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A short follow-up. If you have a Landlab developer install, you can create a separate Jupyter kernel for it. Say, for example, that you've followed the instructions for a developer install and placed it in a conda environment named
Then install the ipykernel package:
Then make the kernel:
After a few moments, this kernel should become visible in the JupyterLab Launcher window, and you'll be able to select it when running a notebook. |
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How can we run notebooks that use Landlab on the Blanca JupyterHub?
We need to:
This required a little more work than I expected.
Start by logging in to Blanca and starting a JupyterHub. Then, from the JupyterLab Launcher, start a terminal.
A conda environment that contains Landlab takes up about 5 GB of disk space. I found that I had to modify my conda settings so that my Landlab environment would be installed in my project directory instead of my home directory. I wrote up a brief explanation of how to do this.
Create a conda environment that has Landlab and also ipykernel.
Once the environment has been created, activate it:
Then make a kernel:
Now, when you open a new Launcher in JupyterLab, you'll see the Landlab kernel listed. Also, when you open a notebook, you'll see the Landlab kernel listed in the kernel switcher.
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