Advanced Containers
In the first part, we pulled and ran existing container images from Docker Hub. In this section, we will build an image from scratch for running some of our own Python3 code. Then, we will push that image back up to Docker Hub so others may find and use it. After going through this module, students should be able to:
Install and test code in a container interactively
Write a Dockerfile from scratch
Build a Docker image from a Dockerfile
Push a Docker image to Docker Hub
Getting Set Up
Scenario: You are a developer who has written some code for reading and parsing meteorite landing data in JSON format. You now want to distribute that code for others to use in what you know to be a stable production environment (including OS and dependency versions). End users may want to use this application on their local workstations, in the cloud, or on an HPC cluster.
The first step in a typical container development workflow entails installing and testing an application interactively within a running Docker container.
Note
We recommend doing this on the class ISP server. But, one of the most important features of Docker is that it is platform agnostic. These steps could be done anywhere Docker is installed.
To begin, make a new folder for this work and prepare to gather some important files.
[isp02]$ cd ~/coe-332/
[isp02]$ mkdir docker-exercise/
[isp02]$ cd docker-exercise/
[isp02]$ pwd
/home/wallen/coe-332/docker-exercise
Specifically, you need your ml_data_analysis.py script and the input data
file called Meteorite_Landings.json. You can make copies of your own, our
download sample copies from the links below. You also need a Dockerfile, and
we can just make an empty one with no contents for now.
[isp02]$ pwd
/home/wallen/coe-332/docker-exercise
[isp02]$ touch Dockerfile
[isp02]$ wget https://raw.githubusercontent.com/tacc/coe-332-sp22/main/docs/unit04/scripts/Meteorite_Landings.json
[isp02]$ wget https://raw.githubusercontent.com/tacc/coe-332-sp22/main/docs/unit04/scripts/ml_data_analysis.py
[isp02]$ ls
Dockerfile Meteorite_Landings.json ml_data_analysis.py
Warning
It is important to carefully consider what files and folders are in the same
PATH as a Dockerfile (known as the ‘build context’). The docker build
process will index and send all files and folders in the same directory as
the Dockerfile to the Docker daemon, so take care not to docker build at
a root level.
Containerize Code Interactively
There are several questions you must ask yourself when preparing to containerize code for the first time:
What is an appropriate base image?
What dependencies are required for my program?
What is the install process for my program?
What environment variables may be important?
We can work through these questions by performing an interactive installation
of our Python script. Our development environment (the class ISP server) is a
Linux server running CentOS 7.7. We know our code works here, so that is how we
will containerize it. Use docker run to interactively attach to a fresh
CentOS 7.7 container.
Warning
Due to Log4Shell vulnerability (CVE-2021-44228 or CVE-2021-45046), let’s instead pull CentOS 7.9.
[isp02]$ docker run --rm -it -v $PWD:/code centos:7.9.2009 /bin/bash
[root@7ad568453e0b /]#
Here is an explanation of the options:
docker run # run a container
--rm # remove the container on exit
-it # interactively attach terminal to inside of container
-v $PWD:/code # mount the current directory to /code
centos:7.9.2009 # image and tag from Docker Hub
/bin/bash # shell to start inside container
The command prompt will change, signaling you are now ‘inside’ the container.
And, new to this example, we are using the -v flag which mounts the contents
of our current directory ($PWD) inside the container in a folder in the root
directory called (/code).
Update and Upgrade
The first thing we will typically do is use the CentOS package manager yum
to update the list of available packages and install newer versions of the
packages we have. We can do this with:
[root@7ad568453e0b /]# yum update
...
Note
You will need to press ‘y’ followed by ‘Enter’ twice to download and install the updates
Install Required Packages
For our Python scripts to work, we need to install two dependencies: Python3 and the ‘pytest’ package (more on the ‘pytest’ package later, let’s just assume for now we need it).
[root@7ad568453e0b /]# yum install python3
...
[root@7ad568453e0b /]# python3 --version
Python 3.6.8
[root@7ad568453e0b /]# pip3 install pytest==7.0.0
Collecting pytest==7.0.0
...
Installing collected packages: py, pyparsing, packaging, typing-extensions, zipp,
importlib-metadata, pluggy, attrs, iniconfig, tomli, pytest
Successfully installed attrs-21.4.0 importlib-metadata-4.8.3 iniconfig-1.1.1 packaging-21.3
pluggy-1.0.0 py-1.11.0 pyparsing-3.0.7 pytest-7.0.0 tomli-1.2.3 typing-extensions-4.1.1
zipp-3.6.0
Warning
An important question to ask is: Does the versions of Python and other dependencies match the versions you are developing with in your local environment? If not, make sure to install the correct version of Python.
Install and Test Your Code
At this time, we should make a small edit to the code that will make it a little more flexible and more amenable to running in a container. Instead of hard coding the filename ‘Meteorite_Landings.json’ in the script, let’s make a slight modification so we can pass the filename on the command line. In the script, add this line near the top:
import sys
And change the with open... statements to these, as appropriate:
with open(sys.argv[1], 'r') as f:
ml_data = json.load(f)
Since we are using a simple Python script, there is not a difficult install process. However, we can make it executable and add it to them user’s PATH.
[root@7ad568453e0b /]# cd /code
[root@7ad568453e0b /]# chmod +rx ml_data_analysis.py
[root@7ad568453e0b /]# export PATH=/code:$PATH
Now test with the following:
[root@7ad568453e0b /]# cd /home
[root@7ad568453e0b /]# cp /code/Meteorite_Landings.json .
[root@7ad568453e0b /]# ml_data_analysis.py Meteorite_Landings.json
83857.3
Northern & Eastern
...etc
We now have functional versions of our script ‘installed’ in this container. Now would be a good time to execute the history command to see a record of the build process. When you are ready, type exit to exit the container and we can start writing these build steps into a Dockerfile.
Assemble a Dockerfile
After going through the build process interactively, we can translate our build
steps into a Dockerfile using the directives described below. Open up your copy
of Dockerfile with a text editor and enter the following:
The FROM Instruction
We can use the FROM instruction to start our new image from a known base image. This should be the first line of our Dockerfile. In our scenario, we want to match our development environment with CentOS 7.9. We know our code works in that environment, so that is how we will containerize it for others to use:
FROM centos:7.9.2009
Base images typically take the form os:version. Avoid using the ‘latest’ version; it is hard to track where it came from and the identity of ‘latest’ can change.
Tip
Browse Docker Hub to discover other potentially useful base images. Keep an eye out for the ‘Official Image’ badge.
The RUN Instruction
We can install updates, install new software, or download code to our image by
running commands with the RUN instruction. In our case, our only dependencies
were Python3 and the “pytest” library. So, we will use a few RUN instructions to
install them. Keep in mind that the the docker build process cannot handle
interactive prompts, so we use the -y flag with yum and pip3.
RUN yum update -y
RUN yum install -y python3
RUN pip3 install pytest==7.0.0
Each RUN instruction creates an intermediate image (called a ‘layer’). Too many layers makes the Docker image less performant, and makes building less efficient. We can minimize the number of layers by combining RUN instructions. Dependencies that are more likely to change over time (e.g. Python3 libraries) still might be better off in in their own RUN instruction in order to save time building later on:
RUN yum update -y && \
yum install -y python3
RUN pip3 install pytest==7.0.0
Tip
In the above code block, the character at the end of the lines causes the newline character to be ignored. This can make very long run-on lines with many commands separated by && easier to read.
The COPY Instruction
There are a couple different ways to get your source code inside the image. One
way is to use a RUN instruction with wget to pull your code from the web.
When you are developing, however, it is usually more practical to copy code in
from the Docker build context using the COPY instruction. For example, we can
copy our script to the root-level /code directory with the following
instructions:
COPY ml_data_analysis.py /code/ml_data_analysis.py
And, don’t forget to perform another RUN instruction to make the script executable:
RUN chmod +rx /code/ml_data_analysis.py
The ENV Instruction
Another useful instruction is the ENV instruction. This allows the image
developer to set environment variables inside the container runtime. In our
interactive build, we added the /code folder to the PATH. We can do this
with ENV instructions as follows:
ENV PATH "/code:$PATH"
Putting It All Together
The contents of the final Dockerfile should look like:
1FROM centos:7.9.2009
2
3RUN yum update -y && \
4 yum install -y python3
5
6RUN pip3 install pytest==7.0.0
7
8COPY ml_data_analysis.py /code/ml_data_analysis.py
9
10RUN chmod +rx /code/ml_data_analysis.py
11
12ENV PATH "/code:$PATH"
Build the Image
Once the Dockerfile is written and we are satisfied that we have minimized the number of layers, the next step is to build an image. Building a Docker image generally takes the form:
[isp02]$ docker build -t <dockerhubusername>/<code>:<version> .
The -t flag is used to name or ‘tag’ the image with a descriptive name and
version. Optionally, you can preface the tag with your Docker Hub username.
Adding that namespace allows you to push your image to a public registry and
share it with others. The trailing dot ‘.’ in the line above simply
indicates the location of the Dockerfile (a single ‘.’ means ‘the current
directory’).
To build the image, use:
[isp02]$ docker build -t username/ml_data_analysis:1.0 .
Note
Don’t forget to replace ‘username’ with your Docker Hub username.
Use docker images to ensure you see a copy of your image has been built. You can
also use docker inspect to find out more information about the image.
[isp02]$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
wjallen/ml_data_analysis 1.0 2883079fad18 About a minute ago 547MB
...
[isp02]$ docker inspect username/ml_data_analysis:1.0
If you need to rename your image, you can either re-tag it with docker tag, or
you can remove it with docker rmi and build it again. Issue each of the
commands on an empty command line to find out usage information.
Test the Image
We can test a newly-built image two ways: interactively and non-interactively.
In interactive testing, we will use docker run to start a shell inside the
image, just like we did when we were building it interactively. The difference
this time is that we are NOT mounting the code inside with the -v flag,
because the code is already in the container:
[isp02]$ docker run --rm -it username/ml_data_analysis:1.0 /bin/bash
...
[root@c5cf05edddcd /]# ls /code
ml_data_analysis.py
[root@c5cf05edddcd /]# cd /home
[root@c5cf05edddcd home]# pwd
/home
[root@c5cf05edddcd home]# ml_data_analysis.py Meteorite_Landings.json
Traceback (most recent call last):
File "/code/ml_data_analysis.py", line 96, in <module>
main()
File "/code/ml_data_analysis.py", line 82, in main
with open(sys.argv[1], 'r') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'Meteorite_Landings.json'
Here is an explanation of the options:
docker run # run a container
--rm # remove the container when we exit
-it # interactively attach terminal to inside of container
username/... # image and tag on local machine
/bin/bash # shell to start inside container
Uh oh! We forgot about Meteorite_Landings.json! We get a FileNotFoundError
in Python3. This is because we (1) did not copy the JSON file into the container
at build time, and (2) we did not copy the JSON file into the container at run
time.
We should pause at this moment to think about how we want to distribute this application. Should the data be encapsulated within? Or should we expect potential users to be brining their own data for analysis?
Let’s try again, but this time mount the data inside the container so we can
access it. If we mount the current folder as, e.g., /data, then everything
in the current folder will be available. In addition, if we write any new files
inside the container to /data, those will be preserved and persist outside
the container once it stops.
[isp02]$ docker run --rm -it -v $PWD:/data username/ml_data_analysis:1.0 /bin/bash
[root@dc0d6bf1875c /]# pwd
/
[root@dc0d6bf1875c /]# ls /data
Dockerfile Meteorite_Landings.json ml_data_analysis.py
[root@dc0d6bf1875c /]# ls /code
ml_data_analysis.py
[root@dc0d6bf1875c /]# ml_data_analysis.py /data/Meteorite_Landings.json
83857.3
Northern & Eastern
... etc
Everything looks like it works now! Next, exit the container and test the code
non-interactively. Notice we are calling the container again with docker run,
but instead of specifying an interactive (-it) run, we just issue the command
as we want to call it on the command line. Also, notice the return of the -v
flag, because we need to create a volume mount so that our data
(Meteorite_Landings.json) is available inside the container.
[isp02]$ docker run --rm -v $PWD:/data username/ml_data_analysis:1.0 ml_data_analysis.py /data/Meteorite_Landings.json
83857.3
Northern & Eastern
... etc
Much simpler and cleaner! Our only local dependencies are the Docker runtime and some input data that we provide. Then we pull and run the image, mounting our data inside the container and executing the embedded Python3 script. Anyone with their own data could follow our same steps to replicate our work in their own environments.