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### ORTModule log clean up ORTModule log level - WARNING(Default) is for end users; INFO and VERBOSE is for internal ORT training developers. Few issues: 1. ONNX export will output lots of WARNING error message like "The shape inference of com.microsoft::SoftmaxCrossEntropyLossInternal/ATen/PythonOp type is missing", which is useless for us or end users.  3. ORT also print some information like ""CleanUnusedInitializersAndNodeArgs] Removing initializer","ReverseBFSWithStopGradient] Skip building gradient for", which is also useless for us or end users most of the time.  5. Different ranks output logs and making ORT developers or end users feels there are too many logs but usually not useful until we need investigate. Few improvements for the issues: 1. For ONNX export logs, there are two kinds of logs: a. export verbose log; b. other logs printed by torch C++ backend. So this PR make following change: # VERBOSE -> FULL export verbose log + FULL torch other logs from stdout and stderr (C++ backend) # INFO -> FULL export verbose log + FILTERED torch other logs from stdout and stderr (C++ backend) # WARNING/ERROR -> [Rank 0] NO export verbose log + FILTERED torch other logs from stdout and stderr (C++ backend) e.g. for verbose level, print all logs as usually; for info level, print verbose export log, and filtered logs from torch C++ backend (removing messages like this "The shape inference of com.microsoft::SoftmaxCrossEntropyLossInternal/ATen/PythonOp type is missing") . For higher level, only log the info on rank 0. 2. For ORT gradient graph build and session creation, also suppress the message and filtered out the message when log level >=INFO. 3. log level > INFO, then only logs on rank 0 is logged, to have a cleaner user experience This is the log for a BLOOM model training after the change: there are limited of warnings.  |
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| onnxruntime | ||
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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.