To get started create a .gitlab-ci.yml, this is where you would define your different jobs: They also have documentation on python which is useful. I would recommend starting at GitLab documentation, it is pretty well written and straight forward. I tweaked with a few tools like Jenkins, AWS CodeBuild, Travis CI and I chose GitLab CI/CD, thus this article. Finally, you can select an individual stage to see the report of that stage of the pipeline, which will help you to debug any failures.įor more help and assistance, please check the GitLab runners documentation.On my path to find a CI/CD solution for our Django app, I was trying to find a tool that is easy to set up, easy to maintain, easy to scale (emphasis on EASY), and FREE*( 400 mins). Selecting the pipeline will take you to a page with details about the stages of the pipeline. To access a test report from a CI pipeline, start by viewing the Pipelines page in the CI/CD menu: This will show you the pipelines run in the repository, with the most recent one listed at the top. The second stage is named "test" and in this case we attempt to install the required Python modules before doing a very simple test on the starting the Python code. Flake8 incidentally is a tool used for checking the style and basic syntax checking of Python code. The first stage is named "flake8" - and there are three steps, the first is to install flake8, then dump the version before running flake8 in verbose mode. In the example above, we perform a two-stage pipeline with both stages using a Python 3.6 Docker image. This will take place whenever a code is pushed to the repo. An example file is given below: -īy default, the run will start by doing a "git fetch" command to pull either master or the relevant branch. ![]() It is important to note that to use the shared runners, you need to include the tag telling GitLab which runners you wish to use, this is done per task within the CI config file and should be set to " bear-gitlab-runners". Within GitLab, CI is enabled by creating a file called ".gitlab-ci.yml" at the root of the repository. To actually do something as part of CI, you now need to define the config. You'll need to do this for each git repository you want to use CI for. You've now enabled the shared runners for your repository. Scroll down until you find the "Shared Runners" section and click the "Enable shared Runners" button: From the GitLab web interface, select the "Settings" item from the left hand menu and chose "CI/CD": To use the BEAR GitLab shared runners, they first need to be enabled within the project in GitLab. Using the shared runners Initial project config As they are provided as part of the service, we ensure that we have monitoring of them to ensure their availability. Whilst researchers are able to setup their own runners outside of the BEAR environment, we provide a number of shared runners which are available for all BEAR GitLab projects to use. Within GitLab, this feature is provided by GitLab runners. This is often used to automate running of code validation tool, running test cases or even building packages for deployment. ![]() When using BEAR GitLab, its possible to use continuous integration to undertake automated tasks when code is committed to your project.
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