Guide to getting started with Docker, Python and Jupyter Notebook on zLinux.
Here, I'm using Red Hat Enterprise Linux 7.5 to build and deploy Jupyter notebook in an Ubuntu container. I will go over the steps used to build and run a Docker container.
Oh, and in case you're wondering: why would anyone do this - check out this snippet from the z14 announcement: "Microservices can be built on z14 with Node.js, Java, Go, Swift, Python, Scala, Groovy, Kotlin, Ruby, COBOL, PL/I, and more. They can be deployed in Docker containers where a single z14 can scale out to 2 million Docker containers".
Establish the OS release and version. We're running on RHEL 7.5 for s390x.
[cmihai@rh74s390x ~]$ cat /etc/redhat-release
Red Hat Enterprise Linux Server release 7.5 (Maipo)
[cmihai@rh74s390x ~]$ uname -a
Linux rh74s390x.novalocal 3.10.0-693.17.1.el7.s390x #1 SMP Sun Jan 14 10:38:29 EST 2018 s390x s390x s390x GNU/Linux
[cmihai@rh74s390x ~]$ docker --version
Docker version 17.05.0-ce, build 89658be
Setup regular user access, sudo and SSH keys
Create a regular user account
useradd cmihai
passwd cmihai
usermod -aG wheel cmihai
su - cmihai
Add your SSH public key to authorized_hosts
mkdir -p ~/.ssh
echo "YOURKEYHERE" >> ~/.ssh/authorized_keys
Log in as your new user, and forward port 9000:
ssh -L 9000:https://www.linkedin.com/redir/invalid-link-page?url=127%2e0%2e0%2e1%3A9000 -i cmihai.pem cmihai@myzLinux
Setup docker
Create the Docker group
sudo groupadd docker
sudo usermod -aG docker cmihai
Start Docker
sudo systemctl enable docker sudo systemctl restart docker.service sudo systemctl status docker.service
Test docker
docker run s390x/hello-world
Let’s run a simple Ubuntu interactive shell:
docker run --name s390x-ubuntu --hostname s390x-ubuntu --interactive --tty s390x/ubuntu /bin/bash
Building a Docker container for Jupyter Notebook
Create a Dockerfile from the s390x/ubuntu base image.
FROM s390x/ubuntu
MAINTAINER Mihai Criveti
# ADD AND RUN
RUN apt-get update \
&& apt-get install -y python3 python3-pip \
&& pip3 install jupyter \
&& apt-get clean
# COMMAND and ENTRYPOINT:
CMD ["jupyter","notebook","--allow-root","--ip=0.0.0.0","--port=9000"]
# NETWORK
EXPOSE 9000
Build the container:
docker build . --tag "cmihai/jupyter-lite:v1" -f Dockerfile
Run your new container:
docker run --name jupyter --hostname jupyter -p 9000:9000 cmihai/jupyter-lite:v1
Connect to Jupyter Notebook
You can now install depedencies directly from Jupyter:
!apt-get install --yes zlib1g-dev libjpeg-dev
Potential next steps:
- Consider setting up persistence for your notebooks (ex: VOLUME ["/notebooks"] in Dockerfile)
- Setup Docker Compose and build multi-tiered applications specifications - such as connecting your Jupyter Notebook to PostgreSQL, Redis, Spark, etc.
- Set up other programming languages or kernels (Java, R) even Zeppelin Notebook
For an interactive tutorial of using Docker for Data Science, check out: https://github.com/crivetimihai/docker-data-science
To see the original article, check out https://www.linkedin.com/pulse/data-science-environment-docker-jupyter-ibm-mainframe-mihai-criveti/
