Introduction to ONNX Runtime
ONNX Runtime is a powerful machine-learning accelerator designed to enhance both inference and training processes. It is compatible with multiple platforms, making it a versatile choice for developers working across different environments. This decoupling from any specific technology stack allows ONNX Runtime to offer impressive performance boosts and cost reductions for machine-learning tasks.
Accelerating Machine Learning Inference
ONNX Runtime shines in speeding up customer experiences. It supports models from contemporary deep learning frameworks, such as PyTorch and TensorFlow/Keras, as well as traditional machine learning libraries like scikit-learn, LightGBM, and XGBoost. Its adaptability to various hardware, operating systems, and drivers ensures that users can always achieve optimal performance. ONNX Runtime achieves this by leveraging available hardware accelerators and implementing graph optimizations and transformations.
Enhanced Model Training
In addition to its inference capabilities, ONNX Runtime also accelerates model training. It significantly reduces the time needed to train transformer models on multi-node NVIDIA GPUs, making it an efficient solution for training. With just a simple one-line addition to existing PyTorch training scripts, developers can enjoy these enhancements without extensive modifications to their code base.
Getting Started and Resources
For those interested in diving deeper into ONNX Runtime, numerous resources are available:
- General Information: onnxruntime.ai
- Usage Documentation and Tutorials: Accessible at onnxruntime.ai/docs
- Video Tutorials: Available on the ONNX Runtime YouTube channel at youtube.com/@ONNXRuntime
- Release Roadmap: Details are provided at onnxruntime.ai/roadmap
- Sample Repositories: Explore inference and training examples at GitHub:
Builtin Pipeline and Third-party Status
ONNX Runtime features robust testing mechanisms across various systems, including Windows, Linux, Mac, Android, iOS, and Web environments. BrowserStack aids these efforts, ensuring thorough cross-platform compatibility. The project maintains a transparent status dashboard showcasing its build successes and offering insights into its reliability.
Contribution and Community Engagement
The ONNX Runtime project actively welcomes contributions and feedback from the community. Developers can participate in the project's growth through:
- Contributions: Guidelines are outlined in CONTRIBUTING.md
- Feature Requests and Bug Reports: File issues on GitHub
- Discussion Forums: Engage in discussions at GitHub Discussions
Licensing and Conduct
ONNX Runtime is distributed under the MIT License, supporting broader community usage and adaptation. The project adheres to the Microsoft Open Source Code of Conduct, ensuring a respectful and inclusive environment for all contributors.
Aspiring users and contributors are encouraged to explore the resources available and contribute to the project's ongoing development for a better machine-learning ecosystem.