Jiri Podivin 4405071de0 Docker image refinement and preparation for future development.
.dockerignore file was added to limit size of the docker context
The more complex docker images will be placed in newly created
dockerfiles dir. Each in its own subdir
and accompanied with README.rst file describing their use.

Right now there is only one, the same as the one in repo root.
But in the future there will be more.

Signed-off-by: Jiri Podivin <jpodivin@redhat.com>
Change-Id: I5ed91d4258d9ad6725a86d5c3c6a40a02212b5d4
2021-02-12 12:48:00 +01:00
2020-06-23 13:54:26 +02:00
2020-09-11 22:04:15 +02:00
2020-02-28 10:42:18 +01:00
2020-02-28 14:47:28 +01:00
2020-02-28 14:47:28 +01:00

validations-libs

A collection of python libraries for the Validation Framework

Development Environment Setup

Vagrantfiles for CentOS and Ubuntu have been provided for convenience; simply copy one into your desired location and rename to Vagrantfile, then run:

vagrant up

Once complete you will have a clean development environment ready to go for working with Validation Framework.

Docker Quickstart

A Dockerfile is provided at the root of the Validations Library project in order to quickly set and hack the Validation Framework, on a equivalent of a single machine. Build the container from the Dockerfile by running:

docker build -t "vf:dockerfile" .

From the validations-libs repo directory.

Note

More complex images are available in the dockerfiles directory and require explicit specification of both build context and the Dockerfile.

Then you can run the container and start to run some builtin Validations:

docker run -ti vf:dockerfile /bin/bash

Then run validations:

validation.py run --validation check-ftype,512e --inventory /etc/ansible/hosts
Description
RETIRED, A collection of python libraries for the Validation Framework
Readme 5.9 MiB