Yes, we currently provide each user container with a maximum of 64 GiB RAM, 32 CPU cores and a 4 GiB of persistent storage space for your home directory.
Students who wish to use RWTHjupyter outside of their courses should use one of the generic kernel profiles. These generic profiles can be customized by installation of additional packages via pip and conda. By default, added packages are not persistent and only available until the next spawn of your Jupyter container. As a workaround, packages can be installed in your home directory:
pip install --user pandas
You can also add this line into a Jupyter Notebook cell by prefixing it with an exclamation mark:
!pip install --user pandas
It is currently not possible to load custom conda environemnts.
We use a Jupyter extension called nbgitpuller to sync Jupyter Notebooks. nbgitpuller uses an "automatic merging behaviour" to sync changes between your local home directory and the upstream Git repo. Please consult the nbgitpuller documentation for details about this merging behaviour.
