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 helpful to other community members by submitting a pull request to docs/using/recipes.md. The sections below capture this knowledge.

Using sudo within a container#

Password authentication is disabled for the NB_USER (e.g., jovyan). We made this choice to avoid distributing images with a weak default password that users ~might~ will forget to change before running a container on a publicly accessible host.

You can grant the within-container NB_USER passwordless sudo access by adding --user root and -e GRANT_SUDO=yes to your Docker command line or appropriate container orchestrator config.

For example:

docker run -it --rm \
    --user root \
    -e GRANT_SUDO=yes \
    jupyter/minimal-notebook

You should only enable sudo if you trust the user and/or if the container is running on an isolated host. See Docker security documentation for more information about running containers as root.

Using mamba install or pip 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:6b49f3337709
# Install in the default python3 environment
RUN pip install --quiet --no-cache-dir 'flake8==3.9.2' && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

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:6b49f3337709
# Install from requirements.txt file
COPY --chown=${NB_UID}:${NB_GID} requirements.txt /tmp/
RUN pip install --quiet --no-cache-dir --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:6b49f3337709
# Install from requirements.txt file
COPY --chown=${NB_UID}:${NB_GID} requirements.txt /tmp/
RUN mamba install --yes --file /tmp/requirements.txt && \
    mamba clean --all -f -y && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

Ref: docker-stacks/commit/79169618d571506304934a7b29039085e77db78c

Add a custom conda environment and Jupyter kernel#

The default version of Python that ships with the image may not be the version you want. The instructions below permit to add a conda environment with a different Python version and make it accessible to Jupyter.

# Choose your desired base image
FROM jupyter/minimal-notebook:latest

# name your environment and choose the python version
ARG conda_env=python37
ARG py_ver=3.7

# you can add additional libraries you want mamba to install by listing them below the first line and ending with "&& \"
RUN mamba create --quiet --yes -p "${CONDA_DIR}/envs/${conda_env}" python=${py_ver} ipython ipykernel && \
    mamba clean --all -f -y

# alternatively, you can comment out the lines above and uncomment those below
# if you'd prefer to use a YAML file present in the docker build context

# COPY --chown=${NB_UID}:${NB_GID} environment.yml "/home/${NB_USER}/tmp/"
# RUN cd "/home/${NB_USER}/tmp/" && \
#     mamba env create -p "${CONDA_DIR}/envs/${conda_env}" -f environment.yml && \
#     mamba clean --all -f -y

# create Python kernel and link it to jupyter
RUN "${CONDA_DIR}/envs/${conda_env}/bin/python" -m ipykernel install --user --name="${conda_env}" && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

# any additional pip installs can be added by uncommenting the following line
# RUN "${CONDA_DIR}/envs/${conda_env}/bin/pip" install --quiet --no-cache-dir

# if you want this environment to be the default one, uncomment the following line:
# RUN echo "conda activate ${conda_env}" >> "${HOME}/.bashrc"

Dask JupyterLab Extension#

Dask JupyterLab Extension provides a JupyterLab extension to manage Dask clusters, as well as embed Dask’s dashboard plots directly into JupyterLab panes. Create the Dockerfile as:

# Start from a core stack version
FROM jupyter/scipy-notebook:latest

# Install the Dask dashboard
RUN pip install --quiet --no-cache-dir dask-labextension && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

# Dask Scheduler & Bokeh ports
EXPOSE 8787
EXPOSE 8786

ENTRYPOINT ["jupyter", "lab", "--ip=0.0.0.0", "--allow-root"]

And build the image as:

docker build -t jupyter/scipy-dasklabextension:latest .

Once built, run using the command:

docker run -it --rm \
    -p 8888:8888 \
    -p 8787:8787 jupyter/scipy-dasklabextension:latest

Ref: https://github.com/jupyter/docker-stacks/issues/999

Let’s Encrypt a Notebook server#

See the README for a basic automation here 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

Slideshows with Jupyter and RISE#

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

# Add Live slideshows with RISE
RUN mamba install --quiet --yes -c damianavila82 rise && \
    mamba clean --all -f -y && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

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

xgboost#

You need to install conda-forge’s gcc for Python xgboost to work correctly. Otherwise, you’ll get an exception about libgomp.so.1 missing GOMP_4.0.

mamba install --quiet --yes gcc && \
    mamba clean --all -f -y && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

pip install --quiet --no-cache-dir xgboost && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

# run "import xgboost" in python

Running behind a nginx proxy#

Sometimes it is helpful to run the Jupyter instance behind a nginx proxy, for example:

  • 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 services in addition to Jupyter running on the same server, and want nginx to help improve server performance in managing 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 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

Ref: https://github.com/jupyter/docker-stacks/issues/199

Manpage installation#

Most containers, including our Ubuntu base image, ship without manpages installed to save space. You can use the following Dockerfile to inherit from one of our images to enable manpages:

# Choose your desired base image
ARG BASE_CONTAINER=jupyter/datascience-notebook:latest
FROM $BASE_CONTAINER

USER root

# `/etc/dpkg/dpkg.cfg.d/excludes` contains several `path-exclude`s, including man pages
# Remove it, then install man, install docs
RUN rm /etc/dpkg/dpkg.cfg.d/excludes && \
    apt-get update --yes && \
    dpkg -l | grep ^ii | cut -d' ' -f3 | xargs apt-get install --yes --no-install-recommends --reinstall man && \
    apt-get clean && rm -rf /var/lib/apt/lists/*

USER ${NB_UID}

Adding the documentation on top of the existing single-user image wastes a lot of space and requires reinstalling every system package. Which can take additional time and bandwidth; the datascience-notebook image has been shown to grow by almost 3GB when adding manpages in this way. Enabling manpages in the base Ubuntu layer prevents this container bloat. To achieve this, use the previous Dockerfile with the original ubuntu image (ubuntu:focal) as your base container:

ARG BASE_CONTAINER=ubuntu:focal

For Ubuntu 18.04 (bionic) and earlier, you may also require to a workaround for a mandb bug, which was fixed in mandb >= 2.8.6.1:

# https://git.savannah.gnu.org/cgit/man-db.git/commit/?id=8197d7824f814c5d4b992b4c8730b5b0f7ec589a
# https://launchpadlibrarian.net/435841763/man-db_2.8.5-2_2.8.6-1.diff.gz

RUN echo "MANPATH_MAP ${CONDA_DIR}/bin ${CONDA_DIR}/man" >> /etc/manpath.config && \
    echo "MANPATH_MAP ${CONDA_DIR}/bin ${CONDA_DIR}/share/man" >> /etc/manpath.config && \
    mandb

Be sure to check the current base image in base-notebook before building.

JupyterHub#

We also have contributed recipes for using JupyterHub.

Use JupyterHub’s dockerspawner#

In most cases for use with DockerSpawner, given an image that already has a notebook stack set up, you would only need to add:

  1. install the jupyterhub-singleuser script (for the correct Python version)

  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:6b49f3337709
RUN pip install --quiet --no-cache-dir jupyterhub==1.4.1 && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

Credit: MinRK

Ref: https://github.com/jupyter/docker-stacks/issues/177

Spark#

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

Using PySpark with AWS S3#

Using Spark session for hadoop 2.7.3

import os

# !ls /usr/local/spark/jars/hadoop* # to figure out what version of hadoop
os.environ[
    "PYSPARK_SUBMIT_ARGS"
] = '--packages "org.apache.hadoop:hadoop-aws:2.7.3" pyspark-shell'

import pyspark

myAccessKey = input()
mySecretKey = input()

spark = (
    pyspark.sql.SparkSession.builder.master("local[*]")
    .config("spark.hadoop.fs.s3a.access.key", myAccessKey)
    .config("spark.hadoop.fs.s3a.secret.key", mySecretKey)
    .getOrCreate()
)

df = spark.read.parquet("s3://myBucket/myKey")

Using Spark context for hadoop 2.6.0

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

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

Using spark-packages.org#

If you’d like to use packages from spark-packages.org, see 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

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 the jre only, needed for pydoop
RUN echo 'deb https://cdn-fastly.deb.debian.org/debian jessie-backports main' > /etc/apt/sources.list.d/jessie-backports.list && \
    apt-get update --yes && \
    apt-get install --yes --no-install-recommends -t jessie-backports 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 https://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 --yes && \
    apt-get install --yes --no-install-recommends 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_UID}

# Install useful jupyter extensions and python libraries like :
# - Dashboards
# - PyDoop
# - PyHive
RUN pip install --quiet --no-cache-dir jupyter_dashboards faker && \
    jupyter dashboards quick-setup --sys-prefix && \
    pip2 install --quiet --no-cache-dir pyhive pydoop thrift sasl thrift_sasl faker && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

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_UID}

Credit: britishbadger from docker-stacks/issues/369

Run Jupyter Notebook/Lab inside an already secured environment (i.e., with no token)#

(Adapted from issue 728)

The default security is very good. There are use cases, encouraged by containers, where the jupyter container and the system it runs within lie inside the security boundary. It is convenient to launch the server without a password or token in these use cases. In this case, you should use the start.sh script to launch the server with no token:

For JupyterLab:

docker run -it --rm \
    jupyter/base-notebook:6b49f3337709 \
    start.sh jupyter lab --LabApp.token=''

For jupyter classic:

docker run -it --rm \
    jupyter/base-notebook:6b49f3337709 \
    start.sh jupyter notebook --NotebookApp.token=''

Enable nbextension spellchecker for markdown (or any other nbextension)#

NB: this works for classic notebooks only

# Update with your base image of choice
FROM jupyter/minimal-notebook:latest

USER ${NB_UID}

RUN pip install --quiet --no-cache-dir jupyter_contrib_nbextensions && \
    jupyter contrib nbextension install --user && \
    # can modify or enable additional extensions here
    jupyter nbextension enable spellchecker/main --user && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

Ref: https://github.com/jupyter/docker-stacks/issues/675

Enable Delta Lake in Spark notebooks#

Please note that the Delta Lake packages are only available for Spark version > 3.0. By adding the properties to spark-defaults.conf, the user no longer needs to enable Delta support in each notebook.

FROM jupyter/pyspark-notebook:latest

ARG DELTA_CORE_VERSION="1.2.0"
RUN pip install --quiet --no-cache-dir delta-spark==${DELTA_CORE_VERSION} && \
     fix-permissions "${HOME}" && \
     fix-permissions "${CONDA_DIR}"

USER root

RUN echo 'spark.sql.extensions io.delta.sql.DeltaSparkSessionExtension' >> "${SPARK_HOME}/conf/spark-defaults.conf" && \
    echo 'spark.sql.catalog.spark_catalog org.apache.spark.sql.delta.catalog.DeltaCatalog' >> "${SPARK_HOME}/conf/spark-defaults.conf"

USER ${NB_UID}

# Trigger download of delta lake files
RUN echo "from pyspark.sql import SparkSession" > /tmp/init-delta.py && \
    echo "from delta import *" >> /tmp/init-delta.py && \
    echo "spark = configure_spark_with_delta_pip(SparkSession.builder).getOrCreate()" >> /tmp/init-delta.py && \
    python /tmp/init-delta.py && \
    rm /tmp/init-delta.py

Add Custom Fonts in Scipy notebook#

The example below is a Dockerfile to load Source Han Sans with normal weight, usually used for the web.

FROM jupyter/scipy-notebook:latest

RUN PYV=$(ls "${CONDA_DIR}/lib" | grep ^python) && \
    MPL_DATA="${CONDA_DIR}/lib/${PYV}/site-packages/matplotlib/mpl-data" && \
    wget --quiet -P "${MPL_DATA}/fonts/ttf/" https://mirrors.cloud.tencent.com/adobe-fonts/source-han-sans/SubsetOTF/CN/SourceHanSansCN-Normal.otf && \
    sed -i 's/#font.family/font.family/g' "${MPL_DATA}/matplotlibrc" && \
    sed -i 's/#font.sans-serif:/font.sans-serif: Source Han Sans CN,/g' "${MPL_DATA}/matplotlibrc" && \
    sed -i 's/#axes.unicode_minus: True/axes.unicode_minus: False/g' "${MPL_DATA}/matplotlibrc" && \
    rm -rf "/home/${NB_USER}/.cache/matplotlib" && \
    python -c 'import matplotlib.font_manager;print("font loaded: ",("Source Han Sans CN" in [f.name for f in matplotlib.font_manager.fontManager.ttflist]))'