diff --git a/docs/contributing/recipes.md b/docs/contributing/recipes.md
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-# Documented Recipes
\ No newline at end of file
+# New Recipes
diff --git a/docs/index.rst b/docs/index.rst
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--- a/docs/index.rst
+++ b/docs/index.rst
@@ -31,6 +31,7 @@ Table of Contents
    using/running
    using/common
    using/specifics
+   using/recipes
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/using/recipes.md b/docs/using/recipes.md
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+# Contributed Recipes
+
+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.
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/43](https://github.com/jupyter/docker-stacks/issues/43)
+
+## Let's Encrypt a Notebook server
+
+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.
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/78](https://github.com/jupyter/docker-stacks/issues/78)
+
+## Add Incubating Dashboard, Declarative Widget, Content Management Extensions
+
+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`).
+
+## Using `pip install` in a Child Docker image
+
+Create a new Dockerfile like the one shown below. 
+
+```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'
+```
+
+Then build a new image.
+
+```bash
+docker build --rm -t jupyter/my-datascience-notebook .
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/commit/79169618d571506304934a7b29039085e77db78c#commitcomment-15960081](https://github.com/jupyter/docker-stacks/commit/79169618d571506304934a7b29039085e77db78c#commitcomment-15960081)
+
+## Use with JupyterHub's dockerspawner
+
+@jtyberg contributed [https://github.com/jupyter/docker-stacks/pull/185](https://github.com/jupyter/docker-stacks/pull/185)
+
+Originally, @quanghoc asked:
+
+> How does this [docker-stacks] work with dockerspawner?
+
+@minrk replied:
+
+> ... in most cases for use with DockerSpawner, given any image that already has a notebook stack set up, you would only need to add:
+
+> 1. install the jupyterhub-singleuser script (for the right Python)
+> 2. change the command to launch the single-user server
+
+> Swapping out the `FROM` line in the `jupyterhub/singleuser` Dockerfile should be enough for most cases.
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/124](https://github.com/jupyter/docker-stacks/issues/124)
+
+## Use xgboost
+
+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.
+
+```
+%%bash
+conda install -y gcc
+pip install xgboost
+
+import xgboost
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/177](https://github.com/jupyter/docker-stacks/issues/177)
+
+## Using PySpark with AWS S3
+
+```
+import os
+os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages com.amazonaws:aws-java-sdk:1.10.34,org.apache.hadoop:hadoop-aws:2.6.0 pyspark-shell'
+
+import pyspark
+sc = pyspark.SparkContext("local[*]")
+
+from pyspark.sql import SQLContext
+sqlContext = SQLContext(sc)
+
+hadoopConf = sc._jsc.hadoopConfiguration()
+myAccessKey = input() 
+mySecretKey = input()
+hadoopConf.set("fs.s3.impl", "org.apache.hadoop.fs.s3native.NativeS3FileSystem")
+hadoopConf.set("fs.s3.awsAccessKeyId", myAccessKey)
+hadoopConf.set("fs.s3.awsSecretAccessKey", mySecretKey)
+
+df = sqlContext.read.parquet("s3://myBucket/myKey")
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/127](https://github.com/jupyter/docker-stacks/issues/127)
+
+## Using Local Spark JARs
+
+```
+import os
+os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars /home/jovyan/spark-streaming-kafka-assembly_2.10-1.6.1.jar pyspark-shell'
+import pyspark
+from pyspark.streaming.kafka import KafkaUtils
+from pyspark.streaming import StreamingContext
+sc = pyspark.SparkContext()
+ssc = StreamingContext(sc,1)
+broker = "<my_broker_ip>"
+directKafkaStream = KafkaUtils.createDirectStream(ssc, ["test1"], {"metadata.broker.list": broker})
+directKafkaStream.pprint()
+ssc.start()
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/154](https://github.com/jupyter/docker-stacks/issues/154)
+
+## Host volume mounts and notebook errors
+
+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)
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/199](https://github.com/jupyter/docker-stacks/issues/199)
+
+## Run JupyterLab
+
+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`
+
+## Use jupyter/all-spark-notebooks with an existing Spark/YARN cluster
+
+Courtesy of @britishbadger:
+
+```
+FROM jupyter/all-spark-notebook
+
+# Set env vars for pydoop
+ENV HADOOP_HOME /usr/local/hadoop-2.7.3
+ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64
+ENV HADOOP_CONF_HOME /usr/local/hadoop-2.7.3/etc/hadoop
+ENV HADOOP_CONF_DIR  /usr/local/hadoop-2.7.3/etc/hadoop
+
+USER root
+# Add proper open-jdk-8 not just the jre, needed for pydoop
+RUN echo 'deb http://cdn-fastly.deb.debian.org/debian jessie-backports main' > /etc/apt/sources.list.d/jessie-backports.list && \
+    apt-get -y update && \
+    apt-get install --no-install-recommends -t jessie-backports -y openjdk-8-jdk && \
+    rm /etc/apt/sources.list.d/jessie-backports.list && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/ && \
+# Add hadoop binaries
+    wget http://mirrors.ukfast.co.uk/sites/ftp.apache.org/hadoop/common/hadoop-2.7.3/hadoop-2.7.3.tar.gz && \
+    tar -xvf hadoop-2.7.3.tar.gz -C /usr/local && \
+    chown -R $NB_USER:users /usr/local/hadoop-2.7.3 && \
+    rm -f hadoop-2.7.3.tar.gz && \
+# Install os dependencies required for pydoop, pyhive
+    apt-get update && \
+    apt-get install --no-install-recommends -y build-essential python-dev libsasl2-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/* && \
+# Remove the example hadoop configs and replace
+# with those for our cluster.
+# Alternatively this could be mounted as a volume
+    rm -f /usr/local/hadoop-2.7.3/etc/hadoop/*
+
+# Download this from ambari / cloudera manager and copy here
+COPY example-hadoop-conf/ /usr/local/hadoop-2.7.3/etc/hadoop/
+
+# Spark-Submit doesn't work unless I set the following
+RUN echo "spark.driver.extraJavaOptions -Dhdp.version=2.5.3.0-37" >> /usr/local/spark/conf/spark-defaults.conf  && \
+    echo "spark.yarn.am.extraJavaOptions -Dhdp.version=2.5.3.0-37" >> /usr/local/spark/conf/spark-defaults.conf && \
+    echo "spark.master=yarn" >>  /usr/local/spark/conf/spark-defaults.conf && \
+    echo "spark.hadoop.yarn.timeline-service.enabled=false" >> /usr/local/spark/conf/spark-defaults.conf && \
+    chown -R $NB_USER:users /usr/local/spark/conf/spark-defaults.conf && \
+    # Create an alternative HADOOP_CONF_HOME so we can mount as a volume and repoint
+    # using ENV var if needed
+    mkdir -p /etc/hadoop/conf/ && \
+    chown $NB_USER:users /etc/hadoop/conf/
+
+USER $NB_USER
+
+# Install useful jupyter extensions and python libraries like :
+# - Dashboards
+# - PyDoop
+# - PyHive
+RUN pip install jupyter_dashboards faker && \
+    jupyter dashboards quick-setup --sys-prefix && \
+    pip2 install pyhive pydoop thrift sasl thrift_sasl faker
+
+USER root
+# Ensure we overwrite the kernel config so that toree connects to cluster
+RUN jupyter toree install --sys-prefix --spark_opts="--master yarn --deploy-mode client --driver-memory 512m  --executor-memory 512m  --executor-cores 1 --driver-java-options -Dhdp.version=2.5.3.0-37 --conf spark.hadoop.yarn.timeline-service.enabled=false"
+USER $NB_USER
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/369](https://github.com/jupyter/docker-stacks/issues/369)
+
+## Use containers with a specific version of JupyterHub
+
+The fix is to make sure that the same version of `jupyterhub` is installed in your image as the Hub itself.
+
+In general, this is enough:
+
+```
+FROM  jupyter/base-notebook:5ded1de07260
+RUN pip install jupyterhub==0.8.0b1
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/423#issuecomment-322767742](https://github.com/jupyter/docker-stacks/issues/423#issuecomment-322767742)
+
+## Add a Python 2.x environment
+
+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.)
+RUN conda create --quiet --yes -p $CONDA_DIR/envs/python2 python=2.7 ipython ipykernel kernda && \
+    conda clean -tipsy
+
+USER root
+
+# Create a global kernelspec in the image and modify it so that it properly activates
+# the python2 conda environment.
+RUN $CONDA_DIR/envs/python2/bin/python -m ipykernel install && \
+$CONDA_DIR/envs/python2/bin/kernda -o -y /usr/local/share/jupyter/kernels/python2/kernel.json
+
+USER $NB_USER
+```
+
+Ref: [https://github.com/jupyter/docker-stacks/issues/440](https://github.com/jupyter/docker-stacks/issues/440)
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