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### Description There are 8 cu files under [flash attention](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/contrib_ops/cuda/bert/flash_attention) and 4 cu files under [cutlass fmha](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/contrib_ops/cuda/bert/cutlass_fmha) need a lot of memory to compile. Previously, the default value is same as parallel - number of CPU cores. Standard_NC4as_T4_v3 has 4 CPUs and 28 GB memory, and we launched 16 nvcc threads in total (4 parallel jobs, and 4 nvcc threads per job). Each thread might take 4 GB on average (peak is around 6GB, but threads are not started at same time). OOM happens since 16 threads might need close to 64 GB in worst case. When build machine has 64GB or larger memory, OOM is rare. Here we set a proper nvcc --threads based on available memory to avoid OOM. ### Motivation and Context Fix `Python Packaging Pipeline (Training Cuda 11.8)` |
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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 |
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| 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.