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* Sticky thread alloaction * Test sticky thread assignment * Test sticky thread assignment * Test sticky thread assignment * Expose control over additional worker assignment stats * Sticky thread alloaction * Test sticky thread assignment * Test sticky thread assignment * Test sticky thread assignment * Expose control over additional worker assignment stats * Merge * Merge * Merge * Fix Windows build * Fix windows build 2 * Build Python 3.8 Windows CPU only * Add env var to override binding * Build Python 3.8 Windows CPU only * Fix windows build * Remove thread affinity override * Remove goodworker * Remove Python build settings * Remove unneeded changes * Remove unneeded changes * Remove unneeded changes * Remove unneeded changes * Remove unneeded changes * Remove unneeded changes * Tidy * Tidy * Avoid race on preferred_worker vector * Improve assertions * Improve assertions * Enum for PushBackWithTag result * Remove unused field * Update comments * Extra debugging * Extra debugging * Extra debugging * Support varying thread pool sizes * Improve comments * Remove requirement for thread local to be trivially destructible * Use unsigned consistently for thread counts, removing casting * Remove debug code * Fix webassembly build * Merge * Merge * Merge * Remove unused code * Fix build * Extra test case for varying loop sizes * Clean variable names * Clean variable names * Clean variable names * Remove unneeded include, fix build * Fix profiling * Update from review comments |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator compatible with deep learning frameworks, PyTorch and TensorFlow/Keras, as well as classical machine learning libraries such as scikit-learn, and more.
ONNX Runtime uses the portable ONNX computation graph format, backed by execution providers optimized for operating systems, drivers and hardware.
Common use cases for ONNX Runtime:
- Improve inference performance for a wide variety of ML models
- Reduce time and cost of training large models
- Train in Python but deploy into a C#/C++/Java app
- Run with optimized performance on different hardware and operating systems
- Support models created in several different frameworks
ONNX Runtime inference APIs are stable and production-ready since the 1.0 release in October 2019 and can enable faster customer experiences and lower costs.
ONNX Runtime training feature was introduced in May 2020 in preview. This feature supports acceleration of PyTorch training on multi-node NVIDIA GPUs for transformer models. Additional updates for this feature are coming soon.
Get Started
- Install
- Inference
- Training
- Documentation
- Samples and Tutorials
- Build Instructions
- Frequently Asked Questions
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
This project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use Github Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.