a54716e5a9
* Fix bug in Transpose CUDA kernel (#7329) * Fix permission error for ORTModule lock file (#7814) * fix topo sort in quant tool (#7833) * fix topo sort in quant tool * add unit test and make the topo sort stable * Relax tol for Conv1D fp16 test (#7844) * Relax tol for Conv1D fp16 test Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Resolve issue with wrapped ORTModule load_state_dict (#7847) * Encapsulate children modules inside a ModuleAccessor object to prevent erroneuos iteration over children while loading the state dictionary * Add named_models, models, apply methods, change ModuleAccessor to ModuleMetadata and modify unit tests * Change ModuleMetadata module getter logic, raise NotImplementedError for add_modules * Add comment explaining why overriding _load_from_state_dict method is needed * fixed bugs in packed mode and enable pack mode tests in ci (#7848) * fixed bugs in packed mode and enable pack mode tests in ci * removed unnecessary space * pr comments * pr comments * disable an average pool test * try disabling another avg pool * disable more avg pool tests * disable maxpool tests * add environment variable to control default training package's local version (#7849) * [js] update documents (#7852) * [js] update documents * escape double quotes * update operators.md * resolve comments * Support bool type for Pad CPU (#7856) * Initial commit * update * nit * Include ORT C/C++ API headers in the ORT Mobile AAR package (#7858) * Add header files of ort c/c++ api to aar package * Move header file selection to cmake based on EP choice * fix duplicated node name (#7865) * Clean up CPU kernel definition for opset 13 Pad (#7867) Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Sherlock <baihan.huang@gmail.com> Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: baijumeswani <bmeswani@microsoft.com> Co-authored-by: Tixxx <tix@microsoft.com> Co-authored-by: liqunfu <liqfu@microsoft.com> Co-authored-by: Yulong Wang <yulongw@microsoft.com> Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com> Co-authored-by: Tianlei Wu <tlwu@microsoft.com> |
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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
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
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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.