54871a2773
### Description 1. Update Linux GPU machine from T4 to A10, sm=8.6 2. update the tolerance ### Motivation and Context 1. Free more T4 and test with higher compute capability. 2. ORT enables TF32 in GEMM for A10/100. TF32 will cause precsion loss and fail this test ``` 2024-01-19T13:27:18.8302842Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12 2024-01-19T13:27:25.8438153Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:347: Failure 2024-01-19T13:27:25.8438641Z Expected equality of these values: 2024-01-19T13:27:25.8438841Z COMPARE_RESULT::SUCCESS 2024-01-19T13:27:25.8439276Z Which is: 4-byte object <00-00 00-00> 2024-01-19T13:27:25.8439464Z ret.first 2024-01-19T13:27:25.8445514Z Which is: 4-byte object <01-00 00-00> 2024-01-19T13:27:25.8445962Z expected 0.145984 (3e157cc1), got 0.975133 (3f79a24b), diff: 0.829149, tol=0.0114598 idx=375. 20 of 388 differ 2024-01-19T13:27:25.8446198Z 2024-01-19T13:27:25.8555736Z [ FAILED ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12, where GetParam() = "cuda_../models/zoo/opset12/SSD/ssd-12.onnx" (7025 ms) 2024-01-19T13:27:25.8556077Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_YOLOv312_yolov312 2024-01-19T13:27:29.3174318Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:347: Failure 2024-01-19T13:27:29.3175144Z Expected equality of these values: 2024-01-19T13:27:29.3175389Z COMPARE_RESULT::SUCCESS 2024-01-19T13:27:29.3175812Z Which is: 4-byte object <00-00 00-00> 2024-01-19T13:27:29.3176080Z ret.first 2024-01-19T13:27:29.3176322Z Which is: 4-byte object <01-00 00-00> 2024-01-19T13:27:29.3178431Z expected 4.34958 (408b2fb8), got 4.51324 (40906c80), diff: 0.16367, tol=0.0534958 idx=9929. 22 of 42588 differ ``` 3. some other test like SSD throw other exception, so skip them ''' 2024-01-22T09:07:40.8446910Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12 2024-01-22T09:07:51.5587571Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:358: Failure 2024-01-22T09:07:51.5588512Z Expected equality of these values: 2024-01-22T09:07:51.5588870Z COMPARE_RESULT::SUCCESS 2024-01-22T09:07:51.5589467Z Which is: 4-byte object <00-00 00-00> 2024-01-22T09:07:51.5589953Z ret.first 2024-01-22T09:07:51.5590462Z Which is: 4-byte object <01-00 00-00> 2024-01-22T09:07:51.5590841Z expected 1, got 63 ''' |
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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 |
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.