d13cabf7f9
### Description This PR upgrades CUDA 11 build pipelines' GCC version from 8 to 11. ### Motivation and Context GCC8 has an experimental std::filesystem implementation which is not ABI compatible with the formal one in later GCC releases. It didn't cause trouble for us, however, ONNX community has encountered this issue much. For example, https://github.com/onnx/onnx/issues/6047 . So this PR increases the minimum supported GCC version from 8 to 9, and removes the references to GCC's "stdc++fs" library. Please note we compile our code on RHEL8 and RHEL8's libstdc++ doesn't have the fs library, which means the binaries in ONNX Runtime's official packages always static link to the fs library. It is just a matter of which version of the library, an experimental one or a more mature one. And it is an implementation detail that is not visible from outside. Anyway, a newer GCC is better. It will give us the chance to use many C++20 features. #### Why we were using GCC 8? It is because all our Linux packages were built on RHEL8 or its equivalents. The default GCC version in RHEL8 is 8. RHEL also provides additional GCC versions from RH devtoolset. UBI8 is the abbreviation of Red Hat Universal Base Image 8, which is the containerized RHEL8. UBI8 is free, which means it doesn't require a subscription(while RHEL does). The only devtoolset that UBI8 provides is GCC 12, which is too new for being used with CUDA 11.8. And our CUDA 11.8's build env is a docker image from Nvidia that is based on UBI8. #### How the problem is solved Almalinux is an alternative to RHEL. Almalinux 8 provides GCC 11. And the CUDA 11.8 docker image from Nvidia is open source, which means we can rebuild the image based on Almalinux 8 to get GCC 11. I've done this, but I cannot republish the new image due to various complicated license restrictions. Therefore I put them at an internal location in onnxruntimebuildcache.azurecr.io. |
||
|---|---|---|
| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.bat | ||
| build.sh | ||
| build_arm64x.bat | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt.in | ||
| SECURITY.md | ||
| setup.py | ||
| ThirdPartyNotices.txt | ||
| VERSION_NUMBER | ||
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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.