ONNX Runtime
A single standard to accelerate models on any hardware.
Overview
ONNX Runtime is a cross-platform inferencing and training accelerator for machine learning models. It is designed to maximize performance and support models from various frameworks (like PyTorch, TensorFlow) that have been converted to the standard ONNX format. It provides a single API to run models on diverse hardware and operating systems, leveraging hardware-specific accelerators (Execution Providers) for optimal performance.
✨ Key Features
- High-performance inference for ONNX models
- Cross-platform (Windows, Linux, Mac, Android, iOS)
- Extensible with hardware-specific accelerators (Execution Providers)
- Supports models from all major frameworks
- Can be used for both inference and training
🎯 Key Differentiators
- Based on the open ONNX standard, promoting interoperability
- Single runtime for a vast array of hardware targets
- Highly performant and broadly adopted
Unique Value: Decouples the model from the runtime, allowing developers to train in any framework and deploy with a single, high-performance engine on any hardware.
🎯 Use Cases (4)
✅ Best For
- Used within Microsoft products to accelerate AI workloads
- Deploying models from PyTorch to run on Windows ML
- Serving a variety of models from a single, standardized inference engine
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Training models from scratch (it is primarily an inference engine)
- Users who do not want to convert their models to ONNX format
🏆 Alternatives
Provides broader interoperability than framework-specific runtimes like TensorFlow Lite and can integrate with vendor-specific backends like TensorRT and OpenVINO for optimal performance.
💻 Platforms
✅ Offline Mode Available
🔌 Integrations
💰 Pricing
Free tier: ONNX Runtime is a free, open-source project.
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