JupyterHub @ Aether¶

Aether offers its users the interactive, web-based computing environment Jupyter.

After logging in, Aether will start a Jupyter Notebook for the user on one of its compute nodes.

Access¶

Users can access Aether via the web-based Jupyter environment. The login page is available at https://aether.uni-bremen.de/jupyter . Access is restricted to the University of Bremen network; for remote access, you can use the ZfN VPN service.

Reserved resources¶

The Jupyter process has access to 4 CPU cores and 18 GB RAM. It has a reserved runtime of 72 hours.

Warning

The process will terminate after 72 hours runtime without further warning; all unsaved work will be lost!

Using JupyterLab¶

For the time being, we direct the interested user to the official JupyterLab documentation.

Available kernels¶

On the JupyterLab Launcher page, you will see a list of available kernels.

Default kernels¶

The current and previous LAMOS environments (as of writing, lamos_2020.1, lamos_2019.1, and lamos_2018.1) will always be available in your JupyterHub. You don’t need to anything about this, we’ll take care of it.

Additional system-wide kernels¶

Additionally, we offer some more kernels which you can make available in your JupyterHub. We don’t make them visible by default in order to not clutter your JupyterHub launcher. Currently, the following additional kernels are available:

Kernel name	kernelspec directory (inside `/home/conda/kernels`)
Bash	`bash`
R 3.6.1 (2019)	`ir_2019`
Julia 1.2.0 (2019)	`julia_2019`
R (2016)	`ir_2016`
LAMOS 2020.2 Beta (!!)	`lamos_2020.2b`
LAMOS 2019.2 Beta	`lamos_2019.2b`
LAMOS v0.1 (Python 2)	`python2-lpy0.1`
LAMOS v0.1 (Python 3)	`python3-lpy0.1`
LAMOS v0.2 (Python 2)	`python2-lpy0.2`
LAMOS v0.2 (Python 3)	`python3-lpy0.2`

When you log in to JupyterHub, it searches for kernels in $HOME/.local/share/jupyter/kernels. This means that in order to make one of these optional kernels available in your JupyterHub, you simply need to create a symlink, e.g.,

mkdir -p ~/.local/share/jupyter/kernels
cd ~/.local/share/jupyter/kernels
ln -s /home/conda/kernels/lamos_2019.2b

If at some point in the future, you don’t want to have that kernel available any more, it is enough to remove that symlink,

rm ~/.local/share/jupyter/kernels/lamos_2019.2b

In order for JupyterHub to “see” these changes, make sure to restart your JupyterHub server (not just one kernel).

Private kernels¶

If you have set up Private conda environments you might want to make these available for use in Jupyter as well.

In order to use your own environment within JupyterHub, you have to do the following steps (thanks to this blog post for laying out the necessary steps).

You have to register the conda environment with JupyterHub:

$ conda activate my_env
$ jupyter kernel install --user --name my_env

This will create a file $HOME/.local/share/jupyter/kernels/my_env/kernel.json, which looks like this:

{
 "display_name": "my_env",
 "language": "python",
 "argv": [
  "/home/YOURUSERNAME/.conda/envs/my_env/bin/python",
  "-m",
  "ipykernel_launcher",
  "-f",
  "{connection_file}"
 ]
}

You can modify the display name (that’s what is displayed on the JupyterHub dashboard) by changing the display_name from my_env to whatever you prefer.