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 delivering GPU-aware optimizations and improving code maintainability 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, refining test fixtures, and aligning code formatting with Ruff linting standards, which improved test throughput and developer productivity. Jemma also clarified API documentation to specify data tuple structure, reducing onboarding friction. The depth of her contributions is reflected in targeted performance improvements, robust testing, and clear documentation, demonstrating strong engineering fundamentals.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
3
Lines of code
65
Activity Months2
Your Network
14 peopleShared Repositories
14
Andrew Michael ToivonenMember
dschodt-lanlMember
Gal Erez EgoziMember
Kyle HickmannMember
Kyle HickmannMember
L281800Member
rworleyMember
ryanw18Member
Sharmistha ChakrabartiMember
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
Loading activity data...
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
Overview of all repositories you've contributed to across your timeline