1c8a22ec68
### Description Fix logging for affinity failures on Linux. Make `GetCpuCores()` consistently return the number of physical cores. Use `CpuInfo` library to correctly set affinities for Linux where supported. Make windows generate affinity masks as ordinals and convert them to masks at the setting site. Allow setting multiple logical processors affinity masks per thread. We continue to set all logical processors as thread affinity per physical core. ### Motivation and Context Error logging on Linux uses `pthread_self()` which does not return Thread ID. Fix default affinity mask generation on Windows. The following are the issues with Windows: - `GetThreadAffinityMasks()` returns bitmasks, but on other platforms it returns ordinals generated for the hardware concurrency - The maximum number of processors supported for requires a mask of 64-bits, but `size_t` type used is not always 64-bit - The masks returned per physical core may have multiple bits set, because the mask applies to several logical cores hosted by the physical core. In the past, customers complained that their threads jump from one core to another which adversely affects performance. The decision was made to stay this way. - 64-bit masks do not allow for logical processors with IDs that are outside of 0-63 range. |
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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 |
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| Windows | |||
| Linux | |||
| Mac | |||
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| 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.