lanl/Yoke
Apr 2025 – May 2025
2 Months active
Languages Used
CSVMarkdownPythonShell
Technical Skills
Code CleanupData ScienceDeep LearningDocumentationFile ManagementHPC
Smyther Shih-Yu Hsiao
PROFILE
Smyther Shih-yu Hsiao
Over two months, Smyther developed and maintained model training pipelines for the lanl/Yoke repository, focusing on the LSC action network. He centralized training resources, cleaned deprecated scripts, and improved documentation to enhance reproducibility and onboarding. Using Python, PyTorch, and SLURM, Smyther introduced parameter tuning and CNN architecture updates, including a step learning rate scheduler, to support more efficient experimentation. His work emphasized code quality through systematic code cleanup, removal of unused configuration parameters, and standardization of job submission processes. These efforts reduced operational risk, improved maintainability, and established a foundation for reliable, scalable machine learning workflows in high-performance environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
3
Lines of code
3,532
Activity Months2
Your Network
12 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025In May 2025, maintenance and code quality improvements were delivered for the lanl/Yoke repository, focusing on code cleanup and configuration hygiene to reduce operational risk and accelerate future feature work. No critical bugs were reported; the work prioritized readability, maintainability, and stable training configurations to support longer-term business value.
2 Commits • 1 Features
May 1, 2025In May 2025, maintenance and code quality improvements were delivered for the lanl/Yoke repository, focusing on code cleanup and configuration hygiene to reduce operational risk and accelerate future feature work. No critical bugs were reported; the work prioritized readability, maintainability, and stable training configurations to support longer-term business value.
May 2025
April 2025
7 Commits • 2 Features
Apr 1, 2025Month: 2025-04 — Delivered foundational training resources and model-tuning pipelines for the LSC action network, improving reproducibility, experimentation efficiency, and maintainability. Focused on centralizing resources, cleaning deprecated scripts, and documenting pipeline improvements to support long-term reliability and faster insight generation.
April 2025
7 Commits • 2 Features
Apr 1, 2025Month: 2025-04 — Delivered foundational training resources and model-tuning pipelines for the LSC action network, improving reproducibility, experimentation efficiency, and maintainability. Focused on centralizing resources, cleaning deprecated scripts, and documenting pipeline improvements to support long-term reliability and faster insight generation.
Activity
Loading activity data...
Quality Metrics
Correctness82.2%
Maintainability86.6%
Architecture82.2%
Performance77.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVMarkdownPythonShell
Technical Skills
Code CleanupCode RefactoringData ScienceDeep LearningDocumentationFile ManagementHPCHigh-Performance ComputingHyperparameter TuningMachine LearningModel TrainingPyTorchPythonPython DevelopmentSLURM
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline