lanl/Yoke
Nov 2024 – Aug 2025
6 Months active
Languages Used
PythonBashInputShellCSVC++SQL
Technical Skills
Data ParallelizationClean Code PracticesCode RefactoringConfiguration ManagementData EngineeringData Loading
Soumi De
PROFILE
Soumi De
Soumide worked on the lanl/Yoke repository, building and refining distributed deep learning workflows for reward network training. Over six months, Soumide delivered features such as NPZ-based data loaders, scalable multi-GPU and DDP training harnesses, and dynamic learning rate scheduling, all aimed at improving throughput and reproducibility. The engineering approach emphasized clean code, robust testing with Pytest, and maintainable configuration using Python and SLURM scripting. Soumide addressed stability and debugging challenges by resolving memory errors, enhancing documentation, and standardizing training scripts. This work enabled faster experimentation, more reliable convergence, and efficient resource utilization across high-performance computing environments.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
43Total
Bugs
2
Commits
43
Features
8
Lines of code
6,493
Activity Months6
Your Network
73 people
Work History
August 2025
4 Commits • 1 Features
Aug 1, 2025In August 2025, delivered a targeted upgrade to the Reward Network Training workflow within the lanl/Yoke repository, focusing on efficiency, stability, and maintainability. The work consolidates multiple improvements into a coherent training flow that scales on cluster environments and improves model training outcomes while aligning project documentation and naming conventions.
4 Commits • 1 Features
Aug 1, 2025In August 2025, delivered a targeted upgrade to the Reward Network Training workflow within the lanl/Yoke repository, focusing on efficiency, stability, and maintainability. The work consolidates multiple improvements into a coherent training flow that scales on cluster environments and improves model training outcomes while aligning project documentation and naming conventions.
August 2025
July 2025
2 Commits
Jul 1, 2025July 2025 monthly summary for lanl/Yoke: Focused on stabilizing the training workflow and debugging enhancements to accelerate iteration and improve reliability. Key outcomes include memory error resolution in train_lsc_reward.py, faster iteration through SLURM config adjustments, and debugging instrumentation added to training utilities without altering core functionality.
July 2025
2 Commits
Jul 1, 2025July 2025 monthly summary for lanl/Yoke: Focused on stabilizing the training workflow and debugging enhancements to accelerate iteration and improve reliability. Key outcomes include memory error resolution in train_lsc_reward.py, faster iteration through SLURM config adjustments, and debugging instrumentation added to training utilities without altering core functionality.
June 2025
15 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for lanl/Yoke: Focused modernization and stabilization of the reward-training workflow, delivering a cleaner harness, dynamic optimization, and stronger test coverage that enable faster experimentation with improved convergence and lower maintenance overhead. Key outcomes: - LSC Reward Network Harness Upgrade and Cleanup: Introduced the ch_lsc_reward harness, consolidated training infrastructure, removed the deprecated chicoma_lsc_reward module, updated SLURM and input configurations, and adjusted training script parameters (image size 800). A symlink-based approach was adopted to reduce churn and maintenance; obsolete Chicoma files and commented code were removed. - Learning Rate Scheduling for Reward Training: Implemented a per-step learning rate scheduler with configurable hyperparameters, integrated into the reward training path, updated scheduling parameters in the CSV, and added a toggle to enable/disable the scheduler; eliminated redundant scheduler increments for correctness. - Testing, Reliability, and Evaluation Coverage: Expanded tests for reward functions and evaluation steps, improved test infra, and addressed linting issues to raise code quality; pytest noise was silenced to stabilize CI pipelines. - Cross-cutting maintainability and consistency: Alignments with policy-network harness naming, reduced technical debt through cleanup, and streamlined configuration/infra changes to support ongoing experimentation with minimal overhead. Impact and business value: - Faster experimentation cycles and more reliable training convergence due to the LR scheduler and cleaner harness. - Reduced maintenance overhead via consolidation, deprecation removal, and stronger test infrastructure. - Improved resource efficiency and scalability as a result of standardized SLURM/input configurations and a cleaner training pipeline. Technologies/skills demonstrated: - RL harness design and refactor, Python-based training loops, per-step LR scheduling, SLURM configuration, test-driven development, linting/CI hygiene, and cross-repo standardization.
15 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for lanl/Yoke: Focused modernization and stabilization of the reward-training workflow, delivering a cleaner harness, dynamic optimization, and stronger test coverage that enable faster experimentation with improved convergence and lower maintenance overhead. Key outcomes: - LSC Reward Network Harness Upgrade and Cleanup: Introduced the ch_lsc_reward harness, consolidated training infrastructure, removed the deprecated chicoma_lsc_reward module, updated SLURM and input configurations, and adjusted training script parameters (image size 800). A symlink-based approach was adopted to reduce churn and maintenance; obsolete Chicoma files and commented code were removed. - Learning Rate Scheduling for Reward Training: Implemented a per-step learning rate scheduler with configurable hyperparameters, integrated into the reward training path, updated scheduling parameters in the CSV, and added a toggle to enable/disable the scheduler; eliminated redundant scheduler increments for correctness. - Testing, Reliability, and Evaluation Coverage: Expanded tests for reward functions and evaluation steps, improved test infra, and addressed linting issues to raise code quality; pytest noise was silenced to stabilize CI pipelines. - Cross-cutting maintainability and consistency: Alignments with policy-network harness naming, reduced technical debt through cleanup, and streamlined configuration/infra changes to support ongoing experimentation with minimal overhead. Impact and business value: - Faster experimentation cycles and more reliable training convergence due to the LR scheduler and cleaner harness. - Reduced maintenance overhead via consolidation, deprecation removal, and stronger test infrastructure. - Improved resource efficiency and scalability as a result of standardized SLURM/input configurations and a cleaner training pipeline. Technologies/skills demonstrated: - RL harness design and refactor, Python-based training loops, per-step LR scheduling, SLURM configuration, test-driven development, linting/CI hygiene, and cross-repo standardization.
June 2025
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 monthly summary for lanl/Yoke: Delivered two major feature streams with robust testing and multi-GPU training support, delivering measurable business value through more reliable data ingestion and scalable experimentation pipelines. The work emphasizes reliability, reproducibility, and performance on NPZ-based datasets and reward-network training workflows.
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 monthly summary for lanl/Yoke: Delivered two major feature streams with robust testing and multi-GPU training support, delivering measurable business value through more reliable data ingestion and scalable experimentation pipelines. The work emphasizes reliability, reproducibility, and performance on NPZ-based datasets and reward-network training workflows.
April 2025
14 Commits • 2 Features
Apr 1, 2025April 2025 delivered two major feature sets for lanl/Yoke, focusing on robust data ingestion, scalable training workflows, and overall code quality to accelerate experimentation and improve reliability. The Dataset Loading Pipeline Enhancements introduced an NPZ-based data loader, robust labeled data handling, and improved path resolution for CSV/NPZ sources, complemented by targeted linting and quality improvements across the dataset module. The Training Pipeline and Resource Configuration Enhancements added a reward-network training harness, multi-GPU configurations, and SLURM adjustments to streamline scalable experiments, along with training-script hygiene. In addition, targeted bug fixes and documentation updates reduced data-prep friction and stabilized end-to-end runs. This work improves throughput, reproducibility, and business value by enabling faster, more reliable model iteration across larger compute resources.
14 Commits • 2 Features
Apr 1, 2025April 2025 delivered two major feature sets for lanl/Yoke, focusing on robust data ingestion, scalable training workflows, and overall code quality to accelerate experimentation and improve reliability. The Dataset Loading Pipeline Enhancements introduced an NPZ-based data loader, robust labeled data handling, and improved path resolution for CSV/NPZ sources, complemented by targeted linting and quality improvements across the dataset module. The Training Pipeline and Resource Configuration Enhancements added a reward-network training harness, multi-GPU configurations, and SLURM adjustments to streamline scalable experiments, along with training-script hygiene. In addition, targeted bug fixes and documentation updates reduced data-prep friction and stabilized end-to-end runs. This work improves throughput, reproducibility, and business value by enabling faster, more reliable model iteration across larger compute resources.
April 2025
November 2024
1 Commits
Nov 1, 2024November 2024 monthly work summary for lanl/Yoke. Focused on stability, data-parallel correctness, and code quality. No new features delivered; maintenance and debugging prioritized to reduce risk ahead of feature work.
November 2024
1 Commits
Nov 1, 2024November 2024 monthly work summary for lanl/Yoke. Focused on stability, data-parallel correctness, and code quality. No new features delivered; maintenance and debugging prioritized to reduce risk ahead of feature work.
Activity
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Quality Metrics
Correctness88.0%
Maintainability88.8%
Architecture83.0%
Performance78.4%
AI Usage20.4%
Skills & Technologies
Programming Languages
BashC++CSVInputPythonSQLShell
Technical Skills
Bug FixingClean Code PracticesCode RefactoringConfiguration ManagementDDPData EngineeringData LoadingData ParallelismData ParallelizationData PreprocessingData ProcessingDataset ManagementDebuggingDeep LearningDistributed Computing
Repositories Contributed To
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