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### Description This PR gets the onnxruntime Rust bindings to a foundation where they can be extended and validated as the onnxruntime progresses. Specifically, the PR does the following. - fixes some of the existing compilation issues due to missing some enums output tensor data types. - introduces a `just vendor` task that will vendor the source code from the onnxruntime to enable a common base directory within the crate directory rather than using a relative parent path. This enables `crate package` to be able to archive the onnxruntime native code, which will enable consumers of the onnxruntime-sys crate to be able to compile on their target. - introduces a GH action to lint the Rust code (rustfmt, clippy), build the library, validate through tests, and validate crate can package correctly. TODOs: - [x] This PR is based on #18200 and will need to be rebased once that PR is merged. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> This is the first step to getting new onnxruntime Rust crates published through this project, which will unblock community Rust projects which would like to take a dependency on onnxruntime Rust. Follow up work to enable publication of onnxruntime Rust crates: - change name of the crates to be published (onnxruntime-rs and onnxruntime-sys are already taken and we'll need new names) - update authors / license to reflect contributions from previous maintainer(s) and new maintainers - introduce a crate publish GH action or ADO pipeline --------- Signed-off-by: David Justice <david@devigned.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 & 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
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.