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* Revert the cuda algo finding change as this causes a significant memory bloat. (#8181) * Revert the cuda algo finding change as this causes a significant memory bloat. * Address PR comment * Make pipelines to support torch1.8.1 and torch1.9.0 (#8084) * Add post-install command to build PyTorch CPP extensions from within onnxruntime package (#8027) ORTModule requires two PyTorch CPP extensions that are currently JIT compiled. The runtime compilation can cause issues in some environments without all build requirements or in environments with multiple instances of ORTModule running in parallel This PR creates a custom command to compile such extensions that must be manually executed before ORTModule is executed for the first time. When users try to use ORTModule before the extensions are compiled, an error with instructions are raised PyTorch CPP Extensions for ORTModule can be compiled by running: python -m onnxruntime.training.ortmodule.torch_cpp_extensions.install Full build environment is needed for this * Patch orttraining-ortmodule pipeline with latest fix on master * add cuda version to build config * lib path * . * . * . * . * . * . * . * . * . * . * . * Remove auto doc gen Co-authored-by: Pranav Sharma <prs@microsoft.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Baiju Meswani <bmeswani@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
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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.