dsi-clinic/CMAP
Jan 2025 – Apr 2025
4 Months active
Languages Used
PythonJSONJupyter NotebookMarkdownYAML
Technical Skills
Argument ParsingCode FormattingCode ModularityData EngineeringLintingPython Scripting
Alan Cherman
PROFILE
Alan Cherman
Alex Cherman developed core training and data engineering workflows for the dsi-clinic/CMAP repository, focusing on modular, reproducible pipelines for deep learning experiments. Over four months, Alex refactored dataset construction and argument parsing, introduced class-balanced sampling, and enhanced per-class metric logging to improve model fairness and observability. He implemented a Slurm/Submitit-based launcher with advanced logging and IoU result parsing, and upgraded CI/CD workflows using GitHub Actions and pre-commit tooling. Working primarily in Python and YAML, Alex emphasized code modularity, maintainability, and robust configuration management, delivering features that accelerated experimentation and improved the reliability of machine learning model training.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
26Total
Bugs
1
Commits
26
Features
10
Lines of code
1,362
Activity Months4
Your Network
27 peopleSame Organization
@fe01.ds.uchicago.edu4
Work History
April 2025
8 Commits • 3 Features
Apr 1, 2025In April 2025, the CMAP workflow delivered a robust core training-run submission pipeline, along with essential documentation and CI/CD improvements. The key outcomes include a Slurm/Submitit-based launcher with enhanced logging, IoU result parsing, and configurable log/output handling; documentation updates clarifying launcher usage and Slurm-based training steps; and upgraded CI/CD workflows and pre-commit tooling for reliability and faster feedback. No major bugs were reported fixed this month; minor formatting and README polish were performed to improve maintainability. Technologies demonstrated include Python scripting, Slurm/Submitit integration, advanced logging and IO parsing, documentation, and modern CI/CD practices. Business value: accelerated experiment throughput, improved reproducibility, and higher developer efficiency.
8 Commits • 3 Features
Apr 1, 2025In April 2025, the CMAP workflow delivered a robust core training-run submission pipeline, along with essential documentation and CI/CD improvements. The key outcomes include a Slurm/Submitit-based launcher with enhanced logging, IoU result parsing, and configurable log/output handling; documentation updates clarifying launcher usage and Slurm-based training steps; and upgraded CI/CD workflows and pre-commit tooling for reliability and faster feedback. No major bugs were reported fixed this month; minor formatting and README polish were performed to improve maintainability. Technologies demonstrated include Python scripting, Slurm/Submitit integration, advanced logging and IO parsing, documentation, and modern CI/CD practices. Business value: accelerated experiment throughput, improved reproducibility, and higher developer efficiency.
April 2025
March 2025
4 Commits • 1 Features
Mar 1, 2025In March 2025, completed key enhancements for the dsi-clinic/CMAP repository focused on dataset balancing and code quality, delivering measurable business value through improved model fairness, stability, and maintainability. The work enabled more representative training data, reducing bias in model evaluation and enabling more reliable experimentation across underrepresented classes.
March 2025
4 Commits • 1 Features
Mar 1, 2025In March 2025, completed key enhancements for the dsi-clinic/CMAP repository focused on dataset balancing and code quality, delivering measurable business value through improved model fairness, stability, and maintainability. The work enabled more representative training data, reducing bias in model evaluation and enabling more reliable experimentation across underrepresented classes.
February 2025
9 Commits • 4 Features
Feb 1, 2025February 2025 CMAP monthly summary focusing on delivering a more robust, observable, and maintainable training pipeline. Work centered on introducing a class-balanced sampling flow, boosting training observability with per-class metrics, hardening training configuration order, and cleaning up obsolete tooling. The results improve model training stability, class balance visibility, and maintenance efficiency while enabling data-driven improvements.
9 Commits • 4 Features
Feb 1, 2025February 2025 CMAP monthly summary focusing on delivering a more robust, observable, and maintainable training pipeline. Work centered on introducing a class-balanced sampling flow, boosting training observability with per-class metrics, hardening training configuration order, and cleaning up obsolete tooling. The results improve model training stability, class balance visibility, and maintenance efficiency while enabling data-driven improvements.
February 2025
January 2025
5 Commits • 2 Features
Jan 1, 2025January 2025 highlights CMAP: Delivered a modular training pipeline with dependency-injected configuration, refactored dataset construction, and standardized CLI argument parsing to boost modularity, testability, and reproducibility of experiments. Fixed critical global-variable handling and head-node robustness to reduce training brittleness. Completed code quality improvements and Ruff lint cleanup for build_dataset docstrings, elevating coding standards and maintainability. Overall, these efforts enable faster, safer experimentation and a more maintainable codebase for future feature work.
January 2025
5 Commits • 2 Features
Jan 1, 2025January 2025 highlights CMAP: Delivered a modular training pipeline with dependency-injected configuration, refactored dataset construction, and standardized CLI argument parsing to boost modularity, testability, and reproducibility of experiments. Fixed critical global-variable handling and head-node robustness to reduce training brittleness. Completed code quality improvements and Ruff lint cleanup for build_dataset docstrings, elevating coding standards and maintainability. Overall, these efforts enable faster, safer experimentation and a more maintainable codebase for future feature work.
Activity
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Quality Metrics
Correctness87.6%
Maintainability89.2%
Architecture86.2%
Performance78.4%
AI Usage21.6%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonYAML
Technical Skills
Argument ParsingCI/CDCode FormattingCode ModularityComputer VisionConfiguration ManagementData AnalysisData AugmentationData EngineeringData LoggingData PreprocessingData SamplingData ScienceData VisualizationDataset Management
Repositories Contributed To
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