Category: SageMath


The Register Function and Reductions of Binary Trees and Lattice Paths — AofA’16

By behackl,

On July 8 I gave a contributed talk on “The Register Function and Reductions of Binary Trees and Lattice Paths” at this years 27th edition of the International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA’16), which took place inΒ KrakΓ³w, Poland.

My Slides can be found here.

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Jupyterhub with SageMath kernel

By behackl,

Recently I made some efforts to get a Jupyterhub instance with a SageMath kernel up and running on one of my servers — with success! πŸ™‚ Actually, the installation is really not that difficult. Nevertheless, I want to write a few lines describing the troubles I ran into and my solutions.

Installing Dependencies

The dependencies of Jupyterhub are well-explained on their github page, and their installation should not result in big trouble. However, as my server runs CentOS 7.2, I had to compile a recent Python version by myself:

Then, in order to find the library, you have to add the line /usr/local/lib to /etc/ld.so.conf.d/local.conf and reload the config by calling ldconfig. These steps (usually) have to be completed as root. Afterwards, Python 3.5.1 should be installed (you can check with python3.5 -V or pip3.5 -V).

The goal of these steps was to obtain a useable installation of pip.

The installation of the second dependency, the node package manager “npm” was straightforward:

Installation of Jupyterhub and linking the SageMath kernel

The next part is really easy: Jupyterhub (together with the notebook) can be installed with the help of pip just by calling

The available kernels then can be listed with jupyter-kernelspec:

In order to add a SageMath-kernel to your installation, you first need a working Sage install. For example, I’ve installed sage-6.10 in the /opt-directory, so that I can run Sage by calling

Then, all that needs to be done is linking the kernel into a directory, where Jupyter is aware of them:

After linking the kernel, jupyter-kernelspec list should already list the new kernel like this:

Configuring the Webserver

In my case, I configured Jupyterhub to run behind a reverse proxy, powered by an Apache web server, such that the service can be reached from a subdomain like https://jupyter.example.com.

First, note that Jupyterhub can be started just by using the command jupyterhub. With jupyterhub --port 98765, you can run the serivce at localhost:98765. The complicated part with the Apache configuration is that you also have to take care of the websockets — otherwise the kernels are not able to connect. The following snippet comes from my Apache configuration:

And finally, in order to have Jupyterhub in a systemd-setting, I’ve put the following into /usr/lib/systemd/system/jupyterhub.service:

… and that’s it! The service can then be started by reloading the configuration via sudo systemctl daemon-reload and sudo systemctl start jupyterhub.service.