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* Add check for a node with an invalid input so we fail during model load. Without this we get a more cryptic failure in the allocation planner. * Add handling for manually created subgraph where outer scope node arg names are in a different place. * Update onnxruntime/core/graph/graph.cc Co-authored-by: Pranav Sharma <prs@microsoft.com> * Ignore Fused nodes when checking inputs are valid. DML EP will remove initializers it has moved across, so they're available when the node runs but are no longer part of the ORT Graph instance. We have to fully resolve the graph before any node fusion happens, so the model was valid in the beginning (which is the main thing we are trying to validate). * Skip check in training build. Rules for allowing an 'invalid' input are unknown for those scenarios. * Only check the initial load for a training build. Co-authored-by: Pranav Sharma <prs@microsoft.com> |
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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.