continuous deployment

docker build gitlab

My Road to Docker – Part 4: Automated Container Builds with GitLab CI

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This post is part of a series on this project. Here is the series so far:

In the course of migrating most of the services I run over to running as Docker containers I’ve mostly tried to steer clear of building my own images. Thanks to the popularity of Docker this has been pretty easy for the most part. Many projects now ship an official Docker image as part of their releases. However, the inevitable happened when I had to build my own image in my recent TT-RSS migration.

The reason for my reluctance to build my own images is not some unfounded fear of Dockerfiles. It’s not building the images that I’m worried about, it’s maintaining them over time.

Every Docker image contains a mini Linux distribution, frozen in time from the day the image was built. Like any distribution of Linux these contain many software packages, some of which are going to need an update sooner or later in order to maintain their security. Of course you could just run the equivalent of an apt upgrade inside each container, but this defeats some of the immutability and portability guarantees which Docker provides.

The solution to this, is basically no more sophisticated than ‘rebuild your images and re-deploy’. The problem is no more help is offered. How do you go about doing that in a consistent and automated way?

Enter GitLab CI

If you have been following this blog you’ll know I’m a big fan of GitLab CI for these kinds of jobs. Luckily, GitLab already has support for building Docker images. The first thing I had to do was get this set up on my self-hosted GitLab runner. This was complicated by the fact that my runner was already running as a Docker container. It’s basically trying to start a Docker-in-Docker container on the host, from inside a Docker container, which is a little tricky (if you want to go further down, Docker itself is running from a Snap on a Virtual Machine! It really is turtles all the way down).

After lots of messing around and tweaking, I finally came up with a runner configuration which allowed the DinD service to start and the Docker client in the CI build to see it:

concurrent = 4
check_interval = 0

[session_server]
  session_timeout = 1800

[[runners]]
  name = "MyRunner"
  url = "https://gitlab.com/"
  token = "insert your token"
  executor = "docker"
  [runners.custom_build_dir]
  [runners.docker]
    tls_verify = false
    image = "ubuntu:18.04"
    privileged = true
    disable_entrypoint_overwrite = false
    oom_kill_disable = false
    disable_cache = false
    environment = ["DOCKER_DRIVER=overlay2", "DOCKER_TLS_CERTDIR=/certs"]
    volumes = ["/certs/client", "/cache"]
    shm_size = 0
    dns = ["my internal DNS server"]
  [runners.cache]
    [runners.cache.s3]
    [runners.cache.gcs]
  [runners.custom]
    run_exec = ""

The important parts here seem to be that the privileged flag must be true in order to allow the starting of privileged containers and that the DOCKER_TLS_CERTDIR should be set to a known location. The client subdirectory of this should then be shared as a volume in order to allow clients to connect via TLS.

You can ignore any setting of the DOCKER_HOST environment variable (unless you are using Kubernetes, maybe?). It should be populated with the correct value automatically. If you set it you will most likely get it wrong and the Docker client won’t be able to connect.

The CI Pipeline

I started the pipeline with a couple of static analysis jobs. First up is my old favourite yamllint. The configuration for this is pretty much the same as previously described. Here it’s only really checking the .gitlab-ci.yml file itself.

Next up is a new one for me. hadolint is a linting tool for Dockerfiles. It seems to make sensible suggestions, though I disabled a couple of them due to personal preference or technical issues. Conveniently, there is a pre-built Docker image, which makes configuring this pretty simple:

hadolint:
  <<: *preflight
  image: hadolint/hadolint
  before_script:
    - hadolint --version
  script:
    - hadolint Dockerfile

Building the Image

Next comes the actual meat of the pipeline, building the image and pushing it to our container registry:

variables:
  IMAGE_TAG: registry.gitlab.com/robconnolly/docker-ttrss:latest

build:
  stage: build
  image: docker:latest
  services:
    - docker:dind
  before_script:
    - docker info
  script:
    - docker login -u gitlab-ci-token -p $CI_BUILD_TOKEN registry.gitlab.com
    - docker build -t $IMAGE_TAG .
    - docker push $IMAGE_TAG
  tags:
    - docker

This is relatively straightforward if you are already familiar with the Docker build process. There are a couple of GitLab specific parts such as starting the docker:dind service, so that we have a usable Docker daemon in the build environment. The login to the GitLab registry uses the built-in $CI_BUILD_TOKEN variable as the password and from there we just build and push using the $IMAGE_TAG variable to name the image. Simple!

docker build gitlab
The pipeline for this is actually rather simple and boring compared to some of my others!

I also added another pipeline stage to send me a notification via my Gotify server when the build is done. This pretty much just follows the Gotify docs to send a message via curl:

notify:
  stage: notify
  image: alpine:latest
  before_script:
    - apk add curl
  script:
    - |
      curl -X POST "https://$GOTIFY_HOST/message?token=$GOTIFY_TOKEN" \
           -F "title=CI Build Complete" \
           -F "message=Scheduled CI build for TT-RSS is complete."
  tags:
    - docker

Of course the $GOTIFY_HOST and $GOTIFY_TOKEN variables are defined as secrets in the project configuration.

You can find the full .gitlab-ci.yml file for this pipeline in the docker-ttrss project repository.

Scheduling Periodic Builds

GitLab actually has support for scheduled jobs built in. However, if you are running on gitlab.com as I am you will find that you have very little control over when your jobs actually run, since it runs all cron jobs at 19 minutes past the hour. This would be fine for this single job, but I’m planning on having more scheduled builds run in the future, so I’d like to distribute them in time a little better, in order to reduce the load on my runner.

docker build gitlab
Pipeline triggers are created via the Settings->CI/CD menu in GitLab.

In order to do this I’m using a pipeline trigger, kicked off by a cron job at my end:

# Run TT-RSS build on Tuesday and Friday mornings
16  10 *   *   2,5   /usr/bin/curl -s -X POST -F token=SECRET_TOKEN -F ref=master https://gitlab.com/api/v4/projects/2707270/trigger/pipeline > /dev/null

This will start my pipeline at 10:16am (NZ time) on Tuesdays and Fridays. If the build is successful there will be a new image published shortly after these times.

An Aside: Managing random cron jobs like these really sucks once you have more than one or two machines. More than once I’ve lost a job because I forgot which machine I put it on! I know Ansible can manage cron jobs, but you are still going to end up with them configured across multiple files. I also haven’t found any way of managing cron jobs which relate to a container deployment (e.g. running WordPress cron for a containerised instance). Currently I’m managing all this manually, but I can’t help feeling that there should be a better way. Please suggest any approaches you may have in the comments!

Updating Deployed Containers

I could extend the pipeline above to update the container on my server with the new image. However, this would leave the other containers on the server without automated updates. Therefore I’m using Watchtower to periodically check for updates and upgrade any out of date images. To do this I added the following to the docker-compose.yml file on that server:

watchtower:
    image: containrrr/watchtower
    environment:
      WATCHTOWER_SCHEDULE: 0 26,56 10,22 * * *
      WATCHTOWER_CLEANUP: "true"
      WATCHTOWER_NOTIFICATIONS: gotify
      WATCHTOWER_NOTIFICATION_GOTIFY_URL: ${WATCHTOWER_NOTIFICATION_GOTIFY_URL}
      WATCHTOWER_NOTIFICATION_GOTIFY_TOKEN: ${WATCHTOWER_NOTIFICATION_GOTIFY_TOKEN}
      TZ: Pacific/Auckland
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    restart: always

Here I’m just checking for container updates four times a day. However, these are divided into two pairs of sequential checks separated by 30 minutes. This is more than sufficient for this server since it’s only accessible from my local networks. One of the update times is scheduled to start ten minutes after my CI build to pick up the new TT-RSS container. The reason for the second check after 30 mins is to ensure that the container gets updated even if the CI pipeline is delayed (as sometimes happens on gitlab.com).

You’ll note that I’m also sending notifications from Watchtower via Gotify. I was pleasantly surprised to discover the Gotify support in Watchtower when I deployed this. However, I think the notifications will get old pretty quickly and I’ll probably disable them eventually.

Conclusion

With all this in place I no longer have to worry about keeping my custom Docker images up to date. They will just automatically build at regular intervals and be updated automatically on the server. It actually feels pretty magical watching this all work end-to-end since there are a lot of moving parts here.

I’ll be deploying this approach as I build more custom images. I also still have to deploy Watchtower on all my other Docker hosts. The good news is that this has cured my reluctance to build custom images, so there will hopefully be more to follow. In the meantime, the community can benefit from a well maintained and updated TT-RSS image. Just pull with the following command:

docker pull registry.gitlab.com/robconnolly/docker-ttrss:latest

Enjoy!

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ansible roles

Automating My Infrastructure with Ansible and Gitlab CI: Part 2 – Deploying Stuff with Roles

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In the first post of this series, I got started with Ansible running in Gitlab CI. This involved setting up the basic pipeline, configuring the remote machines for use with our system and making a basic playbook to perform package upgrades. In this post we’re going to build on top of this to create a re-usable Ansible role to deploy some software and configuration to our fleet of servers. We will do this using the power of Ansible roles.

In last week’s post I described my monitoring system, based on checkmk. At the end of the post I briefly mentioned that it would be great to use Ansible to deploy the checkmk agent to all my systems. That’s what I’m going to describe in this post. The role I’ve created for this deploys the checkmk agent from the package download on my checkmk instance and configures it to be accessed via SSH. It also installs a couple of plugins to enable some extra checks on my systems.

A Brief Aside: ansible-lint

In my previous post I set up a job which ran all the playbooks in my repository with the --check flag. This performs a dry run of the playbooks and will alert me to any issues. In that post I mentioned that all I was really looking for was some kind of syntax/sanity checking on the playbooks and didn’t really need the full dry run. Several members of the community stepped forward to suggest ansible-lint – thanks to all those that suggested it!

I’ve now updated my CI configuration to run ansible-lint instead of the check job. The updated job is shown below:

ansible-lint:
  <<: *ansible
  stage: check
  script:
    - ansible-lint -x 403 playbooks/*.yml

This is a pretty basic use of ansible-lint. All I’m doing is running it on all the playbooks in my playbooks directory. I do skip a single rule (403) with the -x argument. The rule in question is about specifying latest in package installs, which conflicts with my upgrade playbook. Since I’m only tweaking this small thing I just pass this via the CLI rather than creating a config file.

I’ve carried the preflight jobs and the ansible-lint job over to the CI configuration for my new role (described below). Since this is pretty much an exact copy of that of my main repo, I’m not going to explain it any further.

Creating a Base Role

I decided that I wanted my roles self contained in their own git repositories. This keeps everything a bit tidier at the price of a little extra complexity. In my previous Ansible configuration I had all my roles in the same repo and it just got to be a big mess after a while.

To create a role, first initialise it with ansible-galaxy. Then create a new git repo in the resulting directory:

$ ansible-galaxy init ansible_role_checkmk_agent
$ cd ansible_role_checkmk_agent
$ git init .

I actually didn’t perform these steps and instead started from a copy of the old role I had for this in my previous configuration. This role has been tidied up and expanded upon for the new setup.

The ansible-galaxy command above will create a set of files and directories which provide a skeleton role. The first thing to do is to edit the README.md and meta/main.yml files for your role. Just update everything to suit what you are doing here, it’s pretty self explanatory. Once you’ve done this all the files can be added to git and committed to create the first version of your role.

Installing the Role

Before I move on to exactly what my role does, I’m going to cover how we will use this role in our main infrastructure project. This is done by creating a requirements.yml file which will list the required roles. These will then be installed by passing the file to ansible-galaxy. Since the installation tool can install from git we will specify the git URL as the installation location. Here are the contents of my requirements.yml file:

---
 - name: checkmk_agent
   scm: git
   src: git+https://gitlab.com/robconnolly/ansible_role_checkmk_agent.git
   version: master

Pretty simple. In order to do the installation all we have to do is run the following command:

$ ansible-galaxy install --force -r requirements.yml -p playbooks/roles/

This will install the required Ansible roles to the playbooks/roles directory in our main project, where our playbooks can find them. The --force flag will ensure that the role always gets updated when we run the command. I’ve added this command in the before_script commands in the CI configuration to enable me to use the role in my CI jobs.

Now the role will be installed where we actually need it. However, we are not yet using it. I’ll come back to this later. Let’s make the role actually do something first!

What the Role Does

The main behaviour of the role is defined in the tasks/main.yml file. This file is rather long, so I won’t reproduce this here. Instead I’ll ask you to open the link and follow along with my description below:

  • The first task creates a checkmk user on the target system. This will be used by checkmk to log in and run the agent.
  • The next task creates a .ssh directory for the checkmk user and sets it’s permissions correctly.
  • Next we create an authorized_keys file for the user. This uses a template file which will restrict what the key can do. The actual key comes from the checkmk_pub_key variable which will be passed in from the main project. The template is as follows:
command="/usr/bin/sudo /usr/bin/check_mk_agent",no-port-forwarding,no-x11-forwarding,no-agent-forwarding {{ checkmk_pub_key }}
  • Next are a couple of tasks to install some dependent packages for the rest of the role. There is one task for Apt based systems and another for Yum based systems. I’m not sure if the monitoring-plugins package is actually required. I had it in my previous role and have just copied it over.
  • The two tasks remove the xinetd package on both types of system. Since we are accessing the agent via SSH we don’t need this. I was previously using this package for my agent access so I want to make sure it is removed. This behaviour can be disabled by setting the checkmk_purge_xinetd variable to false.
  • The next task downloads the checkmk agent deb file to the local machine. This is done to account for some of the remote servers not having direct access to the checkmk server. I then upload the file in the following task. The variables checkmk_server, checkmk_site_name and checkmk_agent_deb are used to specify the server address, monitoring instance (site) and deb file name. The address and site name are designed to be externally overridden by the main project.
  • The next two tasks repeat the download and upload process for the RPM version of the agent.
  • We then install the correct agent in the next two tasks.
  • The following task disables the systemd socket file for the agent to stop it being accessible over an unencrypted TCP port. Right now I don’t do this on my CentOS machines because they are too old to have systemd!
  • The final few tasks get in to installing the Apt and Docker plugins on systems that require it. I follow the same process of downloading then uploading the files and make them executable. The Docker plugin requires that the docker Python module be installed, which we achieve via pip. It also requires a config file, which as discussed in my previous post needs to be modified. I keep my modified copy in the repository and just upload it to the correct location.

The variables that are used in this are specified in the vars/main.yml and defaults/main.yml files. The default file contains the variables that should be overridden externally. I don’t specify a default for the SSH public key because I couldn’t think of a sensible value, so this at least must be specified for the role to run.

With all this in place our role is ready to go. Next we should try that from our main project.

Applying the Role

The first thing to do is to configure the role via the variables described above. I did this from my hosts.yml file which is encrypted, but the basic form is as follows:

all:
  vars:
    checkmk_server: <server_ip>
    checkmk_site_name: <mysitename>
    checkmk_pub_key: <mypubkey>

The public key has to be that which will be used by the checkmk server. As such the private key must be installed on the server. I’ll cover how to set this up in checkmk below.

Next we have the playbook which will apply our role. I’ve opted to create a playbook for applying common roles to all my systems (of which this is the first). This goes in the file playbooks/common.yml:

---
- hosts: all
  roles:
    - { role: checkmk_agent }

This is extremely basic, all it does is apply the checkmk_agent role to all servers.

The corresponding CI job is only marginally more complex:

common-roles:
  <<: *ansible
  stage: deploy
  script:
    - ansible-playbook playbooks/common.yml
  only:
    refs:
      - master

With those two in place a push to the server will start the pipeline and eventually deploy our role to our servers.

ansible roles
Our updated CI pipeline showing the ansible-lint job and the new common-roles job

Configuring Checkmk Agent Access via SSH

Of course the deployment on the remote servers is only one side of the coin. We also need to have our checkmk instance set up to access the agents via SSH. This is documented pretty well in the checkmk documentation. Basically it comes down to putting the private key corresponding to the public key used earlier in a known location on the server and then setting up an “Individual program call instead of agent access” rule in the “Hosts and Service Parameters” page of WATO.

I modified the suggested SSH call to specify the private key and user to use. Here is the command I ended up using in my configuration.

/usr/bin/ssh -i /omd/sites/site_name/.ssh/id_rsa -o StrictHostKeyChecking=no checkmk@$HOSTADDRESS$

When you create the rule you can apply it to as many hosts as you like. In my setup this is all of them, but you should adjust as you see fit.

ansible roles
The checkmk WATO rule screen for SSH agent access

Conclusion

If you’ve been following along you should now be able to add new hosts to your setup (via hosts.yml) and have the checkmk agent deployed on them automatically. You should also have an understanding of how to create reasonably complex Ansible roles in external repositories and how to use them in your main Ansible project.

There are loads of things about roles that I haven’t covered here (e.g. handlers). The best place to start learning more would be the Ansible roles documentation page. You can then fan out from there on other concepts as they arise.

Next Steps

So far on this adventure I’ve tested my playbooks and roles by just making sure they work against my servers (initially on a non-critical one). It would be nice to have a better way to handle this and to be able to run these tests and verify that the playbook is working from a CI job. I’ll be investigating this for the next part of this series.

The next instalment will probably be delayed by a few weeks. I have something else coming which will take up quite a bit of time. For my regular readers, there will still be blog posts – they just won’t be about Ansible or CI. This is probably a good thing, I’ve been covering a lot of CI stuff recently!

As always please get in contact if you have any feedback or improvements to suggest, or even if you just want to chat about your own Ansible roles.

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home assistant gitlab ci

Continuous Integration/Deployment for Home Assistant with Gitlab CI

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One of the best things about writing this blog is the interactions I have with other people. Of course it’s always nice to get feedback, whether it’s positive or (constructively) negative. It’s even better to see what similar projects other people are undertaking. Sometimes comments even start me off in a different direction than I had been taking.

This project was inspired by one such conversation. This started out rather gruff but actually ended up being really positive and motivated me to go further with an approach that I’d mostly dropped. The conversation in question was in relation to my recent “Seven Home Assistant Tips and Best Practices” post. Specifically it was around testing and deploying your Home Assistant config with Continuous Integration (via Gitlab CI).

I’m already familiar with Continuous Integration and Continuous Deployment through work. I had developed a minimal setup for validating my HASS config. However, I’d previously given up on it due to the slowness of Gitlab’s shared CI runners. What follows is my new attempt. Thanks to /u/automate_the_things and all the other commenters on that thread for the inspiration/persuasion to do this!

Setting Up a Local Runner

Ideally, I’d like to fully self host my own Gitlab instance. However, the recommended RAM requirements are between 4 and 8GB, which is a little ridiculous. Perhaps when I upgrade my server I’ll be able to spare enough RAM for this. For now I’m just running a local runner and connecting it to the cloud version of Gitlab.

I decided to go with deploying the runner as a Docker container and also executing my jobs also within their own containers. This fits with my recent Docker shenanigans and I’m familiar with this setup having deployed it for work. CI systems are one area where I’ve always felt that using containers makes sense, even before my recent Docker adventures. Each build running in it’s own container removes a lot of the complexity in managing build environments for different projects. It also means that your build machines only require Docker.

I set up my runner in a new VM on my main server. Then I pretty much just followed the official instructions to install the runner. I did convert the docker run command to a minimal docker-compose.yml file for ease of reproduction however:

version: '3'

services:
  gitlab-runner:
    image: gitlab/gitlab-runner
    volumes:
      - /home/rob/docker-data/gitlab-runner:/etc/gitlab-runner
      - /var/run/docker.sock:/var/run/docker.sock

Once that was done, I finished by getting the runner registered and connected to my project.

home assistant gitlab ci
Once your runner is active it should show up in your Gitlab runners page (accessible via Settings->CI/CD->Runners)

Build Pipeline Configuration

In my research for this project, I came across Frenck’s Gitlab CI configuration, which is really awesome (thanks @Frenck). I’ve based mine heavily on his with some tweaks for my configuration and environment. The finished pipeline runs the following jobs:

  • shellcheck – this job performs various checks on any shell scripts in the repository
  • yamllint – this job performs a full lint check of all my YAML files. Since I’ve never run this before it threw up loads of errors. I started fixing a few of these, but eventually marked the job with allow_failure: true to allow the pipeline to continue even with these errors. I’ll work on fixing these issues bit by bit over the next few weeks. I also remove some files which are encrypted with git-crypt
  • jsonlint – pretty much the same as yamllint, but for JSON files. Any files that are encrypted are excluded from this check.
  • markdownlint – similar as the previous jobs, but for checking of markdown files (such as the README.md file)
  • ha-latest – checks the HASS configuration against the current release of Home Assistant
  • ha-rc – runs a HASS configuration check against the next release candidate for Home Assistant
  • ha-dev – checks the HASS configuration against the development version of Home Assistant. Both of these jobs are configured to allow failure. This is just intended to give me advanced warning of and breaking configuration that may prevent HASS from starting up in a future release.
  • deploy – deploys the configuration to my HASS server. I’ll discuss this in more detail below.
home assistant gitlab ci
My full pipeline (note the yellow status of the failing yamllint job)

You can find the finished configuration in my hass-config repository.

Deployment Approaches

There are several ways I could have done the deployment, which may suit different scenarios:

  • We could use the Home Assistant Gitlab CI sensor to poll the pipeline status. We would then trigger an automation to pull down the configuration when the pipeline passes. I haven’t tried this approach. However, it is potentially useful if your HASS server and Gitlab runner are on different networks and your HASS server is not publicly available.
  • We could use a pipeline webhook from Gitlab to a webhook handler in HASS. This would trigger an automation similar to that above. Again, I haven’t tried this. It would be useful if your Home Assistant instance and Gitlab CI runner are on different networks. However, it would require your HASS instance to be publicly available.
  • Similar to the approach above you could trigger the HASS webhook handler from a job in your Gitlab runner directly with CURL (rather than using the built in webhooks). This has the advantage over the previous two approaches that it gives you an explicit deploy stage in your pipeline. This is turn gives you the ability to track deployments via environments. It’s also potentially a lot simpler, since it would only be triggered if the previous stages in the pipeline passed. You also wouldn’t have to parse the JSON payload.
  • The approach I have taken is to deploy directly from the runner container via SSH. This is because my runner and HASS machines are running in the same network and so I can easily SSH between them without having to open any ports through my firewall. It also centralises all the deployment logic in the CI configuration, without any HASS automations needed.

My Deployment Job

As per my CI configuration, the deployment job is as follows:

deploy:
  stage: deploy
  image:
    name: alpine:latest
    entrypoint: [""]
  environment:
    name: home-assistant
  before_script:
    - apk --no-cache add curl openssh-client
    - echo "$DEPLOYMENT_SSH_KEY" > id_rsa
    - chmod 600 id_rsa
  script:
    - ssh -i id_rsa -o "StrictHostKeyChecking=no" $DEPLOYMENT_SSH_LOGIN "cd /mnt/docker-data/home-assistant && git fetch && git checkout $CI_COMMIT_SHA"
    - "curl -X POST -H \"Authorization: Bearer $DEPLOYMENT_HASS_TOKEN\" -H \"Content-Type: application/json\" $DEPLOYMENT_HASS_URL/api/services/homeassistant/restart"
  after_script:
    - rm id_rsa
  only:
    refs:
      - master
  tags:
    - hass

As you can see, this job runs in a plain Alpine Linux container and is deploying the the home-assistant environment. This allows me to track what versions were deployed and when from the Gitlab UI.

The before_script portion installs a couple of dependencies which we need for later and pulls the (password-less) SSH key we need for logging into the HASS server from the project variables. This is stored in the $DEPLOYMENT_SSH_KEY variable in the Gitlab configuration. The resulting file must have it’s permissions set to 600 to allow the SSH client to use it.

Moving on to the script portion. The first step performs the actual deployment of the repository to the server via SSH. Here we use our SSH key that we wrote out above. The public portion of this is installed on the HASS server as for the ci user. We also disable strict host key checking to prevent the SSH client prompting us to accept the fingerprint.

home assistant gitlab ci
The Gitlab CI variables page (accessible via Settings->CI/CD->Variables)

The SSH command connects to the server specified in $DEPLOYMENT_SSH_LOGIN, which is again set in the Gitlab variables configuration. This has the form ci@<hass host IP>. It should be noted here that the Alpine container defaults to using Google’s DNS. This means that resolving internal hostnames for your network will fail. I’m using the IP addresses for now to get around this.

Remote Control Commands

The SSH command sends a sequence of commands to be run on the HASS server. These commands are as follows:

  • cd /mnt/docker-data/home-assistant – change directory to the configuration directory on the server
  • git fetch – fetch all the new stuff via git
  • git checkout $CI_COMMIT_SHA – checkout the exact commit that we are running a pipeline for via one of Gitlab’s built in variables

This arrangement of commands allows me to control exactly what gets deployed to the server for each pipeline run. In this way we won’t accidentally deploy the wrong version if new code is checked into the server whilst our pipeline is running.

In order for the git fetch command to work another password-less SSH key is required. This time this is for the ci user on the HASS system. The public portion of this is installed as a deploy key for the project in Gitlab. I suppose it’s equally valid to pull changes via HTTPS (for public repos), but since the remote on my repository was already set up to use SSH I decided to continue using it.

Restarting Home Assistant from Gitlab CI

The second command in our script section is to restart Home Assistant after the configuration has been updated. Here we use CURL to call the homeassistant.restart service as per the API docs. The Home Assistant authentication token and URL are stored in Gitlab CI variables again.

Finally, we enter the after_script section, which will be executed even in the case that one of the above commands fails. Here we simply delete the id_rsa SSH key file.

I’ve restricted my deploy job to run only on pushes to the master branch. This allows me to use other branches in the repo as I please without having them deployed to the server accidentally. I’ve also used a tag of hass to prevent running on runners not intended for this job. Since I only have one runner right now this isn’t a concern, but having it in place makes things easier if/when I add more runners.

Conclusion

I’m really pleased with how this CI pipeline has turned out. However, I’m still a little concerned at how long all these steps take. The latest pipeline took 6 minutes and 42 seconds to run (plus the time it takes HASS to restart). This isn’t very much if it’s just a fire and forget operation. It is however, a long time if I am trying to iterate on my configuration. I’m going to play around with the runner configuration to see if I can get this down further. I also want to investigate options for testing on my local machine. In my previous attempt at this my builds could sit for longer than this waiting for one of Gitlab’s shared runners. So I’ve at least made progress in the speed department.

Next Steps

In terms of further improvements, I’d like better notifications of the pipeline progress and notifications when HASS competes it’s restart. I will implement these with Gotify. Right now I only get an email if the build fails from Gitlab. I’m also going to integrate the pipeline status into Home Assistant with the previously mentioned Gitlab CI sensor. I’m even tempted to turn one of my smart lights into a build light to alert me to problems!

I also want to take my use of CI in my infrastructure further. My next target will be building some modified Docker images for a couple of my services in CI. Deployment of the relevant Docker stacks is another thing I’d like to try out. I’ve not had chance to play with building containers via Gitlab CI before so that will be interesting.

I hope that you’ve enjoyed this post and that it’s inspired you to give something similar a go. Thanks to this pipeline I’ll never have to worry about HASS being unable to start due to a broken configuration again. I’m open to further improvements to this process. Please feel free to share any you may have via the feedback channels. Thanks for reading!

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