lanl/Yoke
Apr 2025 – Jan 2026
7 Months active
Languages Used
CUDAPythonrstDockerfileYAMLBashMarkdown
Technical Skills
Code FormattingCode LintingData Loading OptimizationDocumentationGPU ComputingLinting
Jemma Stachelek
PROFILE
Jemma Stachelek
Worked on the lanl/Yoke repository, delivering features that improved deep learning workflows, deployment, and developer productivity. Built GPU-accelerated training and testing pipelines using Python and PyTorch, enabling faster experimentation and robust model evaluation. Introduced model checkpointing and visualization tools to support reproducible research and streamlined debugging. Enhanced deployment reliability by implementing Docker-based containerization and automated CI/CD pipelines with GitHub Actions, ensuring consistent environments and rapid feedback. Refactored training harnesses and startup scripts for maintainability, leveraging Bash scripting and symbolic links to reduce duplication. Focused on code quality through linting, documentation updates, and clear API guidance, supporting scalable, maintainable development.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
18Total
Bugs
1
Commits
18
Features
10
Lines of code
1,195,801
Activity Months7
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
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for lanl/Yoke: Focused on consolidating the training harness startup flow to improve maintainability, reliability, and onboarding. Implemented a single source of truth for harness startup by converting START_study.py into a symbolic link to a central script, reducing duplication and drift across runs.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for lanl/Yoke: Focused on consolidating the training harness startup flow to improve maintainability, reliability, and onboarding. Implemented a single source of truth for harness startup by converting START_study.py into a symbolic link to a central script, reducing duplication and drift across runs.
January 2026
November 2025
5 Commits • 2 Features
Nov 1, 2025November 2025 for lanl/Yoke focused on deployment portability and experiment infrastructure. Delivered a Docker-based containerized deployment setup with a Bash shell inside containers to improve portability and reliability of deployments. Refactored the MNIST training harness to align with the new API structure, introduced a configurable training script for studies, and wired in START_Study infrastructure and default CLI args. Also removed debugging noise to streamline development and improve readability. These efforts reduce deployment friction, accelerate reproducibility of experiments, and enable faster onboarding for new studies.
November 2025
5 Commits • 2 Features
Nov 1, 2025November 2025 for lanl/Yoke focused on deployment portability and experiment infrastructure. Delivered a Docker-based containerized deployment setup with a Bash shell inside containers to improve portability and reliability of deployments. Refactored the MNIST training harness to align with the new API structure, introduced a configurable training script for studies, and wired in START_Study infrastructure and default CLI args. Also removed debugging noise to streamline development and improve readability. These efforts reduce deployment friction, accelerate reproducibility of experiments, and enable faster onboarding for new studies.
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on Lanl/Yoke's containerization groundwork and CI/CD improvements. The work delivered a minimal Dockerfile for reproducible containerization and introduced GitHub Actions workflows for documentation building, testing, and linting to accelerate feedback and improve code quality. No major bugs were recorded/fixed in this scope; the emphasis was on stability, reproducibility, and maintainability. Technologies demonstrated include Docker, GitHub Actions, automated testing, and linting. Business impact includes faster onboarding, consistent environments, and more reliable deployments.
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on Lanl/Yoke's containerization groundwork and CI/CD improvements. The work delivered a minimal Dockerfile for reproducible containerization and introduced GitHub Actions workflows for documentation building, testing, and linting to accelerate feedback and improve code quality. No major bugs were recorded/fixed in this scope; the emphasis was on stability, reproducibility, and maintainability. Technologies demonstrated include Docker, GitHub Actions, automated testing, and linting. Business impact includes faster onboarding, consistent environments, and more reliable deployments.
October 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 focused on delivering a high-impact feature for the lanl/Yoke project by adding model checkpointing and training visualization. The implementation enables saving and resuming long-running training jobs and generating plots to visualize training progress, enhancing reproducibility, monitoring, and decision-making across experiments.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 focused on delivering a high-impact feature for the lanl/Yoke project by adding model checkpointing and training visualization. The implementation enables saving and resuming long-running training jobs and generating plots to visualize training progress, enhancing reproducibility, monitoring, and decision-making across experiments.
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for lanl/Yoke: Focused on delivering evaluation capabilities for neural network analysis in moving MNIST and hydrodynamic simulations, plus improving training reliability through checkpointing. The work emphasizes business value via reproducibility, faster iteration, and reduced risk of training interruptions.
2 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for lanl/Yoke: Focused on delivering evaluation capabilities for neural network analysis in moving MNIST and hydrodynamic simulations, plus improving training reliability through checkpointing. The work emphasizes business value via reproducibility, faster iteration, and reduced risk of training interruptions.
June 2025
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.
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.
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.
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
Activity
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Quality Metrics
Correctness93.4%
Maintainability92.2%
Architecture87.8%
Performance89.0%
AI Usage25.6%
Skills & Technologies
Programming Languages
BashCUDADockerfileMarkdownPythonYAMLrst
Technical Skills
API integrationCI/CDCode FormattingCode LintingContainerizationData Loading OptimizationData VisualizationDebuggingDeep LearningDevOpsDockerDocumentationGPU ComputingGUI DevelopmentLinting
Repositories Contributed To
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