dsi-clinic/CMAP
Oct 2024 – Nov 2024
2 Months active
Languages Used
MarkdownPythonYAMLJupyter Notebook
Technical Skills
Configuration ManagementDeep LearningDocumentationExperiment TrackingHyperparameter TuningMachine Learning
Xiaoyue Wei
PROFILE
Xiaoyue Wei
Xiaoyue worked on the dsi-clinic/CMAP repository, focusing on experiment reproducibility, model evaluation, and developer workflow improvements. They integrated Weights & Biases for experiment tracking and implemented sweep-based hyperparameter tuning, standardizing usage across training scripts in Python. Xiaoyue created detailed Markdown documentation outlining model metrics and clarified training and debugging workflows to accelerate onboarding. They overhauled CI/CD pipelines, migrated environment management from Conda to Mamba, and adopted Ruff for code quality. Path handling and training utilities were refactored for reliability, and a bug in training image path formatting was resolved, resulting in more robust deployments and streamlined machine learning experimentation.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
11Total
Bugs
1
Commits
11
Features
4
Lines of code
1,836
Activity Months2
Your Network
27 peopleSame Organization
@fe01.ds.uchicago.edu4
Work History
November 2024
5 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for dsi-clinic/CMAP: Key features delivered include documentation improvements for the training and debugging workflow and a comprehensive CI/CD and environment management overhaul. No major bugs fixed this month as the focus was on reliability, onboarding, and maintainability enhancements. The work delivers business value by accelerating developer onboarding, improving debugging efficiency, and stabilizing deployments and environment setup.
5 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for dsi-clinic/CMAP: Key features delivered include documentation improvements for the training and debugging workflow and a comprehensive CI/CD and environment management overhaul. No major bugs fixed this month as the focus was on reliability, onboarding, and maintainability enhancements. The work delivers business value by accelerating developer onboarding, improving debugging efficiency, and stabilizing deployments and environment setup.
November 2024
October 2024
6 Commits • 2 Features
Oct 1, 2024Month: 2024-10 | CMAP repository (dsi-clinic/CMAP). This month focused on improving experiment reproducibility, robust data loading, and clear performance visibility for CMAP models. Key features delivered: WandB-based experiment tracking and sweep-based hyperparameter tuning with patch_size, and standardized WandB usage across training. Documentation: added performance_evaluation.md with model metrics layout (overall Jaccard, class accuracies, losses) and FP/FN placeholders; updated example image paths. Bug fixes: fixed training images path formatting to correctly incorporate epoch numbers in the directory structure. Achievements: improved traceability of experiments, streamlined hyperparameter exploration, and clearer performance reporting. Technologies/skills: Python ML workflow automation, Weights & Biases integration and sweep configuration, Markdown documentation, pre-commit formatting, and robust path handling.
October 2024
6 Commits • 2 Features
Oct 1, 2024Month: 2024-10 | CMAP repository (dsi-clinic/CMAP). This month focused on improving experiment reproducibility, robust data loading, and clear performance visibility for CMAP models. Key features delivered: WandB-based experiment tracking and sweep-based hyperparameter tuning with patch_size, and standardized WandB usage across training. Documentation: added performance_evaluation.md with model metrics layout (overall Jaccard, class accuracies, losses) and FP/FN placeholders; updated example image paths. Bug fixes: fixed training images path formatting to correctly incorporate epoch numbers in the directory structure. Achievements: improved traceability of experiments, streamlined hyperparameter exploration, and clearer performance reporting. Technologies/skills: Python ML workflow automation, Weights & Biases integration and sweep configuration, Markdown documentation, pre-commit formatting, and robust path handling.
Activity
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Quality Metrics
Correctness91.8%
Maintainability91.8%
Architecture90.0%
Performance82.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonYAML
Technical Skills
CI/CDCode QualityCode RefactoringConfiguration ManagementData EngineeringDeep LearningDocumentationEnvironment ManagementExperiment TrackingGitHyperparameter TuningMachine LearningModel EvaluationPathlibPython
Repositories Contributed To
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