description ONNX Runtime Overview
ONNX Runtime executes machine learning models and provides a deployment layer that can be used separately from the framework in which a model was trained. It supports ONNX, the Open Neural Network Exchange format, which represents models in a form intended for exchange between compatible tools. That separation can help teams use a common inference engine for models produced through different training workflows. The runtime applies graph optimizations and connects to hardware through execution providers, allowing supported computation to run on CPUs, GPUs or specialized accelerators.
Its cross-platform design suits applications that must serve models in different operating environments. Performance and compatibility depend on the model's operations and the chosen provider, so export validation matters. Within the category, its focus is executing and optimizing models rather than providing a general neural-network authoring interface.
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.
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