The document presents a guide on building and deploying containerized Python applications using Docker and OpenShift. It covers techniques such as automated testing, continuous integration, and the importance of creating standardized Docker containers to ensure consistent application performance across environments. Additionally, it highlights tools and considerations for creating and managing containers and images.

1. Building and Deploying containerized
Python Apps in the Cloud
Rodolfo Carvalho
Software Engineer
Mar 12th
, 2016

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Shipping Apps
Background Wiring Components Together
Rinse & RepeatCreating Containers

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Background
Manual Automated

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Techniques & Practices
● Sprints
● Automated Tests
● TDD
● Continuous Integration
● Continuous Deployment

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Tools
● Ansible
● Yum / DNF
● IaaS
● Pip
● PaaS
● Docker

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Docker
● “Docker allows you to package an application with all of its
dependencies into a standardized unit”
● “Docker containers wrap up a piece of software in a complete
filesystem that contains everything it needs to run: code, runtime,
system tools, system libraries – anything you can install on a server.
This guarantees that it will always run the same, regardless of the
environment it is running in.”

7. Creating Containers

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Containers & Images

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Minimal Web App and Dockerfile

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docker build -t hello .docker build -t hello .

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Image Considerations 1/2
● Base Image
● Proper usage of layers
● Default user & permissions
● Other defaults:
● Exposed ports
● Work directory
● Entrypoint / Cmd

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Image Considerations 2/2
● Install dependencies
● System
● Language
● Reuse

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Source-To-Image
github.com/openshift/source-to-image

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s2i build . centos/python-34-centos7 hello-s2is2i build . centos/python-34-centos7 hello-s2i

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OpenShift Builds

16. Wiring Components Together

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More Apps

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oc new-appoc new-app

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OpenShift Templates

20. Rinse & Repeat

21. THANK YOU
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22. Coding Dojo
Hands on Coding with Python
Tomorrow 9:00 – 12:00