History

bcambl 6f8e8e98bb update centos version check test Signed-off-by: bcambl <blayne@blaynecampbell.com>		2018-08-29 22:16:11 -06:00
..
README.md	Update README.md	2018-07-12 10:22:50 -05:00
__init__.py	setupVar tests passing for debian & centos	2016-10-10 23:14:39 -05:00
centos.Dockerfile	move PH_TRUE to Dockerfiles to DRY	2016-11-03 22:34:04 -05:00
conftest.py	add Fedora container to test matrix	2018-07-05 18:12:39 -06:00
debian.Dockerfile	Debian buildpack image for docker (Need `curl` for testing, using scm version to add `curl` and `git`.)	2017-02-20 09:24:47 -08:00
fedora.Dockerfile	add Fedora container to test matrix	2018-07-05 18:12:39 -06:00
test_000_build_containers.py	ensure images are build prior to running tests	2018-07-07 18:43:11 -06:00
test_automated_install.py	Improve download error message that is shown when downloading or verifying the binary failed	2018-08-07 16:51:58 +02:00
test_centos_fedora_support.py	update centos version check test	2018-08-29 22:16:11 -06:00
test_shellcheck.py	python linting: 'pytest' imported but unused (F401)	2018-07-03 00:30:00 -06:00

README.md

Recommended way to run tests

Make sure you have Docker and Python w/pip package manager.

From command line all you need to do is:

pip install tox
tox

Tox handles setting up a virtual environment for python dependancies, installing dependancies, building the docker images used by tests, and finally running tests. It's an easy way to have travis-ci like build behavior locally.

Alternative py.test method of running tests

You're responsible for setting up your virtual env and dependancies in this situation.

py.test -vv -n auto -m "build_stage"
py.test -vv -n auto -m "not build_stage"

The build_stage tests have to run first to create the docker images, followed by the actual tests which utilize said images. Unless you're changing your dockerfiles you shouldn't have to run the build_stage every time - but it's a good idea to rebuild at least once a day in case the base Docker images or packages change.

How do I debug python?

Highly recommended: Setup PyCharm on a Docker enabled machine. Having a python debugger like PyCharm changes your life if you've never used it :)