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Upon connecting, you are presented the jupyter dashboard which serves as your home page for jupyter notebook. The Jupyter Notebook screen is rather simple with 3 tabs:

  • Files: (Default selected) Interactive view of the portion of the filesystem accessible by the user, rooted by the directory in which the notebook was launched from.

  • Running: Displays currently running notebooks known to the server. (You can manage notebook kernels from here)

  • Clusters: Gives a summary of iPython Parallel clusters
    (More about this later)

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What are Kernels?

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Most users think of this as the “compiler” or programming language used when running code cells.
The Kernel empowers you to execute code in different programming languages like Python, R, or Julia or other languages and instantly view the outcomes within the notebook interface.

After opening Once you open a new notebook, you will be prompted to select a kernel

  • If you have never created a kernel to use, you will only see a list of default Jupyter kernels available on the cluster

  • You may check the box to start with the preferred kernel every time you open a notebook

Default Kernels on ARCC HPC Resources currently include:

  • Python Kernels

  • R Kernels (only on Beartooth)

HPC-wide kernels are titled by packages installed and available when launched

Users can also create user-defined kernels from conda environments (Covered in a subsequent module. See: Launching Jupyter Kernels from Conda Environments)

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