Selecting the Julia-Jupyter application
Now, we need to select the resources for running Julia-Jupyter. First, we must select a project for billing and partition for computing resources.
Next, we must set the desired computing resources: CPU cores, memory, local disk, and time.
Finally, we must select the Jupyter type. We recommend using Jupyter lab, but
the classic notebook is also available. The working directory sets the root
directory for Jupyter. The Julia depot directory sets the location for package
installations, compiled files, and other Julia depots. If you plan to install
large amounts of Julia packages, we recommend using
instead of the
$HOME directory as it could run out of quota. For example,
Plots.jl installs over 10k files and is quite large.
Starting Julia kernel
Inside the Jupyter session, we can start notebooks with the desired Julia version. Starting a kernel loads the corresponding Julia module and starts the notebook. We launch the Julia kernel using IJulia.jl. Note that the Jupyter installation for Julia is separate from the Jupyter installation for Python and is not intended for other use.