changed 7 years ago
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Usecases for Jupyter kernels and notebooks

Use cases

  • Reproducible logbooks (aka lab notebooks) documenting a computer experimentation.
    Ideally eventualy published along the research paper.
  • Reproducible live slides example
  • Interactive software demos
  • Interactive documentation (either as notebooks or live html page with ThebeLab)
  • Teaching documents, possibly with auto-grading,
  • Ease of software deployment (via the cloud)
  • Widgets:
  • "Lab notebook" for conducting research; keep notes, computations

Benefits of Jupyter

  • reducing entry barriers for new users by:
    • providing "standard" interface which they may know from using other Jupyter kernels (Python, R, etc.)
    • allowing them to easily organise their code and documentation into Jupyter notebooks
  • ability to use full GAP installation on Windows by connecting to a Docker container (or a remote GAP?)
  • Using JupyterKernel as a backend for other IDE's
  • potentially using an equivalent of ipyparallel for parallel distributed calculations
  • Benefits for reproducible research

Inconveniences of using Jupyter

Missing features?

  • input/output/error similar/identical to the GAP command line interface
  • A notebook extension to force edition at the end of the notebook (it seems that what's exist already is changing the colour of cells below as soon as a cell in the middle is edited - this is already fairly good)
  • break loops in Jupyter? How to do interactive debugging?

Further directions in Jupyter developments from which we may benefit

  • JupyterLab (a kind of an IDE)

Technical questions sidetracking discussion - list them here to return to them later


Guides to Jupyter

  • "Quick start" guide / best practices

References

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