This is an attempt to standardize and streamline computational work
that combines SnapPy, SageMath, and friends via a custom Docker <http://www.docker.com> image that is based around Ubuntu 20.04 LTS
and the latest and greatest SageMath <http://sagemath.org>. It is
indirectly derived from the official sagemath/sagemath Docker
image, but includes SnapPy <http://bitbucket.org/t3m/snappy>, and
the Python interfaces to Regina <http://regina-normal.github.io/>,
and PHCPack <http://homepages.math.uic.edu/~jan/>_.
A container instance of this image starts a shell as the user sage
with all the environment variables set as if you had executed sage -sh.
This image takes a long time to build, even on a fast computer, since
it compiles SageMath from source, and weighs in at 5.2 GB (1.6
GB compressed). We recommend you use the posted image <http://hub.docker.com/r/computop/sage/>_ on DockerHub via::
docker pull computop/sage docker run -it computop/sage
Here, the first command downloads the image from DockerHub and the
second starts a container that is running said image. Moving files
between the container and the rest of the world can be done using
scp or having the container mount a local directory as in this example::
docker run -it --mount type=bind,source="$HOME/Dropbox/linux_share",target=/home/sage/linux_share computop/sage
where the directory linux_share inside the user's Dropbox folder is
shared.
If you want to use Sage's Jupyter notebook interface, start the container via::
docker run -it -p 127.0.0.1:8888:8888 computop/sage
and in said container type::
sage --notebook
and then point your computer's web browser to
http://localhost:8888. If you want to use the beta JupyterLab <https://jupyterlab.readthedocs.io/en/stable/>_, which will
eventually superseded Jupyter, you can instead do::
sage --notebook jupyterlab
In JupyterLab, you have access not just to notebooks but also Python consoles and shell terminal windows.
The images for SageMath 8.9 and older use Python 2 (and Ubuntu 16.04) and those for SageMath 9.0 and newer use Python 3 (and Ubuntu 18.04 or 20.04). You can always request Docker run a particular version of the image, for example if you want SageMath 8.9 do::
docker run -it computop/sage:8.9
A partial list of included software:
SageMath <http://sagemath.org>_SnapPy <http://snappy.computop.org>_Regina <http://regina-normal.github.io/>_PHCPack <http://homepages.math.uic.edu/~jan/>_pandas <http://pandas.pydata.org/>_pe <http://bitbucket.org/t3m/pe>_gridlink <http://bitbucket.org/t3m/gridlink>_flipper <http://flipper.readthedocs.io> and curver <http://curver.readthedocs.io>heegaard <http://bitbucket.org/t3m/heegaard>_pygraphviz <http://pygraphviz.github.io/>_snap <http://snap-pari.sourceforge.net>_We no longer include HIKMOT <http://www.oishi.info.waseda.ac.jp/~takayasu/hikmot/>_ as it is not
compatible with Python 3.
To build from scratch, which takes 1.3 hours using all 8 cores on a Mac Pro::
docker build --tag=computop/sage sage
If compiling Sage fails, make sure the Docker application is configured so that it can use at least 4GB of memory.
Put out on DockerHub::
docker push computop/sage