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If we select the default Python 3 (ipykernel), we are presented with the file explorer showing our home directory as it’s rooted location. This means we can’t go up any further in system’s directory structure.

  • With our local root location for the notebook set to our $HOME, we are unable to see our /project and /gscratch directories on the cluster.

files.png

  • To expose these folders to the jupyter environment, create a symbolic link (aka shortcut) within our /home.

  • Instructions for creating a symbolic link may be found here or expanded in the cell to the right

Expand
titleSteps to create a symbolic link

  1. Open an ssh connection to the HPC cluster with:
    ssh your_username@clustername.arcc.uwyo.edu
    or open a shell through OnDemand:

  2. In the shell/terminal interface, create a symbolic link to your project (replacing project_name with the name of your project) with:

[~] ln -s /project/project_name/ project

  1. In the shell/terminal interface, create a symbolic link to your gscratch (replacing username with the your username on the HPC) with:

[~] ln -s /gscratch/username/ gscratch

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Or, lets get clever:

Alternatively, we can simplify things by create a symbolic link from within our notebook using ! functionality (if we’re running an ipython kernel):

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Can we get outside of home now?

We can see new links to our external directories:

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And now we can get to them:

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What Packages are Available in our Kernel?

In our notebook, we can see which modules are available by opening a new cell with the + button.

In our cell box, set as “code” use the python import command, followed by a space, then hit tab to get a list of options.

Hitting tab after import runs autocomplete options for the import command. This list of options has populated all modules available to us in our jupyter notebook:

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New Cell in our Notebook

Since we appear to have a large number of packages available in this environment, we’ll import one we expect to be there.

In our bottom-most cell, add to the import command by typing an import for a common package used in mathematic and multi dimensional matrix computations - numpy.

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