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ONNX Runtime (ORT) is a high-performance inference and training engine designed to accelerate machine learning models. It supports various frameworks such as PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and others. ORT is cross-platform, functioning across cloud, edge, web, and mobile environments. It integrates seamlessly with hardware-specific libraries, optimizing performance on CPUs, GPUs, NPUs, and other accelerators.

The core functionality of ORT includes fast inferencing and training capabilities, reducing costs and improving efficiency for large model training. It is used extensively in Microsoft’s products like Windows, Office, Azure Cognitive Services, and Bing, and is trusted by companies such as NVIDIA, Intel, Adobe, and Hugging Face. ORT supports a wide array of platforms, including Windows, Linux, macOS, iOS, Android, and web browsers, making it highly versatile for developers working in different environments and programming languages, such as Python, C#, JavaScript, and C++.

Key features of ONNX Runtime include:

1. Cross-Platform Support: ORT operates on various operating systems and supports multiple programming languages, ensuring broad usability.
2. Performance Optimization: It provides state-of-the-art model optimization techniques, including quantization and mixed precision models, to enhance performance and reduce resource consumption.
3. Generative AI Integration: ORT facilitates the integration of generative AI models, enabling applications for image synthesis, text generation, and more.
4. Extensive Documentation and Tutorials: The website offers detailed installation guides, API documentation, and numerous tutorials for deploying models across different environments, from IoT devices to mobile applications and web browsers.
5. Community and Open Source: ONNX Runtime is open-source, with an active community contributing to its development and improvement.

For more detailed information on how to use ONNX Runtime, including installation instructions and tutorials, you can visit their official website ONNX Runtime.

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