Cloud with DJ

In this tutorial, I will show you how to set up Docker containers for Python projects with Boto3 and bind mounts. Let’s have fun.

Scenario

A Python development team is looking to simplify their setup process and maintain consistency across environments. They can create a Dockerfile to set up Python and Boto3 environments, download repositories locally, and configure containers for seamless collaboration.

Before starting, we need few tools ready.

Prerequisites

• An AWS account
• An IDE  
• Docker engine installed
• Basic understanding of Docker commands
• Familiarity with Git and GitHub

Step 1: Install Docker

• Log in to your AWS account and open CloudShell

• If Docker isn’t already installed, follow these commands:

sudo yum update -y 

sudo yum install docker -y 

Verify the installation by checking the Docker version:

docker –version 

Step 2. Create a Dockerfile and Build an Image

• Create a project directory:

mkdir dj_Docker 

cd dj_Docker 

• Create a Dockerfile: touch Dockerfile

• Use the following content:

• Build the Docker image: docker build -t python_boto3_image . 

 

Note: When running docker build, I realize I forgot to mention FROM in my dockerfile, and received this error

 

So I added the missing FROM

• re-run docker build -t python_boto3_image . 

• Go back to the running image: docker image

Step 3 – Download GitHub Repositories

• Create or find 3 repositories on GitHub
• Clone these three repositories locally:

git clone <GitHub_Repo_URL> 

• Verify if the 3 repositories are in your folder

Step 4. Create Ubuntu Containers with Bind Mounts

• Run containers with bind mounts for each repository directory:

docker run -d -t –name <containerName> -v “$(pwd)/<repoDir>:/dj_Docker” ubuntu 

 

• List active containers:

docker ps 

Step 5. Inspect and Verify Bind Mounts

• Inspect bind mounts: docker container inspect <containerName> 

• Access each container to verify: docker exec -it <containerName> bash 
• Exit each container before accessing the next: exit 

We successfully created Docker containers for Python projects, configured bind mounts, logged into containers. 

If time to cleaned up our resources.

• Stop and remove containers:  docker container stop <containerName> 

docker container prune 

Thank you for reading and/or following along! Leave us  a comment, Share & Follow. Please stay tuned for all my upcoming projects.