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Dump statistics of input and/or output tensors of each node. It could help to find out why a model outputs NaN. To use this tool, just add `--cmake_extra_defines onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=1` when build onnxruntime package. Then set some environment varaibles before running model with onnxruntime: ``` export ORT_DEBUG_NODE_IO_DUMP_INPUT_DATA=1 export ORT_DEBUG_NODE_IO_DUMP_OUTPUT_DATA=1 export ORT_DEBUG_NODE_IO_DUMP_STATISTICS_DATA=1 ``` Then statistics data will be appended after the dumping of input and output tensors. One possible cause of a FP16 or mixed precision model outputs NaN: some number exceeds the limit of FP16 (like max FP16 value is 65504). When a fp32 model has value > 65504 in a node output, it will become INF when converting the node to FP16. In this case, you need keep related nodes in FP32 to avoid the issue. You can dump tensor statistics of FP32 model to find out such candidate nodes. |
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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.