2dae8aaced
### Description Added some change in fuzzer project code to support linux also. How to test on linux: 1. Make sure you have installed clang/llvm. 2. run below command to build asan instrumented project: ``` CFLAGS="-g -fsanitize=address -shared-libasan -fprofile-instr-generate -fcoverage-mapping" CXXFLAGS="-g -shared-libasan -fsanitize=address -fprofile-instr-generate -fcoverage-mapping" CC=clang CXX=clang++ ./build.sh --update --build --config Debug --compile_no_warning_as_error --build_shared_lib --skip_submodule_sync --skip_tests --use_full_protobuf --parallel --fuzz_testing --build_dir build/ ``` 3. run fuzzer for some time, it will generate *.profraw file: ``` LLVM_PROFILE_FILE="%p.profraw" ./build/Debug/onnxruntime_security_fuzz /t /v onnxruntime/test/testdata/bart_tiny.onnx 1 m ``` 4. Get the cov by running below cmd: ``` llvm-profdata merge -sparse *.profraw -o default.profdata llvm-cov report ./build/Debug/onnxruntime_security_fuzz -instr-profile=default.profdata ``` <img width="1566" alt="Screenshot 2024-09-05 at 4 25 08 PM" src="https://github.com/user-attachments/assets/2aa0bb83-6634-4d33-b026-3535e97df431"> ### Motivation and Context 1. Currently fuzzer only supports windows and MSVC, we can't generate the code coverage using MSVC. With clang/llvm we can try and use clang instrumentation and llvm tools like llvm-cov. 2. In future we can add coverage guided fuzzer (libfuzzer) in same project. (Working on it) |
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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 documentation 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.