0d4f0f8ad2
* support optimizer opt for deepspeed 0.5.9 * resolve comments * resolve comments * FP16_Optimizer Support for more Deepspeed Versions (#12046) * fp16_optimizer for more ds versions * change ds version * bugfix * fix bug * Fix unused function warning for decodeMIDR(). (#12069) Changed from static function defined in header to function declared in header and defined in separate .cc file. * pin protobuf version to be compatible with onnx (#12132) Co-authored-by: Ashwini Khade <askhade@microsoft.com@orttrainingdev10.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> * RoiAlign CPU EP add warning for max mode with samples != 1 (#12136) * RoiAlign add warning about incorrect max summation when sample size not 1 * include coreml_provider_factory.h in macos build instead of coreml_ex… (#12138) include coreml_provider_factory.h in macos build instead of coreml_execution_provider.h * List 3.10 as supported python version and remove 3.6 (#12141) list 3.10 as supported python version and remove 3.6 Co-authored-by: Randy Shuai <rashuai@microsoft.com> * Use updated symbolic_helper.check_training_mode (#11900) Co-authored-by: Jingyan Wang, Baiju Meswani * Fix GH issue 12151 by using inverse perms for updating DQ axis attribute (#12158) * Fix GH issue 12151. Need to use inverse perms for updating that axis to what is used for transposing the input. This only applies if the DQ node is doing per-axis dequantization. * fixing positions for beam search gpt2 (#12156) * fixing positions for beam search gpt2 Co-authored-by: Tianlei Wu <tlwu@microsoft.com> * remove wrong placed libs (#12201) * Add file mapping for windows platform. (#12183) * Add file mapping for windows platform. * Add unit test for file mapping for windows. Also add an error message for mis-aligned offset * Add unit test for file mapping for windows. Also add an error message for mis-aligned offset * Update data type to avoid warnings * Compitable data type to avoid warnings. Update CreatFileMapping2 condition for winml compiling. * Add type conversion to avoid warnings for X86 release build. Co-authored-by: Ting Cao <ticao@microsoft.com> * Fix bug where onnxruntime_USE_NCCL flag would default to ON (#12195) Fix bug where onnxruntime_USE_NCCL flag would default to ON, causing ORT to not build properly. New functionality: flag is ON when training is enabled and NCCL is not disabled. Flag is OFF otherwise Co-authored-by: zhijxu <zhijxu@microsoft.com> Co-authored-by: zhijxu <zhijxu> Co-authored-by: Vincent Wang <wangwchpku@outlook.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Ashwini Khade <askhade@microsoft.com> Co-authored-by: Ashwini Khade <askhade@microsoft.com@orttrainingdev10.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com> Co-authored-by: Carson Swope <carsonswope@users.noreply.github.com> Co-authored-by: Randy Shuai <rashuai@microsoft.com> Co-authored-by: jingyanwangms <47403504+jingyanwangms@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Viswanath Boga <44417868+viboga@users.noreply.github.com> Co-authored-by: leqiao-1 <61653207+leqiao-1@users.noreply.github.com> Co-authored-by: caoting-dotcom <71617901+caoting-dotcom@users.noreply.github.com> Co-authored-by: Ting Cao <ticao@microsoft.com> Co-authored-by: Sean Murray <59740888+seanmurr1@users.noreply.github.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
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 |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| 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.