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* Reduce the binary size growth from this change. Minimal build grew by 7KB from this checkin. Firstly simplify the checking logic a little. Same checks are still done - just without using an extra layer of helpers. The issue being addressed by the original change only applies if you have a graph output where the shape wasn't able to be inferred. e.g. Reshape node with dynamic input causes downstream shapes to be unknown. If that is not the case, MergeShapeInfo in graph.cc would have resolved any differences between a specified output shape and the inferred output shape during Graph::Resolve. The issue does not apply to the execution frame used by the optimizer as the only time it would create a graph output is if it could constant fold all the way through, so MergeShapeInfo would have handled any difference in that case as well. Due to these considerations, wiring a logger in at the IExecutionFrame level isn't necessary if VerifyOutputSizes optionally overridden by an implementation that cares. * Address PR 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.