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### Description <!-- Describe your changes. --> ### Motivation and Context Some models from model zoo failed in the Linux CPU workflow. https://github.com/onnx/models/issues/562 Skip them temporarily. ###Verfication Linux CPU CI passed with beta image https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=789772&view=results **2022-10-21T13:31:17.6740348Z Skip symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/Inception-1-int8/inception-v1-12-int8.onnx** 2022-10-21T13:31:17.6740998Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/DenseNet-121-12-int8/densenet-12-int8.onnx 2022-10-21T13:31:17.6741618Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/MNIST-12/mnist-12.onnx **2022-10-21T13:31:17.6742207Z Skip symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/SSD-int8/ssd-12-int8.onnx** 2022-10-21T13:31:17.6742898Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/ResNet50_fp32/resnet50-v1-12.onnx 2022-10-21T13:31:17.6743544Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/MobileNet v2-1.0-fp32/mobilenetv2-12.onnx 2022-10-21T13:31:17.6744259Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/ResNet101_DUC_HDC-12/ResNet101-DUC-12.onnx 2022-10-21T13:31:17.6744891Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/YOLOv3-12-int8/yolov3-12-int8.onnx 2022-10-21T13:31:17.6745501Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/AlexNet/bvlcalexnet-12.onnx 2022-10-21T13:31:17.6746114Z Running symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/ZFNet-512-int8/zfnet512-12-int8.onnx **2022-10-21T13:31:17.6746768Z Skip symbolic shape inference on : /mnt/vss/_work/1/b/Release/../models/zoo/opset12/SSD-MobilenetV1-12-int8/ssd_mobilenet_v1_12-int8.onnx** |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.