If you’re using GitLab CI to build your software, you might also want to use it to build Docker images of your application.This can be a little tricky, because by default GitLab CI runs jobs inside Docker containers.
Pytest is used to run unit tests in the Analytics project. The tests are executed from the root directory of the project with the pythonpytest CI pipeline job. The job produces a JUnit report of test results which is then processed by GitLab and displayed on merge requests. Writing New Tests.
- Clone with HTTPS. Open in your IDE. Visual Studio Code (SSH) Visual Studio Code (HTTPS) Copy HTTPS clone URL. Copy SSH clone URL git@gitlab.com:ayufan/python-getting-started.git. Copy HTTPS clone URL.
- Python development guidelines SCSS style guide Shell scripting standards and style guidelines Sidekiq debugging Sidekiq style guide. GitLab Runner officially supported binaries are available for the following architectures: x86, AMD64, ARM64, ARM, s390x, ppc64le.
The standard technique for getting around this problem is using Docker-in-Docker, but you can also use a simpler technique by using Podman, the reimplemented version of Docker.Let’s see why and how.
Option #1: Docker-in-Docker
When you run the docker command-line tool, it is actually not doing much work itself.Instead, it talks to dockerd, a daemon or server typically running on the same machine where you’re running the CLI.The actual work of running a container or building an image is done by dockerd.
When you want to run docker inside GitLab CI, you face the issue that GitLab CI jobs typically run as Docker containers.So you can’t just rely on a normal dockerd being available as you would, for example, in a virtual machine.
To help with this scenario, there’s a Docker image that runs dockerd for you: docker:dind.Once that is running, you can point docker at that running daemon and issue commands like docker build.
In the context of GitLab CI, your jobs can run services, which are also Docker containers.So we can configure .gitlab-ci.yml to run docker:dind as a service in a job:
In this case, the service is given the hostname alias dockerdaemon.You also need to tell the docker CLI how to find the server, which you can do via an environment variable DOCKER_HOST, as well as set a couple of other variables that make it work, and work faster:
A full configuration that builds an image and pushes it to the GitLab image registry corresponding to the GitLab CI repository looks like this:
For more details see the relevant GitLab CI documentation.
A working example
I’ve set up an example repository that contains this configuration.Here’s what the Dockerfile looks like:
Like most GitLab repositories, it has a corresponding Docker image registry, and you can run the image built by the above configuration like so:
Note: Outside the very specific topic under discussion, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article.
To ensure you’re following all the best practices you need to have a secure, correct, fast Dockerfiles, check out the Python on Docker Production Handbook.
Option #2: Podman
Podman is a reimplemented version of Docker from RedHat.It supports the same command-line options, but has a fundamentally different architecture: unlike Docker, there is no daemon by default.The CLI does all the work itself.
That means we can do a much simpler GitLab CI config, without the service running the daemon:
Notice all we had to do was change the docker command-line to do podman instead; they basically accept the same options.
A working example
The same example repository is also configured to use Podman.Again, you can run the resulting image:
Docker-in-Docker (DinD) vs Podman
Which of these two should you choose?DinD gives you access to BuildKit, which has some useful features and performance improvements; Podman does not support all of them yet, though it does support build secrets.
On the other hand, running the DinD daemon adds some overhead, since another image has to be downloaded; the DinD build adds another 20 seconds of fixed overhead in my test.For less trivial builds this overhead probably will be overwhelmed by other factors.
If you don’t care about BuildKit’s additional features, using Podman is just a little bit simpler while offering the same user experience.Finally, you could look into Buildah, which is how podman build is implemented: it’s a tool specifically focused only on building images.
Gitlab Runner Python Version
Hi,
I use gitlab runner with docker in docker. Unfortunately, our runners sometimes (and infrequently and un-reproducibly) hang or get stuck upon the following commands while building the docker container:
RUN pip download --no-cache-dir -r requirements.txt -d /artifacts
or
RUN pip install --no-cache-dir --no-index --find-links=/tmp/artifacts /tmp/artifacts/*
It hangs there for an hour until the build times out. The only solution is to restart the build process (and then it usually completes within just 3-5 minutes!).
The gitlab yaml looks (kinda) like this following minimal example:
and make build simply doesdocker build $(IMAGE_NAME) -t $(IMAGE_NAME_SHORT) -f Dockerfile .
Gitlab Runner Python Online
Any idea what could be the cause for this or where to first dig into to identify the problem?
- Is this most likely a pypi issue?
- Or is this a docker issue?
- Or is this a gitlab issue?
Gitlab Python Ci
Thanks!