veyepar: Automated deployment of Python packages for development

Author(s):	Andrew MacDonald
Location	Room 104
Date	aug Sat 13	Days Raw Files
Start	13:40	First Raw Start	error-in-template
Duration	0:40:00	Offset	None
End	14:20	Last Raw End
Chapters
Total cuts_time	None min.

https://2016.pycon-au.org/schedule/109/view_talk
raw-playlist raw-mp4-playlist encoded-files-playlist mp4 svg png

assets release.pdf Automated_deployment_of_Python_packages_for_development.json
logs
Admin: episode episode list cut list raw files day marks day marks day image_files

State:

Locked:

clear this to unlock

Locked by:

user/process that locked.

Start:

initially scheduled time from master, adjusted to match reality

Duration:

length in hh:mm:ss

Name:

Video Title (shows in video search results)

Emails:

email(s) of the presenter(s)

Released:

has someone authorised pubication

Normalise:

Channelcopy:

m=mono, 01=copy left to right, 10=right to left, 00=ignore.

Thumbnail:

filename.png

Description:

markdown

The ability to automatically deploy development and test versions of software supports a rapid development/release cycle.

Within our section of the Bureau of Meteorology we have a number of internal Python packages, ranging from small simple packages to large applications that are dependent on the smaller packages. We manage the development cycle to ease deployment of these packages and applications into development, test, and production environments.

Elements of our process are:
    * Source code management (git)
    * Code review (Gerrit)
    * Continuous integration (Jenkins)
    * Internal PyPi servers (Apache)
    * A development environment for automatic deployment of every Gerrit approved commit (Anaconda environment via Jenkins)
    * Versioning (git tags + versioneer)
    * Test environment for every tagged version (Anaconda environment via Jenkins)
    * Production environment for specified releases (Anaconda environment via Ansible)
 
A key benefit of this process is that we have a deployed Python environment for the latest development version of all packages, a pinned collection of packages for testing, and an approved stable collection for production. The development and test environments are rapidly updated when commits are approved in Gerrit or tagged. The production environment is then readily updated with specific versions after a period of testing in the test environment.
This presentation will discuss our development process, how it works for us and how we leverage Python packaging to do it.

Comment:

production notes