ONNX Runtime (ORT) is an open-source inference engine designed to efficiently execute machine learning models in the ONNX format. It acts as a bridge, allowing models trained in popular frameworks like PyTorch, TensorFlow, or Keras to be exported to ONNX and then run with optimized performance across a wide range of devices and operating systems. The core mechanism involves graph optimizations, such as node fusion and layout transformations, combined with leveraging hardware-specific accelerators through 'Execution Providers' (e.g., CUDA, TensorRT, OpenVINO, DirectML). This matters because it solves the critical problem of deploying ML models with high performance and portability, reducing latency and increasing throughput in production environments. It is widely adopted by ML engineers, data scientists, and companies like Microsoft for deploying models in cloud services, edge devices, and mobile applications, enabling faster and more cost-effective AI solutions.
ONNX Runtime is a powerful tool for running AI models faster and more efficiently across different devices and operating systems. It takes models from various training frameworks, optimizes them, and uses specialized hardware to speed up predictions, making AI deployment simpler and more cost-effective.
ONNX RT, ORT
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