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Encourages well documented/commented code
Great visualization
Provides a good mechanism for users explain their workflow and processes to others
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It’s wasn’t
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originally intended to be used on an HPC
In many ways, HPC computations and Jupyter notebooks don’t really go together naturallysuit each other’s strengths. Their use cases and original intentions are very different.
To work around this, tools can be used.
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. Jupyter Notebooks can be powerful development and collaboration tools, but they often aren’t suitable for long-running, computationally intensive workflows.
You can however use them together, and tools are available if you want to do this:
ipython parallel
dask
spark
But in In some cases they the tools end up being more of a “workaround” to make things that just don’t work together, work at the same timeand don’t really allow your computation to be run as one job inside the notebook. Instead what you may have is two separate jobs running simultaneously with information communicated between them.
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Next Steps
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