Labels

• Component: Data

  Anything like data collection, cleaning and preparation for training or distribution.
• Component: DevOps/MLOps

  Automation for CI/CD, and managing Machine Learning lifecycles at scale.
• Component: Documentation

  Documentation for the package, including the README and PyPi page.
• Component: Evaluation

  For the benchmarking, metrics, and evaluation of models and features.
• Component: Explainability

  Anything related ot the explainable AI related to the package.
• Component: Models

  Model design, training, evaluation and deployment.
• Component: Paper

  For drafting text, managing citations, or creating figures for a publishable manuscript.
• Component: Post-processing

  Analysis, External Notebooks, Downstream usage.
• Component: Pre-processing

  Covers external HPC scripts, data cleaning pipelines, and training pipelines.
• Priority: 0 (Critical)

  Drop everything! The public package is broken; a core feature is broken. No other work until fixed.
• Priority: 1 (High)

  "Must have for next release." Crucial features or bugs that impede major functionality.
• Priority: 2 (Medium)

  "Standard work." Regular features, non-critical bugs, and improvements.
• Priority: 3 (Low)

  "Nice to have." Minor tweaks, changes, or features for the distant future.
• Type: Bug

  Something is broken.
• Type: Feature

  New functionality added to the package.
• Type: Maintenance

  Updates the user doesn't see but developer must do.
• Type: Proposal

  A research idea that is serious enough to track but needs definition before coding starts.
• Type: Task

  Running jobs, training models, manual work outside of the package.
• Type: Writing

  Writing and analysis of text.