mozilla/onnxruntime
Nov 2024 – Apr 2025
4 Months active
Languages Used
C++CMakePowerShellPythonYAMLAssembly
Technical Skills
C++image processingmachine learningperformance optimizationC++ developmentunit testing
Yi-Hong Lyu
PROFILE
Yi-hong Lyu
Yilyu contributed to the mozilla/onnxruntime repository by developing and optimizing core features for image processing and machine learning workloads. Over four months, Yilyu implemented performance enhancements such as optimizing transpose operations around QLinearSoftmax and introducing a DepthToSpace uint8 kernel, which improved inference throughput for segmentation and super-resolution models. Yilyu also built a Windows OpenVINO CI pipeline, automating environment setup and validation to strengthen cross-platform reliability. Using C++, Python, and CMake, Yilyu delivered new operators and configuration options, including a cubic image resize operator and a saturation checker, demonstrating depth in performance optimization, CI/CD automation, and robust feature delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
5
Lines of code
795
Activity Months4
Your Network
4170 people
Work History
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for mozilla/onnxruntime focused on feature delivery and quality improvements. Key capabilities added this month include a new Resize operator in cubic mode without antialiasing to enable upsampling with defined scales, along with a CMake option to enable a saturation checker for the ConvSymKernelAvx2 path to improve overflow detection in VPMADDUBSW computations. No critical bugs fixed this month; stability and maintainability improvements were achieved through enhanced validation and testing coverage.
2 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for mozilla/onnxruntime focused on feature delivery and quality improvements. Key capabilities added this month include a new Resize operator in cubic mode without antialiasing to enable upsampling with defined scales, along with a CMake option to enable a saturation checker for the ConvSymKernelAvx2 path to improve overflow detection in VPMADDUBSW computations. No critical bugs fixed this month; stability and maintainability improvements were achieved through enhanced validation and testing coverage.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for mozilla/onnxruntime: Delivered Windows OpenVINO CI pipeline and related tooling to enhance Windows validation and OpenVINO compatibility. Implemented automated Windows test environment setup and integrated Windows OpenVINO support into the existing CI workflow by updating run_CIs_for_external_pr.py. These changes close Windows-specific validation gaps, accelerate feedback loops for Windows users, and improve overall cross-platform reliability for ONNX Runtime. No major bugs fixed this month; primary focus on building robust CI coverage and ensuring readiness for Windows OpenVINO workloads. Technologies demonstrated include CI/CD automation, Python scripting, Windows environment orchestration, OpenVINO, and ONNX Runtime integration.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for mozilla/onnxruntime: Delivered Windows OpenVINO CI pipeline and related tooling to enhance Windows validation and OpenVINO compatibility. Implemented automated Windows test environment setup and integrated Windows OpenVINO support into the existing CI workflow by updating run_CIs_for_external_pr.py. These changes close Windows-specific validation gaps, accelerate feedback loops for Windows users, and improve overall cross-platform reliability for ONNX Runtime. No major bugs fixed this month; primary focus on building robust CI coverage and ensuring readiness for Windows OpenVINO workloads. Technologies demonstrated include CI/CD automation, Python scripting, Windows environment orchestration, OpenVINO, and ONNX Runtime integration.
January 2025
1 Commits • 1 Features
Jan 1, 2025Month 2025-01: Delivered a high-impact performance optimization for Image Super-Resolution INT8 workloads in mozilla/onnxruntime. Implemented DepthToSpace uint8 kernel, improved FPS by ~25%, enabled graph-level optimization via DropQDQNodesRules, and added robust unit tests. These changes improve inference throughput for production models and streamline optimization workflows.
1 Commits • 1 Features
Jan 1, 2025Month 2025-01: Delivered a high-impact performance optimization for Image Super-Resolution INT8 workloads in mozilla/onnxruntime. Implemented DepthToSpace uint8 kernel, improved FPS by ~25%, enabled graph-level optimization via DropQDQNodesRules, and added robust unit tests. These changes improve inference throughput for production models and streamline optimization workflows.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 focused on delivering performance improvements for ONNX Runtime image segmentation workloads in the mozilla/onnxruntime repository. Implemented a targeted optimization of the transpose path around QLinearSoftmax to reduce bottlenecks in inference, enabling faster segmentation results and better utilization of hardware. The change was captured in a focused commit and validated against representative models to ensure no regressions. No separate bug fixes were recorded this month; the primary impact came from performance gains and improved readiness for production deployment in the next release cycle.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 focused on delivering performance improvements for ONNX Runtime image segmentation workloads in the mozilla/onnxruntime repository. Implemented a targeted optimization of the transpose path around QLinearSoftmax to reduce bottlenecks in inference, enabling faster segmentation results and better utilization of hardware. The change was captured in a focused commit and validated against representative models to ensure no regressions. No separate bug fixes were recorded this month; the primary impact came from performance gains and improved readiness for production deployment in the next release cycle.
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Quality Metrics
Correctness100.0%
Maintainability83.4%
Architecture93.4%
Performance90.0%
AI Usage23.4%
Skills & Technologies
Programming Languages
AssemblyC++CMakePowerShellPythonYAML
Technical Skills
Assembly language programmingC++C++ developmentCI/CDCMakeCMake configurationDevOpsPerformance optimizationPythonPython scriptingWindows Developmentimage processingmachine learningperformance optimizationunit testing
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