lanl/Yoke
Oct 2024 – Sep 2025
12 Months active
Languages Used
BashCSVInput configurationPythonSQLShellJinjaSLURM
Technical Skills
Configuration ManagementData EngineeringData HandlingData LoadingData OrganizationData Splitting
Kyle Hickmann
PROFILE
Kyle Hickmann
Over the past year, Kevin Hickman engineered scalable machine learning infrastructure and robust training pipelines for the lanl/Yoke repository. He developed distributed training harnesses and modularized utilities using Python and PyTorch, enabling efficient experimentation on HPC systems. His work included refactoring CNN architectures, integrating DDP and Flux-based workflows, and enhancing dataset management for both policy and inverse learning tasks. By expanding test coverage, improving code quality with type hints and documentation, and modernizing CI/CD workflows, Kevin reduced technical debt and improved maintainability. These efforts resulted in reproducible, high-throughput research pipelines and reliable, production-ready model training environments for scientific computing.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
335Total
Bugs
46
Commits
335
Features
107
Lines of code
704,145
Activity Months12
Your Network
73 people
Work History
September 2025
17 Commits • 5 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for lanl/Yoke: Delivered a focused set of enhancements that strengthen training reliability, improve visibility into training dynamics, and raise code quality, enabling safer experimentation and faster maintenance in distributed training workflows.
17 Commits • 5 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for lanl/Yoke: Delivered a focused set of enhancements that strengthen training reliability, improve visibility into training dynamics, and raise code quality, enabling safer experimentation and faster maintenance in distributed training workflows.
September 2025
August 2025
8 Commits • 4 Features
Aug 1, 2025August 2025 (lanl/Yoke) — Deliveries focused on training dynamics, scalable pipelines, and architecture modernization for LSC-related models. Key features delivered include per-block gradient computation with progressive unfreezing for LSC policy training, a distributed LSC inverse training harness with enhanced datasets and SLURM integration, Image2VectorCNN integration with CNN refactors (cnn_utils) and renaming of CNN components for clarity, and Distributed Data Parallel (DDP) training support for CNNs to improve scalability and throughput. No explicit major bugs were documented in this month’s scope; however, the refactors and new pipelines contributed to improved stability, reproducibility, and experimentation throughput. Overall impact and accomplishments: The changes enable faster, more reliable experimentation with LSC policies and inverse learning, reduce bottlenecks in distributed training, and modernize the CNN architecture for clearer maintenance and future extensions. These efforts set the stage for more aggressive hyperparameter studies and larger-scale training runs on HPC infrastructure. Technologies/skills demonstrated: PyTorch (including DDP), per-block gradient control, distributed data workflows, SLURM-based training orchestration, dataset engineering for inverse training (include_time flag, hfield2cntr datasets), CNN architecture refactoring (Image2VectorCNN, Image2ScalarCNN), and codebase modernization (cnn_utils, renamed components).
August 2025
8 Commits • 4 Features
Aug 1, 2025August 2025 (lanl/Yoke) — Deliveries focused on training dynamics, scalable pipelines, and architecture modernization for LSC-related models. Key features delivered include per-block gradient computation with progressive unfreezing for LSC policy training, a distributed LSC inverse training harness with enhanced datasets and SLURM integration, Image2VectorCNN integration with CNN refactors (cnn_utils) and renaming of CNN components for clarity, and Distributed Data Parallel (DDP) training support for CNNs to improve scalability and throughput. No explicit major bugs were documented in this month’s scope; however, the refactors and new pipelines contributed to improved stability, reproducibility, and experimentation throughput. Overall impact and accomplishments: The changes enable faster, more reliable experimentation with LSC policies and inverse learning, reduce bottlenecks in distributed training, and modernize the CNN architecture for clearer maintenance and future extensions. These efforts set the stage for more aggressive hyperparameter studies and larger-scale training runs on HPC infrastructure. Technologies/skills demonstrated: PyTorch (including DDP), per-block gradient control, distributed data workflows, SLURM-based training orchestration, dataset engineering for inverse training (include_time flag, hfield2cntr datasets), CNN architecture refactoring (Image2VectorCNN, Image2ScalarCNN), and codebase modernization (cnn_utils, renamed components).
July 2025
48 Commits • 18 Features
Jul 1, 2025July 2025 – lanl/Yoke: Delivered major modularization, expanded test coverage, and pipeline reliability improvements that drive safer, scalable training across experiments. Key features delivered include modularizing LodeRunner integration and refactoring torch_training_utils to support checkpointing and cross-utility imports; new utils modules for parameters, dataloader, and restart utilities to enable modular data loading and configuration; and broad test coverage enhancements with nc_dataset tests and NPZ/dataset tests, complemented by ongoing test infrastructure maintenance (imports, linting, formatting). Major pipeline and reliability improvements include the Artimis allocation switch for resource management, per-block gradient RMS logging, and block-wise learning-rate configuration to improve training stability and efficiency. Additional enhancements include policy timing instrumentation and learning-rate study scaffolding to enable more controlled experimentation and reproducibility. Overall, these changes reduce technical debt, improve maintainability, and enable more scalable, reproducible experiments while preserving and improving model performance.
48 Commits • 18 Features
Jul 1, 2025July 2025 – lanl/Yoke: Delivered major modularization, expanded test coverage, and pipeline reliability improvements that drive safer, scalable training across experiments. Key features delivered include modularizing LodeRunner integration and refactoring torch_training_utils to support checkpointing and cross-utility imports; new utils modules for parameters, dataloader, and restart utilities to enable modular data loading and configuration; and broad test coverage enhancements with nc_dataset tests and NPZ/dataset tests, complemented by ongoing test infrastructure maintenance (imports, linting, formatting). Major pipeline and reliability improvements include the Artimis allocation switch for resource management, per-block gradient RMS logging, and block-wise learning-rate configuration to improve training stability and efficiency. Additional enhancements include policy timing instrumentation and learning-rate study scaffolding to enable more controlled experimentation and reproducibility. Overall, these changes reduce technical debt, improve maintainability, and enable more scalable, reproducible experiments while preserving and improving model performance.
July 2025
June 2025
17 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for lanl/Yoke: Delivered Flux-based submission integration and tooling enhancements, expanded ADAMS LSC policy training production capabilities, and improved code quality with typing and documentation. These efforts boosted workflow efficiency, scalability, and maintainability, directly translating to faster iteration, more robust training pipelines, and clearer interfaces for future work.
June 2025
17 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for lanl/Yoke: Delivered Flux-based submission integration and tooling enhancements, expanded ADAMS LSC policy training production capabilities, and improved code quality with typing and documentation. These efforts boosted workflow efficiency, scalability, and maintainability, directly translating to faster iteration, more robust training pipelines, and clearer interfaces for future work.
May 2025
11 Commits • 2 Features
May 1, 2025May 2025 performance summary for lanl/Yoke: this month focused on reliability improvements and scalable training capabilities. Key outcomes include stabilization of the test suite, introduction and refinement of a Flux-based distributed training workflow with ROCm DDP support, and enhancements to policy training dynamics through an LR scheduler and expanded configuration. These efforts reduce flaky tests, enable GPU-enabled distributed runs, and provide a more tunable learning-rate strategy for policy models.
11 Commits • 2 Features
May 1, 2025May 2025 performance summary for lanl/Yoke: this month focused on reliability improvements and scalable training capabilities. Key outcomes include stabilization of the test suite, introduction and refinement of a Flux-based distributed training workflow with ROCm DDP support, and enhancements to policy training dynamics through an LR scheduler and expanded configuration. These efforts reduce flaky tests, enable GPU-enabled distributed runs, and provide a more tunable learning-rate strategy for policy models.
May 2025
April 2025
60 Commits • 21 Features
Apr 1, 2025April 2025 performance summary for lanl/Yoke: Delivered significant business value through documentation quality, code reliability, and production-readiness improvements across the policy and dataset stack. Key features delivered include doc enhancements for autodoc-based Sphinx docs (builds, theme, docstrings), linting and tests for the policy module, memory-efficient policy model architectures, and readiness for policy training and production runs. Major bugs fixed improved correctness and stability in data handling and model interactions (save/load validation, immutable data structures, CUDA pin_memory guard, missing half_image attribute). These efforts collectively improved experiment throughput, reproducibility, and onboarding, while maintaining a lean, maintainable codebase. Technologies demonstrated include Python, Sphinx, linting/CI workflows, dataset engineering, CUDA-aware memory optimizations, and robust production pipelines.
April 2025
60 Commits • 21 Features
Apr 1, 2025April 2025 performance summary for lanl/Yoke: Delivered significant business value through documentation quality, code reliability, and production-readiness improvements across the policy and dataset stack. Key features delivered include doc enhancements for autodoc-based Sphinx docs (builds, theme, docstrings), linting and tests for the policy module, memory-efficient policy model architectures, and readiness for policy training and production runs. Major bugs fixed improved correctness and stability in data handling and model interactions (save/load validation, immutable data structures, CUDA pin_memory guard, missing half_image attribute). These efforts collectively improved experiment throughput, reproducibility, and onboarding, while maintaining a lean, maintainable codebase. Technologies demonstrated include Python, Sphinx, linting/CI workflows, dataset engineering, CUDA-aware memory optimizations, and robust production pipelines.
March 2025
32 Commits • 8 Features
Mar 1, 2025Month: 2025-03 | Repository: lanl/Yoke | Focus: testing asset optimization, CI/CD modernization, code quality, coverage security, and distributed training readiness. Delivered a leaner test data/assets footprint, modernized packaging and CI workflows, strengthened code quality and security practices, prototyped dynamic DDP checkpointing, and improved CI reliability and documentation. Result: faster, more reliable test cycles; lower maintenance and infra friction; and foundations for scalable model training in distributed environments.
32 Commits • 8 Features
Mar 1, 2025Month: 2025-03 | Repository: lanl/Yoke | Focus: testing asset optimization, CI/CD modernization, code quality, coverage security, and distributed training readiness. Delivered a leaner test data/assets footprint, modernized packaging and CI workflows, strengthened code quality and security practices, prototyped dynamic DDP checkpointing, and improved CI reliability and documentation. Result: faster, more reliable test cycles; lower maintenance and infra friction; and foundations for scalable model training in distributed environments.
March 2025
February 2025
19 Commits • 6 Features
Feb 1, 2025February 2025 summary for lanl/Yoke focused on advancing distributed training capabilities, robust data handling, and improved evaluation tooling to accelerate research-to-production cycles. The month delivered production-ready DDP enhancements for Chicoma, pipeline refinements for shaped charge training, and study-level improvements for EADA, alongside expanded temporal datasets and enhanced LodeRunner evaluation visuals. These efforts improved reproducibility, scalability, and data quality while tightening release hygiene.
February 2025
19 Commits • 6 Features
Feb 1, 2025February 2025 summary for lanl/Yoke focused on advancing distributed training capabilities, robust data handling, and improved evaluation tooling to accelerate research-to-production cycles. The month delivered production-ready DDP enhancements for Chicoma, pipeline refinements for shaped charge training, and study-level improvements for EADA, alongside expanded temporal datasets and enhanced LodeRunner evaluation visuals. These efforts improved reproducibility, scalability, and data quality while tightening release hygiene.
January 2025
80 Commits • 26 Features
Jan 1, 2025January 2025 performance summary: Delivered data prototyping, expanded test coverage, and hardened the codebase while establishing a scalable distributed training foundation. Key data features include LSC and policy datasets with tests, and a CNN/value-network prototype with accompanying tests. Training infrastructure was advanced with Lightning integration, DDP and Fabric harness groundwork, and multi-node readiness, enabling reliable scalable experiments. Major stability and quality improvements were implemented (Dt tensor consistency, memory usage tuning, and numpy import fixes) to reduce run-time failures and CI noise. This period demonstrates business value through faster experimentation cycles, robust pipelines, and maintainable code via code quality and comprehensive documentation.
80 Commits • 26 Features
Jan 1, 2025January 2025 performance summary: Delivered data prototyping, expanded test coverage, and hardened the codebase while establishing a scalable distributed training foundation. Key data features include LSC and policy datasets with tests, and a CNN/value-network prototype with accompanying tests. Training infrastructure was advanced with Lightning integration, DDP and Fabric harness groundwork, and multi-node readiness, enabling reliable scalable experiments. Major stability and quality improvements were implemented (Dt tensor consistency, memory usage tuning, and numpy import fixes) to reduce run-time failures and CI noise. This period demonstrates business value through faster experimentation cycles, robust pipelines, and maintainable code via code quality and comprehensive documentation.
January 2025
December 2024
13 Commits • 5 Features
Dec 1, 2024December 2024: Yoke - concise monthly summary focused on delivering business value, reliability, and scalability.
December 2024
13 Commits • 5 Features
Dec 1, 2024December 2024: Yoke - concise monthly summary focused on delivering business value, reliability, and scalability.
November 2024
22 Commits • 6 Features
Nov 1, 2024November 2024 was a focused sprint delivering core features, stabilizing training workflows, and establishing foundations for scalable experiments. Delivered features include typed TVT lists for uniq prefixes and explicit function output type; enhanced LodeRunner training wiring for Chicoma and a Venado-based architecture cycle-through prototype; added tests for HDF5 model save/load and model/optimizer load; launched LR_scheduler module with prototypes and tests, plus initial training study scaffolds for LodeRunner benchmarks.
22 Commits • 6 Features
Nov 1, 2024November 2024 was a focused sprint delivering core features, stabilizing training workflows, and establishing foundations for scalable experiments. Delivered features include typed TVT lists for uniq prefixes and explicit function output type; enhanced LodeRunner training wiring for Chicoma and a Venado-based architecture cycle-through prototype; added tests for HDF5 model save/load and model/optimizer load; launched LR_scheduler module with prototypes and tests, plus initial training study scaffolds for LodeRunner benchmarks.
November 2024
October 2024
8 Commits • 3 Features
Oct 1, 2024Oct 2024 summary for lanl/Yoke: Implemented temporal data support for LSC rho2rho datasets, enhanced dataset handling with prefixes, and launched a GPU-accelerated Venado harness for LSC density surrogate training. Strengthened data preparation and testing, standardized file naming conventions, and prepared scalable workflows for future experiments. No major bugs reported during the period; notable improvements lay groundwork for reproducible ML experiments and faster research cycles.
October 2024
8 Commits • 3 Features
Oct 1, 2024Oct 2024 summary for lanl/Yoke: Implemented temporal data support for LSC rho2rho datasets, enhanced dataset handling with prefixes, and launched a GPU-accelerated Venado harness for LSC density surrogate training. Strengthened data preparation and testing, standardized file naming conventions, and prepared scalable workflows for future experiments. No major bugs reported during the period; notable improvements lay groundwork for reproducible ML experiments and faster research cycles.
Activity
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Quality Metrics
Correctness89.4%
Maintainability90.2%
Architecture85.8%
Performance81.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashCSVFluxGit ConfigurationInput configurationJinjaJupyter NotebookMarkdownPytestPython
Technical Skills
Backend DevelopmentBash ScriptingBenchmarkingBug FixingBuild System ConfigurationCI/CDCLI DevelopmentCNNCNNsCUDACheckpointingCode CoverageCode DocumentationCode FormattingCode Linting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline