c716b56f26
Switched the code to C++17. To build ONNX Runtime on old distros like CentOS 7, you need to install a newer GCC from additionary repos. If you build onnxruntime with the newer GCC, typically the result binary can't be distributed to other places because it depends on the new GCC's runtime libraries, something that the stock OS doesn't have. But on RHEL/CentOS, it can be better. We use Red Hat devtoolset 8/9/10 with CentOS7 building our code. The new library features(like std::filesystem) that not exists in the old C++ runtime will be statically linked into the applications with some restrictions: 1. GCC has dual ABI, but we can only use the old one. It means std::string is still copy-on-write and std::list::size() is still O(n). Also, if you build onnxruntime on CentOS 7 and link it with some binaries that were built on CentOS 8 or Ubuntu with the new ABI and export C++ symbols directly(instead of using a C API), the it won't work. 2. We still can't use std::optional. It is a limitation coming from macOS. We will solve it when we got macOS 11 build machines. It won't be too long. 3. Please avoid to use C++17 in CUDA files(*.cu). Also, the *.h files that they include(like core/framework/float16.h). This is Because CUDA 10.2 doesn't support C++17. You are welcome to use the new features in any *.cc files. |
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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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| 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.