CodeLinaro/onnxruntime
Feb 2026 – Feb 2026
1 Month active
Languages Used
C++Python
Technical Skills
C++C++ programmingPythonalgorithm designbackend developmentdependency management
Chaya
PROFILE
Chaya
During February 2026, Cha worked on the CodeLinaro/onnxruntime repository, focusing on enhancing reliability and deployment readiness for ONNX operator implementations. Using C++ and Python, Cha addressed a division-by-zero crash in GatherND by adding input validation and comprehensive unit tests, ensuring correct behavior when batch dimensions mismatch. For AveragePool, Cha corrected divisor calculations for asymmetric padding in 2D and 3D cases, aligning results with ONNX specifications and adding regression tests to prevent future issues. Additionally, Cha improved quantization workflows by introducing an optional ml_dtypes dependency through package management, enabling clean environment support without impacting existing runtime functionality.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
135
Activity Months1
Your Network
65 peopleWork History
February 2026
3 Commits • 1 Features
Feb 1, 2026February 2026: Strengthened reliability and deployment readiness for CodeLinaro/onnxruntime. Key deliverables include robustness fixes for GatherND and AveragePool with unit tests to prevent crashes and ensure ONNX-spec aligned behavior, plus packaging improvements adding an optional ml_dtypes dependency to enable onnxruntime.quantization in clean environments. These changes reduce crash risk, improve result correctness under edge cases, and lower deployment friction for quantization workflows.
3 Commits • 1 Features
Feb 1, 2026February 2026: Strengthened reliability and deployment readiness for CodeLinaro/onnxruntime. Key deliverables include robustness fixes for GatherND and AveragePool with unit tests to prevent crashes and ensure ONNX-spec aligned behavior, plus packaging improvements adding an optional ml_dtypes dependency to enable onnxruntime.quantization in clean environments. These changes reduce crash risk, improve result correctness under edge cases, and lower deployment friction for quantization workflows.
February 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++C++ programmingPythonalgorithm designbackend developmentdependency managementpackage managementunit testing
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