ibm-granite/granite-tsfm
Oct 2024 – Dec 2024
3 Months active
Languages Used
MarkdownPythonShellJupyter NotebookYAMLTOML
Technical Skills
BenchmarkingConfiguration ManagementData AnalysisData VisualizationHugging Face TransformersMachine Learning
Arindam Jati
PROFILE
Arindam Jati
Arindam Jati developed and enhanced the TinyTimeMixer benchmarking and forecasting workflows in the ibm-granite/granite-tsfm repository, focusing on robust model integration and maintainability. Over three months, Arindam built a comprehensive benchmarking framework, centralized model loading with improved configuration handling, and integrated TinyTimeMixer with GluonTS for end-to-end forecasting. Using Python, PyTorch, and Hugging Face Transformers, Arindam refactored code for clarity, expanded test coverage, and improved error handling to support multiple model variants and data constraints. The work addressed onboarding, reproducibility, and reliability, resulting in a cleaner codebase and streamlined workflows for time series analysis and model evaluation.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
21Total
Bugs
1
Commits
21
Features
8
Lines of code
6,350
Activity Months3
Your Network
12 people
Work History
December 2024
8 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for ibm-granite/granite-tsfm focused on delivering end-to-end forecast workflow improvements, strengthening model loading under variable data constraints, and cleaning the codebase for long-term maintainability. The work enhances reliability, accelerates production-ready model access, and reduces tech debt while expanding the feature set.
8 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for ibm-granite/granite-tsfm focused on delivering end-to-end forecast workflow improvements, strengthening model loading under variable data constraints, and cleaning the codebase for long-term maintainability. The work enhances reliability, accelerates production-ready model access, and reduces tech debt while expanding the feature set.
December 2024
November 2024
8 Commits • 3 Features
Nov 1, 2024Month: 2024-11 Summary: In November, the granite-tsfm work focused on consolidating model loading, improving benchmarking workflow onboarding, and tightening notebook maintainability to boost reliability and business value. The team delivered a centralized get_model pathway across notebooks, enhanced documentation for benchmarking data access, and refactored notebook code for readability, while addressing a critical bug to ensure robust variant support and reproducibility.
November 2024
8 Commits • 3 Features
Nov 1, 2024Month: 2024-11 Summary: In November, the granite-tsfm work focused on consolidating model loading, improving benchmarking workflow onboarding, and tightening notebook maintainability to boost reliability and business value. The team delivered a centralized get_model pathway across notebooks, enhanced documentation for benchmarking data access, and refactored notebook code for readability, while addressing a critical bug to ensure robust variant support and reproducibility.
October 2024
5 Commits • 2 Features
Oct 1, 2024Month 2024-10: Delivered a robust TinyTimeMixer benchmarking framework with end-to-end scripts, CSV reporting across configurations, and a README; enhanced the CLI to accept a data root path and improved result sorting. Added GetTTM model loading tests to ensure reliable save/load and configuration handling (context length, prediction length). Fixed visualization plotting for long-horizon predictions, addressing correctness when the plot context exceeds available past values and introduced new configuration options for prefix tuning and specifying a Hugging Face model path. These changes improve benchmarking reliability, model evaluation accuracy, and onboarding for new models.
5 Commits • 2 Features
Oct 1, 2024Month 2024-10: Delivered a robust TinyTimeMixer benchmarking framework with end-to-end scripts, CSV reporting across configurations, and a README; enhanced the CLI to accept a data root path and improved result sorting. Added GetTTM model loading tests to ensure reliable save/load and configuration handling (context length, prediction length). Fixed visualization plotting for long-horizon predictions, addressing correctness when the plot context exceeds available past values and introduced new configuration options for prefix tuning and specifying a Hugging Face model path. These changes improve benchmarking reliability, model evaluation accuracy, and onboarding for new models.
October 2024
Activity
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Quality Metrics
Correctness91.0%
Maintainability91.0%
Architecture88.0%
Performance83.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonShellTOMLYAML
Technical Skills
BenchmarkingBug FixingCode FormattingCode OrganizationCode RefactoringConfiguration ManagementData AnalysisData PreprocessingData ValidationData VisualizationDeep LearningDependency ManagementDocumentationError HandlingGluonTS
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