dsi-clinic/CMAP
Jan 2025 – Apr 2025
4 Months active
Languages Used
MarkdownPythonJupyter NotebookYAML
Technical Skills
Data ProcessingDocumentationGeospatial AnalysisScientific ComputingTechnical WritingCode Formatting
cassiehaas
PROFILE
Cassiehaas
Worked on the dsi-clinic/CMAP repository over four months, delivering eight features and resolving four bugs to advance machine learning workflows for geospatial analysis. Developed and integrated Digital Elevation Model (DEM) normalization, visualization, and data handling into the training pipeline, using Python, PyTorch, and YAML for configuration and scripting. Enhanced experiment throughput by parallelizing training across GPUs, improving resource utilization and reproducibility. Refactored code for maintainability, improved documentation, and streamlined configuration management. Addressed reliability by fixing GPU device selection and optimizing memory usage. Upgraded CI/CD workflows with GitHub Actions and pre-commit tooling, ensuring code quality and scalable, automated experimentation.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
40Total
Bugs
4
Commits
40
Features
8
Lines of code
2,083
Activity Months4
Your Network
23 peopleShared Repositories
23
Alan ChermanMember
Alan ChermanMember
Alan ChermanMember
Anna PoonMember
Anna PoonMember
Anna PoonMember
Anna Elizabeth WoodardMember
Nathan HaasMember
Collin Edward KimMember
Work History
April 2025
6 Commits • 2 Features
Apr 1, 2025In April 2025, CMAP delivered key capabilities to accelerate ML experimentation, improve reliability, and strengthen CI quality. The team implemented a parallelized training workflow that enables multiprocessing across GPUs, reorganizing trial execution and refining argument handling and serialization for multiprocessing. This included dataset initialization tweaks and RGB channel adjustments to support robust parallel runs, resulting in faster experiment throughput and better resource utilization. A critical GPU targeting bug was fixed, ensuring training uses the intended CUDA device as configured, improving reproducibility and correctness. CI reliability was enhanced through an update to pre-commit tooling (v3.0.1) and associated workflow adjustments to leverage newer linting features and fixes. Additional work underpins long-term stability by addressing config/args unpacking and serialization issues within multiprocessing, setting the stage for scalable experimentation and easier maintenance.
6 Commits • 2 Features
Apr 1, 2025In April 2025, CMAP delivered key capabilities to accelerate ML experimentation, improve reliability, and strengthen CI quality. The team implemented a parallelized training workflow that enables multiprocessing across GPUs, reorganizing trial execution and refining argument handling and serialization for multiprocessing. This included dataset initialization tweaks and RGB channel adjustments to support robust parallel runs, resulting in faster experiment throughput and better resource utilization. A critical GPU targeting bug was fixed, ensuring training uses the intended CUDA device as configured, improving reproducibility and correctness. CI reliability was enhanced through an update to pre-commit tooling (v3.0.1) and associated workflow adjustments to leverage newer linting features and fixes. Additional work underpins long-term stability by addressing config/args unpacking and serialization issues within multiprocessing, setting the stage for scalable experimentation and easier maintenance.
April 2025
March 2025
15 Commits • 3 Features
Mar 1, 2025March 2025 CMAP monthly summary for dsi-clinic/CMAP focusing on delivering business-value and robust technical outcomes. Key features were implemented to enhance DEM data handling, normalization, and configuration, accompanied by data loading improvements and documentation. Reliability improvements in plotting and training paths reduced runtime errors and improved scalability. Overall, the month delivered measurable improvements in model readiness, reproducibility, and user guidance, aligning with roadmap priorities.
March 2025
15 Commits • 3 Features
Mar 1, 2025March 2025 CMAP monthly summary for dsi-clinic/CMAP focusing on delivering business-value and robust technical outcomes. Key features were implemented to enhance DEM data handling, normalization, and configuration, accompanied by data loading improvements and documentation. Reliability improvements in plotting and training paths reduced runtime errors and improved scalability. Overall, the month delivered measurable improvements in model readiness, reproducibility, and user guidance, aligning with roadmap priorities.
February 2025
14 Commits • 1 Features
Feb 1, 2025February 2025 CMAP monthly recap: Implemented end-to-end Digital Elevation Model (DEM) integration and visualization enhancements in the training pipeline, delivering DEM-driven image generation, plotting, and robust normalization; improved performance and maintainability while ensuring metric reliability and clear business value.
14 Commits • 1 Features
Feb 1, 2025February 2025 CMAP monthly recap: Implemented end-to-end Digital Elevation Model (DEM) integration and visualization enhancements in the training pipeline, delivering DEM-driven image generation, plotting, and robust normalization; improved performance and maintainability while ensuring metric reliability and clear business value.
February 2025
January 2025
5 Commits • 2 Features
Jan 1, 2025January 2025 CMAP (dsi-clinic/CMAP): Implemented Z-score normalization for the difference DEM, refactored normalization logic for clarity and reuse, and expanded DEM documentation and contributor information. These efforts standardize cross-dataset comparisons, improve reproducibility, and strengthen onboarding for contributors.
January 2025
5 Commits • 2 Features
Jan 1, 2025January 2025 CMAP (dsi-clinic/CMAP): Implemented Z-score normalization for the difference DEM, refactored normalization logic for clarity and reuse, and expanded DEM documentation and contributor information. These efforts standardize cross-dataset comparisons, improve reproducibility, and strengthen onboarding for contributors.
Activity
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Quality Metrics
Correctness82.4%
Maintainability84.0%
Architecture78.0%
Performance70.4%
AI Usage20.6%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonYAML
Technical Skills
CI/CDCode CleanupCode FormattingCommand Line InterfaceCommand-line InterfaceConfiguration ManagementDEM AnalysisData ConfigurationData EngineeringData HandlingData LoadingData PreprocessingData ProcessingData ScienceData Visualization
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