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Goals:

  • Provide the other side of jupyter so users know what to look out for.

Some people hate Jupyter!

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But why?

  • If you only learn to program in Jupyter notebooks, it’s possible that you’ll develop bad coding practices.

  • It’s somewhat counterintuitive to object oriented programming and can discourage OOP practices.

    • As part of that, it can discourage modularity.

  • Most jupyter notebooks and their output are not easily reproducible. Even if you don’t have cells that use randomization, if the original creator only ran some cell a few times and then just kept the state without running it in subsequent runs, the next person who runs it won’t be able to reproduce it running it from scratch.

    • You may run a notebook always skipping a cell, while the next person to run it doesn’t skip that cell. You’ll end up with different output.

    • Cells executed in different orders give you different output. You can override the linear run of cells in jupyter.

There are great things about Jupyter

  1. Encourages well documented/commented code

  2. Great visualization

It’s wasn’t made to be used on an HPC

In many ways, HPC computations and Jupyter notebooks don’t really go together naturally. Their use cases and original intentions are very different.

To work around this, tools can be used.

  • ipyparallel

  • dask

  • spark

But in some cases they end up being more of a “workaround” to make things that just don’t work together, work at the same time.

Next Steps

Previous

Dive into Jupyter Notebooks

Workshop Home 

Jupyter with OnDemand

Use the following link to provide feedback on this training: https://forms.gle/qBBwXpKeTNqSR5516 or use the QR code below.

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