// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
ONNX
ONNX (Open Neural Network Exchange) is an open format that allows AI models to be moved between different machine learning frameworks, making them more flexible.
TECHNICAL DEFINITION
ONNX (Open Neural Network Exchange) is an open-source format for representing machine learning models, providing an interoperable standard that enables developers to move models between different deep learning frameworks (e.g., PyTorch, TensorFlow) and hardware platforms for optimized inference.
SYNONYMS & ALIASES
- Open Neural Network Exchange
- Model interchange format
- Universal model format
USAGE NOTE
Engineers convert models to ONNX format to deploy them efficiently across various inference engines and hardware accelerators.
DEVELOPERS
Organizations developing technology related to ONNX.
As a co-creator of ONNX, Microsoft is a primary maintainer and developer of ONNX Runtime, an inference engine that runs ONNX models across various hardware and software platforms. They also integrate ONNX deeply into their Azure AI services for efficient model deployment.
Originating from Facebook AI Research, Meta is a co-creator and major contributor to the ONNX standard, particularly through its PyTorch framework, which offers robust capabilities for exporting models to ONNX format for deployment and optimization.
NVIDIA actively supports the ONNX ecosystem by developing optimized inference solutions. Their TensorRT SDK, designed for high-performance deep learning inference, can directly import and optimize ONNX models for deployment on NVIDIA GPUs.
Intel provides extensive support for ONNX models across its hardware portfolio. They develop and optimize ONNX Runtime for Intel CPUs and integrated GPUs, and their OpenVINO toolkit can consume ONNX models for high-performance inference on Intel devices.
Qualcomm focuses on enabling ONNX model deployment on its Snapdragon platforms for edge and mobile devices. They provide tools and runtimes that optimize ONNX models for efficient inference on their specialized AI hardware.
AWS integrates ONNX into its machine learning services, such as SageMaker, allowing users to deploy and run ONNX-formatted models efficiently in the cloud. They develop tools and services to streamline the workflow from ONNX model creation to scalable inference.
Hugging Face provides extensive tools and support for converting their vast library of transformer models to the ONNX format, enabling more efficient and portable inference across various deployment environments. They develop utilities for optimizing and using ONNX models with their ecosystem.
Baidu's deep learning framework, PaddlePaddle, supports the ONNX standard, allowing models trained in PaddlePaddle to be exported to ONNX for cross-framework compatibility and optimized deployment in various production environments.