zoom_in Click to enlarge

ONNX Runtime

▲ ▼

language

description ONNX Runtime Overview

ONNX Runtime is a high-performance inference engine designed to accelerate deep learning model deployment across various platforms. It supports the ONNX (Open Neural Network Exchange) format, enabling interoperability between different frameworks. ONNX Runtime's optimizations and hardware acceleration capabilities ensure efficient inference on CPUs, GPUs, and other specialized hardware. Its cross-platform compatibility makes it suitable for a wide range of deployment scenarios.

help ONNX Runtime FAQ

What problem does ONNX Runtime solve for machine learning deployment?

ONNX Runtime runs models saved in the ONNX format, which helps move trained models between frameworks such as PyTorch, TensorFlow, and scikit-learn. It is used mainly for inference, where latency and portability matter.

Can ONNX Runtime use GPUs?

Yes, ONNX Runtime supports hardware acceleration through execution providers such as CUDA, TensorRT, DirectML, OpenVINO, and CoreML. The available provider depends on the operating system, installed drivers, and target hardware.

How is ONNX Runtime different from exporting a model to TensorFlow Lite?

TensorFlow Lite is focused heavily on mobile and embedded TensorFlow deployment, while ONNX Runtime is a broader inference engine for ONNX models across servers, desktops, mobile, and edge devices. Teams often choose based on their training framework and target hardware.

Does ONNX Runtime only work with deep learning models?

No, ONNX can represent many model types, including some traditional machine learning pipelines. ONNX Runtime is often associated with deep learning, but it can also run supported classical ML operators when exported correctly.

swap_horiz

Looking for ONNX Runtime alternatives? Compare top competitors ranked & scored

arrow_forward