ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Ștefan Talpalaru 1b19045afa
[build] allow MPI on Unix when NCCL is disabled (#21175)
### Description

CMake logic fixed to allow enabling MPI while NCCL is disabled.

### Motivation and Context

MPI is also used on the CPU backend, not only with CUDA, so it makes
sense to decouple it properly from NCCL (which is for dealing with
multiple Nvidia GPUs).
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.github CoreML: Disable 1D ML Program matmul due to bug in coreml (#21186) 2024-06-29 12:19:51 -07:00
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cmake [build] allow MPI on Unix when NCCL is disabled (#21175) 2024-07-09 21:21:40 -07:00
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docs [CPU] SparseAttention op (#21110) 2024-07-03 21:51:57 -07:00
include/onnxruntime/core Remove core/common/gsl.h (#20894) 2024-07-08 18:09:39 -07:00
java Remove warning suppression from Java Packaging pipeline. (#21010) 2024-06-24 16:46:21 -07:00
js [js/webnn] Enable user-supplied MLContext (#20600) 2024-07-08 10:19:39 -07:00
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onnxruntime [ROCm] fix: obtain AMD GPU memory info through rocm_smi library (#21190) 2024-07-09 20:35:26 -07:00
orttraining Remove core/common/gsl.h (#20894) 2024-07-08 18:09:39 -07:00
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tools Update OpenVino CI Ubuntu to 22.04 (#21127) 2024-07-09 09:56:44 -07:00
winml Remove core/common/gsl.h (#20894) 2024-07-08 18:09:39 -07:00
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requirements.txt Add compatibility for NumPy 2.0 (#21085) 2024-06-27 13:50:53 -07:00
SECURITY.md
setup.py onnxruntime shared lib inside python package (#21223) 2024-07-02 15:37:50 -07:00
ThirdPartyNotices.txt
VERSION_NUMBER

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

Builtin Pipeline Status

System Inference Training
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Third-party Pipeline Status

System Inference Training
Linux Build Status

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.