lanl/Yoke
May 2025 – May 2025
1 Month active
Languages Used
BashBatchCSVPythonShell
Technical Skills
Argument ParsingCLI DevelopmentCode OrganizationCode RefactoringData ConfigurationData Processing
rworley
PROFILE
Rworley
In May 2025, Ryan Worley developed a modular PyTorch-based MNIST CNN model and surrogate training harness for the lanl/Yoke repository, focusing on experiment automation and maintainability. He modernized the MNIST study workflow to support automated hyperparameter-driven experiments using CSV configuration, and introduced robust argument parsing and data directory management. Ryan implemented multi-platform job submission via CLI, supporting SLURM, shell, and batch templates, and improved code quality through linting and refactoring. His work leveraged Python, Bash, and shell scripting, demonstrating depth in experiment management, high-performance computing, and code organization, resulting in a more flexible and maintainable research codebase.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
16Total
Bugs
1
Commits
16
Features
4
Lines of code
1,223
Activity Months1
Your Network
73 people
Work History
May 2025
16 Commits • 4 Features
May 1, 2025May 2025 Monthly Summary for lanl/Yoke: Delivered a PyTorch-based MNIST CNN model with a modular surrogate training harness supporting argument parsing and data-dir configuration, modernized the MNIST study harness for automated experiments driven by a hyperparameter CSV, and added robust multi-submission type support (SLURM, shell, batch) with corresponding training templates. Implemented code quality improvements across the MNIST model and startup scripts to improve maintainability and readability.
16 Commits • 4 Features
May 1, 2025May 2025 Monthly Summary for lanl/Yoke: Delivered a PyTorch-based MNIST CNN model with a modular surrogate training harness supporting argument parsing and data-dir configuration, modernized the MNIST study harness for automated experiments driven by a hyperparameter CSV, and added robust multi-submission type support (SLURM, shell, batch) with corresponding training templates. Implemented code quality improvements across the MNIST model and startup scripts to improve maintainability and readability.
May 2025
Activity
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Quality Metrics
Correctness87.0%
Maintainability87.6%
Architecture85.0%
Performance76.2%
AI Usage23.8%
Skills & Technologies
Programming Languages
BashBatchCSVPythonShell
Technical Skills
Argument ParsingCLI DevelopmentCode OrganizationCode RefactoringData ConfigurationData ProcessingData ScienceDeep LearningDocumentationExperiment ManagementFile Path ManagementHPCHigh-Performance ComputingLintingMachine Learning
Repositories Contributed To
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