ibm-granite/granite-tsfm
May 2025 – Jun 2025
2 Months active
Languages Used
Jupyter NotebookPythonBashCSVJSONMarkdownShellText
Technical Skills
ClassificationData PreprocessingDeep LearningModel Fine-tuningPyTorchTime Series Analysis
subodh2702
PROFILE
Subodh2702
Subu worked on the ibm-granite/granite-tsfm repository, developing and refining time series classification and imputation workflows over a two-month period. He delivered a comprehensive Jupyter notebook for pulse classification, implementing end-to-end data preprocessing, model configuration, fine-tuning, and evaluation using Python and PyTorch. Subu enhanced pipeline reproducibility by centralizing hyperparameter management and adding reproducibility scripts, while also improving code quality through style enforcement, dependency management, and expanded testing. His work included integrating Hugging Face Transformers, expanding imputation pipelines, and updating documentation, resulting in more robust, maintainable, and reproducible machine learning pipelines for time series data analysis.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
51Total
Bugs
8
Commits
51
Features
28
Lines of code
9,250
Activity Months2
Your Network
12 people
Work History
June 2025
49 Commits • 27 Features
Jun 1, 2025June 2025: Delivered pipeline enhancements and reproducibility improvements across TSPulse workflows. Implemented hyperparameters details to centralize tuning information; added reproducibility scripts for TSPulse classification; expanded imputation pipeline with TSPulse support and alignment fixes; improved code quality through style guidelines, logging upgrades, and dependency hygiene; expanded testing and documentation to boost reliability and onboarding. Note: A prototype for training TSP in preprocess was explored but reverted to preserve pipeline stability.
49 Commits • 27 Features
Jun 1, 2025June 2025: Delivered pipeline enhancements and reproducibility improvements across TSPulse workflows. Implemented hyperparameters details to centralize tuning information; added reproducibility scripts for TSPulse classification; expanded imputation pipeline with TSPulse support and alignment fixes; improved code quality through style guidelines, logging upgrades, and dependency hygiene; expanded testing and documentation to boost reliability and onboarding. Note: A prototype for training TSP in preprocess was explored but reverted to preserve pipeline stability.
June 2025
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ibm-granite/granite-tsfm. Delivered a new Time Series Pulse Classification Notebook (TSPulse) with end-to-end preprocessing, model configuration, fine-tuning workflow, and evaluation, achieving reported perfect accuracy on the test dataset. Fixed CUDA device handling in the learning rate finder utility to improve robustness across environments. These contributions enhance reproducibility, reliability, and business value by enabling faster experimentation and more trustworthy model evaluation.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ibm-granite/granite-tsfm. Delivered a new Time Series Pulse Classification Notebook (TSPulse) with end-to-end preprocessing, model configuration, fine-tuning workflow, and evaluation, achieving reported perfect accuracy on the test dataset. Fixed CUDA device handling in the learning rate finder utility to improve robustness across environments. These contributions enhance reproducibility, reliability, and business value by enabling faster experimentation and more trustworthy model evaluation.
Activity
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Quality Metrics
Correctness89.6%
Maintainability90.2%
Architecture86.0%
Performance81.2%
AI Usage21.6%
Skills & Technologies
Programming Languages
BashCSVJSONJupyter NotebookMarkdownPythonShellText
Technical Skills
ClassificationCode FormattingCode RefactoringCross-ValidationData AnalysisData EngineeringData ImputationData LoadingData PreprocessingData ScienceData VisualizationDataFramesDebuggingDeep LearningDependency Management
Repositories Contributed To
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