lanl/Yoke
Apr 2025 – May 2025
2 Months active
Languages Used
CUDAPythonrst
Technical Skills
Code FormattingCode LintingData Loading OptimizationDocumentationGPU ComputingLinting
Jemma Stachelek
PROFILE
Jemma Stachelek
Jemma Stachelek enhanced the lanl/Yoke repository by developing GPU-aware features and improving code quality over a two-month period. She implemented automatic device placement for deep learning models using PyTorch and CUDA, enabling SwinV2 and test suites to leverage GPU acceleration when available. Her work included optimizing data loading and training throughput, as well as refining test fixtures to support these changes. Jemma also updated documentation to clarify API data structures, reducing onboarding friction. Throughout, she applied code formatting and linting standards with Python and rst, resulting in more maintainable, performant, and developer-friendly workflows for the project.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
3
Lines of code
65
Activity Months2
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for lanl/Yoke: Delivered GPU-aware initialization by enabling automatic device placement for SwinV2 when CUDA is available, including a minor test fixture adjustment. Fixed API clarity by updating documentation to specify that the data tuple includes lead time in training/evaluation APIs. Impact: improved GPU utilization and faster experimentation cycles, with clearer API guidance and reduced onboarding/support overhead. Technologies demonstrated: Python, PyTorch, CUDA integration, testing fixtures, and API documentation standards.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for lanl/Yoke: Delivered GPU-aware initialization by enabling automatic device placement for SwinV2 when CUDA is available, including a minor test fixture adjustment. Fixed API clarity by updating documentation to specify that the data tuple includes lead time in training/evaluation APIs. Impact: improved GPU utilization and faster experimentation cycles, with clearer API guidance and reduced onboarding/support overhead. Technologies demonstrated: Python, PyTorch, CUDA integration, testing fixtures, and API documentation standards.
May 2025
April 2025
6 Commits • 2 Features
Apr 1, 2025Month: 2025-04. Focused on delivering performance, reliability, and maintainability improvements for lanl/Yoke. Implemented GPU-aware test and training optimizations, along with targeted code quality cleanups.
April 2025
6 Commits • 2 Features
Apr 1, 2025Month: 2025-04. Focused on delivering performance, reliability, and maintainability improvements for lanl/Yoke. Implemented GPU-aware test and training optimizations, along with targeted code quality cleanups.
Activity
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Quality Metrics
Correctness95.0%
Maintainability97.6%
Architecture87.6%
Performance92.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CUDAPythonrst
Technical Skills
Code FormattingCode LintingData Loading OptimizationDeep LearningDocumentationGPU ComputingLintingModel DeploymentPerformance OptimizationPyTorchTesting
Repositories Contributed To
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