the import of ONNX models into different frameworks or environments for further processing or deployment.

Integration Platforms

We can integrate ONNX with various platforms some of them are listed below −

• Azure Machine Learning: Provides services for training, deploying, and managing ONNX models in the cloud, integrating with various Azure services for enhanced scalability and performance.
• Azure Custom Vision: Allows users to export custom vision models to ONNX format, making them ready for deployment across different platforms.
• Azure SQL Edge: Supports machine learning predictions using ONNX models on edge devices, enabling inferring machine learning models in Azure SQL Edge.
• Azure Synapse Analytics: Integrates ONNX models within Synapse SQL.

Inference Servers

NVIDIA Triton Inference Server: A server that supports ONNX Runtime as a back end, enabling efficient and scalable model inference on NVIDIA GPUs. Triton provides high-performance inferencing and supports multiple model formats, including ONNX.

Automated Machine Learning

ML.NET: This is an open-source, cross-platform framework for building machine learning models in .NET ecosystem. ML.NET supports ONNX models for inference, allowing .NET developers to integrate advanced ML capabilities into their applications.

It is an Automated ML (AutoML) Open Neural Network Exchange (ONNX) model that makes predictions in a C# console application with ML.NET.