c8524d2dab
### Description 1. Move it to a separated pool that use the same image as [the public hosted pool](https://learn.microsoft.com/en-us/azure/devops/pipelines/agents/hosted?view=azure-devops&tabs=yaml). Also, create a beta pool which contains the next version image of the hosted pool, and add jobs in our post merge pipeline to test if the next version image will break our CI. So, usually we will have at least one week to prepare. 2. Change the cmake generator in use in our pipelines from "Ninja" to "MingW Makefile", because the latest version of cmake doesn't work with the latest version of Ninja. People who prefer Ninja could still use ninja in their local build by passing "--cmake_generator ninja" to [build.py](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/build.py). 3. Delete eager mode CI pipeline. ### Motivation and Context I need to update the software we have in our CI build machines, and I need to resolve this incompatibility issue. In more detail, the build error I hit was: em++: error: CMakeFilesonnxruntime_mlas_test.dirC_a_work1sonnxruntimetestmlasunittesttest_activation.cpp.o: No such file or directory ("CMakeFilesonnxruntime_mlas_test.dirC_a_work1sonnxruntimetestmlasunittesttest_activation.cpp.o" was expected to be an input file, based on the commandline arguments provided) After this PR we will deprecate python 3.7 support. The eager mode CI pipeline is the last one that still use python 3.7. Then we can rework the PR #10953 made by [fs-eire](https://github.com/fs-eire) last year. Fixed [AB#14435](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/14435) |
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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 & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.