installation-instructions.md

@@ -76,7 +76,7 @@ and then run the container with
 docker run -ti -u user -w /home/user --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix tiagopeixoto/graph-tool ipython
 ```
 
-## Jupyter notebooks
+### Jupyter notebooks
 
 To run jupyter notebooks from inside the docker image, you need to forward the necessary ports to the container, so that your native browser can connect to it at http://localhost:8888/. You need first to start an interactive shell session
 ```bash
@@ -90,6 +90,18 @@ and then start the notebook server
 
 (MacOS and Windows users still need to bind the above ports in the VM, as described [here](https://stackoverflow.com/questions/33636925/how-do-i-start-tensorflow-docker-jupyter-notebook))
 
+## Development snapshots
+
+Continuous snapshots of the most current development version of `graph-tool` are available as the [graph-tool-git](https://hub.docker.com/r/tiagopeixoto/graph-tool-git) docker repository. This repository is updated automatically after every commit is made to the upstream git repository.
+
+The development version can be installed simply by running:
+```bash
+docker pull tiagopeixoto/graph-tool-git
+```
+And following the same usage instructions as above.
+
+**WARNING:** The development version may contain bugs! Prefer the stable releases whenever possible.
+
 # Native installation
 
 Python modules are usually very easy to install, typically requiring nothing more that `pip install <package>` for basically any operating system. For `graph-tool`, however, the situation is different. This is because, in reality, `graph-tool` is a C++ library wrapped in Python, and it has many C++ dependencies such as [Boost](http://www.boost.org/), [CGAL](http://www.cgal.org/) and [expat](http://expat.sourceforge.net/), which are not installable via Python-only package management systems such as [pip](http://www.pip-installer.org/). Because the module lives between the C++ and Python worlds, its installation is done more like a C++ library rather than a typical python module. This means it inherits some of the complexities common of the C++ world that some Python users do not expect.