6dffd1a890
### Description 1. Update model_tests.cc: avoid auto adding new tests from new opsets. 2. Simplify the "ConcatPathComponent" function. It does not need to be a template. ### Motivation and Context All our Windows/Linux CI build machines are preloaded with some test data. In model_tests.cc, we auto add all of them to onnxruntime_test_all.exe's unit tests. However, it causes problems when we update the CI build machine images: new data could cause pipelines suddenly failing. Therefore, instead of auto discovering test data and adding all of them to tests, this PR changes it to explicitly specify the opset names. This change doesn't impact how Web CI pipeline runs its tests. Going forward, the workflow would be like: Step 1: update the onnx version in deps.txt Step 2: Update js/scripts/prepare-onnx-node-tests.ts. Like #16943 . Better to put step 1 and step 2 in the same PR. Step 3: onnxruntime-es team regenerates VM images, test them and deploy them. Step 4: Enable the new opset test data for EPs. [AB#18340](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/18340) |
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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 documention 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 |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.