Contributed Recipes

Users sometimes share interesting ways of using the Jupyter Docker Stacks. We encourage users to contribute these recipes to the documentation in case they prove useful to other members of the community by submitting a pull request to docs/using/ The sections below capture this knowledge.

Using pip install or conda install in a Child Docker image

Create a new Dockerfile like the one shown below.

# 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.

docker build --rm -t jupyter/my-datascience-notebook .

To use a requirements.txt file, first create your requirements.txt file with the listing of packages desired. Next, create a new Dockerfile like the one shown below.

# Start from a core stack version
FROM jupyter/datascience-notebook:9f9e5ca8fe5a
# Install from requirements.txt file
COPY requirements.txt /tmp/
RUN pip install --requirement /tmp/requirements.txt && \
    fix-permissions $CONDA_DIR && \
    fix-permissions /home/$NB_USER

For conda, the Dockerfile is similar:

# Start from a core stack version
FROM jupyter/datascience-notebook:9f9e5ca8fe5a
# Install from requirements.txt file
COPY requirements.txt /tmp/
RUN conda install --yes --file /tmp/requirements.txt && \
    fix-permissions $CONDA_DIR && \
    fix-permissions /home/$NB_USER

Ref: docker-stacks/commit/79169618d571506304934a7b29039085e77db78c

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



Run JupyterLab

JupyterLab is preinstalled as a notebook extension starting in tag c33a7dc0eece.

Run jupyterlab using a command such as docker run -it --rm -p 8888:8888 jupyter/datascience-notebook jupyter lab

Let’s Encrypt a Notebook server

See the README for the simple automation here which includes steps for requesting and renewing a Let’s Encrypt certificate.


Slideshows with Jupyter and RISE

RISE allows via extension to create live slideshows of your notebooks, with no conversion, adding javascript Reveal.js:

# Add Live slideshows with RISE
RUN conda install -c damianavila82 rise

Credit: Paolo D. based on docker-stacks/issues/43


You need to install conda’s gcc for Python xgboost to work properly. Otherwise, you’ll get an exception about missing GOMP_4.0.

conda install -y gcc
pip install xgboost

import xgboost

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 ( rather than a port (
  • 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 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.

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



We also have contributed recipes for using JupyterHub.

Use JupyterHub’s dockerspawner

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.

Credit: Justin Tyberg, quanghoc, and Min RK based on docker-stacks/issues/124 and docker-stacks/pull/185

Containers with a specific version of JupyterHub

To use a specific version of JupyterHub, the version of jupyterhub in your image should match the version in the Hub itself.

FROM  jupyter/base-notebook:5ded1de07260
RUN pip install jupyterhub==0.8.0b1

Credit: MinRK



A few suggestions have been made regarding using Docker Stacks with spark.

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 ="s3://myBucket/myKey")


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"], {"": broker})



If you’d like to use packages from, see 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

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 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 && \
    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=" >> /usr/local/spark/conf/spark-defaults.conf  && \
    echo " -Dhdp.version=" >> /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/


# 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= --conf spark.hadoop.yarn.timeline-service.enabled=false"

Credit: britishbadger from docker-stacks/issues/369