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* first attempt rocm training wheel * modifications needed to python packaging pipeline for Rocm 4.1 * changges to not conflict with cuda missed stage1 changes remove package push add option r to getopt try again without python install try again without python install try again without python install split pipelines and add back push to remote storage try on cuda gpu pool try again try again try running without az subscription set try again on original pipeline change pool passing AMD Rocm whl on AMD-GPU pool split rocm pipeline from cuda pipeline remove comments * try adding Rocm tests as well * try with tests in place * fix trailing ws * add training data * try again as root for tests * use python3 * typo * try to map video, render group into container * try again * try again * try to avoid yum error code * make UID 1001 * try without yum downgrade * define rocm_version=None * remove CUDA related comments for Rocm Dockerfile * Dont pin nightly torch torchvision torchtext versions as they expire (for now nightly is required for Rocm 4.1) * missed requirements-rocm.txt from last commit * fix whitespace |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator compatible with deep learning frameworks, PyTorch and TensorFlow/Keras, as well as classical machine learning libraries such as scikit-learn, and more.
ONNX Runtime uses the portable ONNX computation graph format, backed by execution providers optimized for operating systems, drivers and hardware.
Common use cases for ONNX Runtime:
- Improve inference performance for a wide variety of ML models
- Reduce time and cost of training large models
- Train in Python but deploy into a C#/C++/Java app
- Run with optimized performance on different hardware and operating systems
- Support models created in several different frameworks
ONNX Runtime inference APIs are stable and production-ready since the 1.0 release in October 2019 and can enable faster customer experiences and lower costs.
ONNX Runtime training feature was introduced in May 2020 in preview. This feature supports acceleration of PyTorch training on multi-node NVIDIA GPUs for transformer models. Additional updates for this feature are coming soon.
Get Started
- Install
- Inference
- Training
- Documentation
- Samples and Tutorials
- Build Instructions
- Frequently Asked Questions
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
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.