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### Description When user dump the EP context model, if the nodes not partitioned to the EP, and they have external initializers, then the dumped model still point to the old external data file. It does not make sense that new generated model still point to old external data file. Example, model has node A, B, C, D all has external initializer in ext.bin. So ext.bin contains data for A, B, C, D. After dumping the EP context model, node A is on CPU, node B, C, D are on EP and dumped as EPContext node. If A's data is still in ext.bin, then new generated model has to depend on old ext.bin which contains all external data for the old model which is a big overhead. Fix: For new generated model, user should have option to specify the new external data file, so that the new generated model either pack all initializers into the Onnx model or has all initializers in the external data file. Add option ep.context_model_external_initializers_file_name to specify the new external data file and size threshold. All initializers will be inside the external data fie if the options is specified. Otherwise all initializers will be inside the EP context Onnx model. ### Motivation and Context Fix the issue https://github.com/microsoft/onnxruntime/issues/23358 |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
Data/Telemetry
Windows distributions of 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.