Users sometimes share interesting ways of using the Jupyter Docker Stacks. We encourage users to [contribute these recipes](../contributing/recipes.html) to the documentation in case they prove useful to other members of the community. The sections below capture this knowledge.
## Add RISE
@pdonorio said:
> There is a great repo called [RISE](https://github.com/damianavila/RISE) which allow via extension to create live slideshows of your notebooks, with no conversion, adding javascript Reveal.js.
> I like it a lot, and find my self often adding this feature on top of your official images.
```
# Add Live slideshows with RISE
RUN conda install -c damianavila82 rise
```
Ref: [https://github.com/jupyter/docker-stacks/issues/43](https://github.com/jupyter/docker-stacks/issues/43), updated 2018-04-22 to use `conda`
## Running behind a nginx proxy
Sometimes it is useful to run the Jupyter instance behind a nginx proxy, for instance:
- you would prefer to access the notebook at a server URL with a path (`https://example.com/jupyter`) rather than a port (`https://example.com:8888`)
- you may have many different services in addition to Jupyter running on the same server, and want to nginx to help improve server performance in manage the connections
Here is a [quick example NGINX configuration](https://gist.github.com/cboettig/8643341bd3c93b62b5c2) to get started. You'll need a server, a `.crt` and `.key` file for your server, and `docker` & `docker-compose` installed. Then just download the files at that gist and run `docker-compose up -d` to test it out. Customize the `nginx.conf` file to set the desired paths and add other services.
## Using spark-packages.org
If you'd like to use packages from spark-packages.org, see [https://gist.github.com/parente/c95fdaba5a9a066efaab](https://gist.github.com/parente/c95fdaba5a9a066efaab) for an example of how to specify the package identifier in the environment before creating a SparkContext.
See the README for the simple automation here [https://github.com/jupyter/docker-stacks/tree/master/examples/make-deploy](https://github.com/jupyter/docker-stacks/tree/master/examples/make-deploy) which includes steps for requesting and renewing a Let's Encrypt certificate.
Create a new Dockerfile like the one shown in this gist: [https://gist.github.com/parente/0d735d93cb81a582d635](https://gist.github.com/parente/0d735d93cb81a582d635). Switch the base stack image to whichever you please (e.g., `FROM jupyter/datascience-notebook`, `FROM jupyter/pyspark-notebook`).
If you are mounting a host directory as `/home/jovyan/work` in your container and you receive permission errors or connection errors when you create a notebook, be sure that the `jovyan` user (UID=1000 by default) has read/write access to the directory on the host. Alternatively, specify the UID of the `jovyan` user on container startup using the `-e NB_UID` option described in the [Common Features, Docker Options section](../using/common.html#Docker-Options)
Python 2.x was removed from all images on August 10th, 2017, starting in tag `cc9feab481f7`. You can add a Python 2.x environment by defining your own Dockerfile inheriting from one of the images like so:
```
# Choose your desired base image
FROM jupyter/scipy-notebook:latest
# Create a Python 2.x environment using conda including at least the ipython kernel
# and the kernda utility. Add any additional packages you want available for use
# in a Python 2 notebook to the first line here (e.g., pandas, matplotlib, etc.)
