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.
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 by submitting a pull request to `docs/using/recipes.md`. The sections below capture this knowledge.
## Add RISE
## Using `pip install` in a Child Docker image
@pdonorio said:
Create a new Dockerfile like the one shown below.
> 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.
```dockerfile
# Start from a core stack version
FROM jupyter/datascience-notebook:9f9e5ca8fe5a
# Install in the default python3 environment
RUN pip install'ggplot==0.6.8'
```
> I like it a lot, and find my self often adding this feature on top of your official images.
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
Ref: [https://github.com/jupyter/docker-stacks/issues/43](https://github.com/jupyter/docker-stacks/issues/43), updated 2018-04-22 to use `conda`
# 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.)
- 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
USER $NB_USER
```
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.
If you'd like to use packages from [spark-packages.org](https://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.
JupyterLab is preinstalled as a notebook extension starting in tag [c33a7dc0eece](https://github.com/jupyter/docker-stacks/wiki/Docker-build-history).
[RISE](https://github.com/damianavila/RISE) allows via extension to create live slideshows of your notebooks, with no conversion, adding javascript Reveal.js:
```dockerfile
# Start from a core stack version
FROM jupyter/datascience-notebook:9f9e5ca8fe5a
# Install in the default python3 environment
RUN pip install'ggplot==0.6.8'
```
# Add Live slideshows with RISE
RUN conda install -c damianavila82 rise
```
Credit: [Paolo D.](https://github.com/pdonorio) based on [docker-stacks/issues/43](https://github.com/jupyter/docker-stacks/issues/43)
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
## Use with JupyterHub's dockerspawner
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.
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)
> How does this [docker-stacks] work with dockerspawner?
1. install the jupyterhub-singleuser script (for the right Python)
2. change the command to launch the single-user server
## Use xgboost
Swapping out the `FROM` line in the `jupyterhub/singleuser` Dockerfile should be enough for most cases.
You need to install conda's gcc for Python xgboost to work properly. Otherwise, you'll get an exception about libgomp.so.1 missing GOMP_4.0.
Credit: [Justin Tyberg](https://github.com/jtyberg), [quanghoc](https://github.com/quanghoc), and [Min RK](https://github.com/minrk) based on [docker-stacks/issues/124](https://github.com/jupyter/docker-stacks/issues/124) and [docker-stacks/pull/185](https://github.com/jupyter/docker-stacks/pull/185)
```
%%bash
conda install -y gcc
pip install xgboost
### Containers with a specific version of JupyterHub
import xgboost
To use a specific version of JupyterHub, the version of `jupyterhub` in your image should match the version in the Hub itself.
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)
JupyterLab is preinstalled as a notebook extension starting in tag [c33a7dc0eece](https://github.com/jupyter/docker-stacks/wiki/Docker-build-history).
You can try jupyterlab using a command like `docker run -it --rm -p 8888:8888 jupyter/datascience-notebook start.sh jupyter lab`
If you'd like to use packages from [spark-packages.org](https://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.
## Use jupyter/all-spark-notebooks with an existing Spark/YARN cluster
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.)
