It is possible to customize the software environment of a project. There are various ways to do this, which are described below.
Within a Project#
Users are able to install their own software packages in their projects. A project is essentially a full Linux “user” environment, without elevated privileges. This means all the usual ways to install software as a user are available, e.g. for Python:
pip install --user --upgrade [mypackage]
for R Software:
or for GNU Autotools based packages:
./configure --prefix=$HOME/.local make make install
CoCalc Doc: Installing Python Packages
See Projects Software about how to get read/write access to the global
This is quite powerful, because it allows you to install software packages globally, which are then available to all projects.
Useful detail: if a file
/ext/.bashrc exists, it is sourced by
all projects via their local
~/.bashrc file. This means it is
possible to extend the path, configure aliases, etc. right there.
If some users want to opt out for a project, they just have to
comment or delete this from the bottom of their local
Custom Jupyter Kernels#
It is possible to globally deploy customized Jupyter Kernels.
Each sub-directory of
/ext/jupyter/kernels/ could hold your own kernels,
/ext mountpoint is where the globally shared read-only
filesystem is mounted in all projects (see Projects Software).
This works, because by default
$JUPYTER_PATH is configured and points to that jupyter directory.
Globally installed kernels with the same directory name can be overwritten,
because that path takes precedence – e.g.
To check if a kernel is available:
Open a terminal in a project and run
jupyter kernelspec list.
Try to start it via
jupyter console --kernel=[kernelname].
For a Python kernel, we suggest to add these parameters to the
argv array in the
"--HistoryManager.enabled=False": there is no need to record the history in a local database. In particular, if you’re on an NFS file-system, the underlying Sqlite database could cause problems in the form of “database is locked” errors, preventing the kernel from starting.
"--matplotlib=inline": to automatically load matplotlib
The entire project image can be built from a Dockerfile by you.
This is the most flexible way to customize the software environment.
Look into the
./project directory for more details.
Once your built project image is on your own registry,
configure your CoCalc deployment to make it available to your users.