Concise monthly summary for lanl/Yoke - April 2025 focusing on delivered features, major fixes, impact, and technologies demonstrated. The primary accomplishment this month is the GIF Animation Workflow Automation to streamline cross-study evaluation and visualization workflows.

March 2025 — lanl/Yoke: Delivered end-to-end enhancements to the Chicoma LodeRunner training workflow and improved observability and reliability. Key features include configurable channel subsampling with new config files and training script, CLI support to configure channel map size, and half-image processing to reduce compute. Also completed logging/Slurm cleanup and logger import path updates to improve batch-job reliability and maintainability. Business value: faster experimentation cycles, more predictable training performance, and easier long-term maintenance.

February 2025 monthly summary for lanl/Yoke: Delivered Channel Subsampling feature for training and fixed a critical bug in the channel map generator. Updated the training pipeline to support subsampling via hyperparameters, training script, and dataset loading, enabling flexible experiments with varying channel configurations.

January 2025 monthly summary for lanl/Yoke focusing on reliability improvements to SLURM-based job submissions and configuration consistency across training templates and the Venado GPU partition. The changes reduce submission failures and misconfigurations by correcting SLURM account names and removing obsolete reservations, ensuring consistent resource allocation for GPU workloads.

December 2024: Focused on improving dataset key processing reliability in lanl/Yoke through comprehensive unit tests for LSCnpz2key. This work strengthens data integrity in the LSC dataset module, enhances regression detection, and supports maintainable code going forward.

Month: 2024-10 | lanl/Yoke repository Key features delivered: - ConvTranspose2d shape utility test coverage added, focusing on correct output dimensions and total elements, including scenarios with output padding. Major bugs fixed: - No major bugs resolved in this period for lanl/Yoke. Overall impact and accomplishments: - Strengthened reliability of CNN utility shape calculations, reducing risk of runtime dimension errors in convolutional architectures and enabling safer model integration. - Provides targeted pytest coverage for convtranspose2d_shape to guard against regressions and support future feature work. Technologies/skills demonstrated: - Python, pytest, unit testing strategy, test-driven validation for CNN utilities, and documentation of work via commit references.