pytorch/torchrec
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisMachine LearningMetric ComputationUnit Testing
Swetha Subramanian
PROFILE
Swetha Subramanian
Swetha Subramanian developed a granular per-label precision metric for multi-label classification in the pytorch/torchrec repository, focusing on enhancing model evaluation and observability during training. She implemented per-label true positive and false positive computation by decoding LSB-first bit-encoded labels, enabling detailed precision tracking for each label. The feature integrated seamlessly with TensorBoard, allowing real-time visualization of per-label metrics. Swetha used Python and applied skills in data analysis, machine learning, and metric computation, ensuring robust unit testing and thorough documentation. Her work addressed the need for finer-grained monitoring in multi-label tasks, supporting better model selection and training insights for the team.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
671
Activity Months1
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Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for pytorch/torchrec: Delivered a granular per-label precision metric for multi-label classification, enabling per-label precision tracking during training and visualization in TensorBoard. The feature supports multi-label tasks with per-label TP/FP calculation and a decoding scheme for LSB-first bit-encoded labels to expose per-label precision. Implemented and integrated in codebase with commit 530dddbd22f13488947a422cf6979ddb710818ec, tied to PR 3661 (https://github.com/meta-pytorch/torchrec/pull/3661) and differential revision D87835212. This work enhances observability and model evaluation, enabling finer-grained monitoring and better model selection during training. No major bugs fixed this month; primary focus on feature development, code quality, and documentation. Reviewed by iamzainhuda.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for pytorch/torchrec: Delivered a granular per-label precision metric for multi-label classification, enabling per-label precision tracking during training and visualization in TensorBoard. The feature supports multi-label tasks with per-label TP/FP calculation and a decoding scheme for LSB-first bit-encoded labels to expose per-label precision. Implemented and integrated in codebase with commit 530dddbd22f13488947a422cf6979ddb710818ec, tied to PR 3661 (https://github.com/meta-pytorch/torchrec/pull/3661) and differential revision D87835212. This work enhances observability and model evaluation, enabling finer-grained monitoring and better model selection during training. No major bugs fixed this month; primary focus on feature development, code quality, and documentation. Reviewed by iamzainhuda.
January 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisMachine LearningMetric ComputationUnit Testing
Repositories Contributed To
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