[{"data":1,"prerenderedAt":817},["ShallowReactive",2],{"/en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean":3,"navigation-en-us":37,"banner-en-us":446,"footer-en-us":456,"blog-post-authors-en-us-Owen Williams":698,"blog-related-posts-en-us-autoscale-continuous-deployment-gitlab-runner-digital-ocean":712,"blog-promotions-en-us":754,"next-steps-en-us":807},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean.yml","Autoscale Continuous Deployment Gitlab Runner Digital Ocean",[7],"owen-williams",null,"engineering",{"slug":11,"featured":12,"template":13},"autoscale-continuous-deployment-gitlab-runner-digital-ocean",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to autoscale continuous deployment with GitLab Runner on DigitalOcean","Our friends over at DigitalOcean share how to configure a highly scalable, responsive and cost-effective GitLab infrastructure.",[18],"Owen Williams","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749680042/Blog/Hero%20Images/gitlab-digitalocean-cover.jpg","2018-06-19","[GitLab CI/CD](/solutions/continuous-integration/) is an effective way to build the habit of testing all code before it’s deployed. GitLab CI/CD is also highly scalable thanks to an additional tool, GitLab Runner, which automates scaling your build queue in order to avoid long wait times for development teams trying to release code.\n\nIn this guide, we will demonstrate how to configure a highly scalable GitLab infrastructure that manages its own costs, and automatically responds to load by increasing and decreasing available server capacity.\n\n## Goals\n\nWe’re going to build a scalable CI/CD process on DigitalOcean that automatically responds to demand by creating new servers on the platform and destroys them when the queue is empty.\n\nThese reusable servers are spawned by the GitLab Runner process and are automatically deleted when no jobs are running, reducing costs and administration overhead for your team.\n\nAs we’ll explain in this tutorial, you are in control of how many machines are created at any given time, as well as the length of time they’re retained before being destroyed.\n\nWe’ll be using three separate servers to build this project, so let’s go over terminology first:\n\n* **GitLab**: Your hosted GitLab instance or self-managed instance where your code repositories are stored.\n\n* **GitLab Bastion**: The *bastion* server or Droplet is the core of what we’ll be configuring. It is the control instance that is used to interact with the DigitalOcean API to create Droplets and destroy them when necessary. No jobs are executed on this server.\n\n* **GitLab Runner**: Your *runners* are transient servers or Droplets that are created on the fly by the *bastion* server when needed to execute a CI/CD job in your build queue. These servers are disposable, and are where your code is executed or tested before your build is marked as passing or failing.\n\n![GitLab Runners Diagram](https://assets.digitalocean.com/articles/gitlab-runner/Autoscaling-GitLab-Runners.png){: .medium.center}\n\nBy leveraging each of the GitLab components, the CI/CD process will enable you to scale responsively based on demands. With these goals in mind, we are ready to begin setting up our [continuous deployment](/topics/ci-cd/) with GitLab and DigitalOcean.\n\n## Prerequisites\n\nThis tutorial will assume you have already configured GitLab on your own server or through the hosted service, and that you have an existing DigitalOcean account.\n\nTo set this up on an Ubuntu 16.04 Droplet, you can use the DigitalOcean one-click image, or follow our guide: “[How To Install and Configure GitLab on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-install-and-configure-gitlab-on-ubuntu-16-04).”\n\nFor the purposes of this tutorial, we assume you have private networking enabled on this Droplet, which you can achieve by following our guide on “[How To Enable DigitalOcean Private Networking on Existing Droplets](https://www.digitalocean.com/community/tutorials/how-to-enable-digitalocean-private-networking-on-existing-droplets),” but it is not compulsory.\n\nThroughout this tutorial, we’ll be using non-root users with admin privileges on our Droplets.\n\n## Step 1: Import JavaScript project\nTo begin, we will create a new example project in your existing GitLab instance containing a sample Node.js application.\n\n![GitLab interface](https://assets.digitalocean.com/articles/gitlab-runner/gitlab.jpg){: .shadow.large.center}\n\nLog into your GitLab instance and click the **plus icon**, then select **New project** from the dropdown menu.\n\nOn the new project screen, select the **Import project** tag, then click **Repo by URL** to import our example project directly from GitHub.\n\nPaste the below clone URL into the Git repository URL:\n\n```bash\nhttps://github.com/do-community/hello_hapi.git\n```\n\nThis repository is a basic JavaScript application for the purposes of demonstration, which we won’t be running in production. To complete the import, click the **New Project** button.\n\nYour new project will now be in GitLab and we can get started setting up our CI pipeline.\n\n## Step 2: Set up infrastructure\n\nOur GitLab Code Runner requires specific configuration as we’re planning to programmatically create Droplets to handle CI load as it grows and shrinks.\n\nWe will create two types of machines in this tutorial: a **bastion** instance, which controls and spawns new machines, and our **runner** instances, which are temporary servers spawned by the bastion Droplet to build code when required. The bastion instance uses Docker to create your runners.\n\nHere are the DigitalOcean products we’ll use, and what each component is used for:\n\n* **Flexible Droplets** — We will create memory-optimized Droplets for our GitLab Runners as it’s a memory-intensive process which will run using Docker for containerization. You can shrink or grow this Droplet in the future as needed, however we recommend the flexible Droplet option as a starting point to understand how your pipeline will perform under load.\n\n* **DigitalOcean Spaces (Object Storage)** — We will use [DigitalOcean Spaces](https://www.digitalocean.com/products/spaces/) to persist cached build components across your runners as they’re created and destroyed. This reduces the time required to set up a new runner when the CI pipeline is busy, and allows new runners to pick up where others left off immediately.\n\n* **Private Networking** — We will create a private network for your bastion Droplet and GitLab runners to ensure secure code compilation and to reduce firewall configuration required.\n\nTo start, we’ll create the bastion Droplet. Create a [new Droplet](https://cloud.digitalocean.com/droplets/new), then under **choose an image**, select the **One-click apps** tab. From there, select **Docker 17.12.0-ce on 16.04** (note that this version is current at the time of writing), then choose the smallest Droplet size available, as our bastion Droplet will manage the creation of other Droplets rather than actually perform tests.\n\nIt is recommended that you create your server in a data center that includes  [DigitalOcean Spaces](https://www.digitalocean.com/community/tutorials/an-introduction-to-digitalocean-spaces) in order to use the object storage caching features mentioned earlier.\n\nSelect both the **Private networking** and **Monitoring** options, then click **Create Droplet**.\n\nWe also need to set up our storage space which will be used for caching. Follow the steps in “[How To Create a DigitalOcean Space and API Key](https://www.digitalocean.com/community/tutorials/how-to-create-a-digitalocean-space-and-api-key)” to create a new Space in the same or nearest data center as your hosted GitLab instance, along with an API Key.\n\nNote this key down, as we’ll need it later in the tutorial.\n\nNow it’s time to get our CI started!\n\n## Step 3: Configure the GitLab Runner Bastion Server\n\nWith the fresh Droplet ready, we can now configure GitLab Runner. We’ll be installing scripts from GitLab and GitHub repositories.\n\nAs a best practice, be sure to inspect scripts to confirm what you will be installing prior to running the full commands below.\n\nConnect to the Droplet using SSH, move into the `/tmp` directory, then add the [official GitLab Runner repository](https://docs.gitlab.com/runner/install/linux-repository.html) to Ubuntu’s package manager:\n\n```bash\ncd /tmp\ncurl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | sudo bash\n```\n\nOnce added, install the GitLab Runner application:\n\n```bash\nsudo apt-get install gitlab-runner\n```\n\nWe also need to install **[Docker Machine](https://docs.docker.com/machine/install-machine/#install-machine-directly)**, which is an additional Docker tool that assists with automating the deployment of containers on cloud providers:\n\n```bash\ncurl -L https://github.com/docker/machine/releases/download/v0.14.0/docker-machine-`uname -s`-`uname -m` >/tmp/docker-machine && \\\nsudo install /tmp/docker-machine /usr/local/bin/docker-machine\n```\n\nWith these installations complete, we can move on to connecting our GitLab Runner to our GitLab install.\n\n## Step 4: Obtain Runner registration token\n\nTo link GitLab Runner to your existing GitLab install, we need to link the two instances together by obtaining a token that authenticates your runner to your code repositories.\n\nLogin to your existing GitLab instance as the admin user, then click the wrench icon to enter the admin settings area.\n\nOn the left of your screen, hover over **Overview** and select **Runners** from the list that appears.\n\nOn the Runners page under the **How to set up a shared Runner for a new project** section, copy the token shown in Step 3, and make a note of it along with the publicly accessible URL of your GitLab instance from Step 2. If you are using HTTPS for Gitlab, make sure it is not a self-signed certificate, or GitLab Runner will fail to start.\n\n## Step 5: Configure GitLab on the Bastion Droplet\n\nBack in your SSH connection with your bastion Droplet, run the following command:\n\n```bash\nsudo gitlab-runner register\n```\n\nThis will initiate the linking process, and you will be asked a series of questions.\n\nOn the next step, enter the **GitLab instance URL** from the previous step:\n\n```bash\nPlease enter the gitlab-ci coordinator URL (e.g. https://gitlab.com)\nhttps://example.digitalocean.com\n```\n\nEnter the token you obtained from your GitLab instance:\n\n```bash\nPlease enter the gitlab-ci token for this runner\nsample-gitlab-ci-token\n```\n\nEnter a description that will help you recognize it in the GitLab web interface. We recommend naming this instance something unique, like `runner-bastion` for clarity.\n\n```bash\nPlease enter the gitlab-ci description for this runner\n[yourhostname] runner-bastion\n```\n\nIf relevant, you may enter the tags for code you will build with your runner. However, we recommend this is left blank at this stage. This can easily be changed from the GitLab interface later.\n\n```bash\nPlease enter the gitlab-ci tags for this runner (comma separated):\ncode-tag\n```\n\nChoose whether or not your runner should be able to run untagged jobs. This setting allows you to choose whether your runner should build repositories with no tags at all, or require specific tags. Select true in this case, so your runner can execute all repositories.\n\n```bash\nWhether to run untagged jobs [true/false]: true\n```\n\nChoose if this runner should be shared among your projects, or locked to the current one, which blocks it from building any code other than those specified. Select false for now, as this can be changed later in GitLab’s interface:\n\n```bash\nWhether to lock Runner to current project [true/false]: false\n```\n\nChoose the executor which will build your machines. Because we’ll be creating new Droplets using Docker, we’ll choose `docker+machine` here, but you can read more about the advantages of each approach in this [compatibility chart](https://docs.gitlab.com/runner/executors/README.html#compatibility-chart):\n\n```bash\nPlease enter the executor: ssh, docker+machine, docker-ssh+machine, kubernetes, docker, parallels, virtualbox, docker-ssh, shell:\ndocker+machine\n```\n\nYou’ll be asked which image to use for projects that don’t explicitly define one. We’ll choose a basic, secure default:\n\n```bash\nPlease enter the Docker image (e.g. ruby:2.1):\nalpine:latest\n```\n\nNow you’re done configuring the core bastion runner! At this point it should appear within the GitLab Runner page of your GitLab admin settings, which we accessed to obtain the token.\n\nIf you encounter any issues with these steps, the [GitLab Runner documentation](https://docs.gitlab.com/runner/register/index.html) includes options for troubleshooting.\n\n## Step 6: Configure Docker caching and Docker Machine\nTo speed up Droplet creation when the build queue is busy, we’ll leverage Docker’s caching tools on the Bastion Droplet to store the images for your commonly used containers on DigitalOcean Spaces.\n\nTo do so, upgrade Docker Machine on your SSH shell using the following command:\n\n```bash\ncurl -L https://github.com/docker/machine/releases/download/v0.14.0/docker-machine-`uname -s`-`uname -m` >/tmp/docker-machine && sudo install /tmp/docker-machine /usr/local/bin/docker-machine\n```\n\nWith Docker Machine upgraded, we can move on to setting up our access tokens for GitLab Runner to use.\n\n## Step 7: Gather DigitalOcean credentials\n\nNow we need to create the credentials that GitLab Runner will use to create new Droplets using your DigitalOcean account.\n\nVisit your DigitalOcean [dashboard](https://cloud.digitalocean.com) and click **API**. On the next screen, look for **Personal access tokens** and click **Generate New Token**.\n\nGive the new token a name you will recognize such as `GitLab Runner Access` and ensure that both the read and write scopes are enabled, as we need the Droplet to create new machines without human intervention.\n\nCopy the token somewhere safe as we’ll use it in the next step. You can’t retrieve this token again without regenerating it, so be sure it’s stored securely.\n\n## Step 8: Edit GitLab Runner configuration files\nTo bring all of these components together, we need to finish configuring our bastion Droplet to communicate with your DigitalOcean account.\n\nIn your SSH connection to your bastion Droplet, use your favorite text editor, such as nano, to open the GitLab Runner configuration file for editing:\n\n```bash\nnano /etc/gitlab-runner/config.toml\n```\n\nThis configuration file is responsible for the rules your CI setup uses to scale up and down on demand. To configure the bastion to autoscale on demand, you need to add the following lines:\n\n```bash\nconcurrent = 50   # All registered Runners can run up to 50 concurrent builds\n\n[[runners]]\n  url = \"https://example.digitalocean.com\"\n  token = \"existinggitlabtoken\"             # Note this is different from the registration token used by `gitlab-runner register`\n  name = \"example-runner\"\n  executor = \"docker+machine\"        # This Runner is using the 'docker+machine' executor\n  limit = 10                         # This Runner can execute up to 10 builds (created machines)\n  [runners.docker]\n    image = \"alpine:latest\"               # Our secure image\n  [runners.machine]\n    IdleCount = 1                    # The amount of idle machines we require for CI if build queue is empty\n    IdleTime = 600                   # Each machine can be idle for up to 600 seconds, then destroyed\n    MachineName = \"gitlab-runner-autoscale-%s\"    # Each machine will have a unique name ('%s' is required and generates a random number)\n    MachineDriver = \"digitalocean\"   # Docker Machine is using the 'digitalocean' driver\n    MachineOptions = [\n        \"digitalocean-image=coreos-stable\", # The DigitalOcean system image to use by default\n        \"digitalocean-ssh-user=core\", # The default SSH user\n        \"digitalocean-access-token=DO_ACCESS_TOKEN\", # Access token from Step 7\n        \"digitalocean-region=nyc3\", # The data center to spawn runners in\n        \"digitalocean-size=1gb\", # The size (and price category) of your spawned runners\n        \"digitalocean-private-networking\" # Enable private networking on runners\n    ]\n  [runners.cache]\n    Type = \"s3\"   # The Runner is using a distributed cache with the S3-compatible Spaces service\n    ServerAddress = \"nyc3.spaces.digitaloceanspaces.com\"\n    AccessKey = \"YOUR_SPACES_KEY\"\n    SecretKey = \"YOUR_SPACES_SECRET\"\n    BucketName = \"your_bucket_name\"\n    Insecure = true # We do not have a SSL certificate, as we are only running locally\n\n```\n\nOnce you’ve added the new lines, customize the access token, region and Droplet size based on  your setup. For the purposes of this tutorial, we’ve used the smallest Droplet size of 1GB and created our Droplets in NYC3. Be sure to use the information that is relevant in your case.\n\nYou also need to customize the cache component, and enter your Space’s server address from the infrastructure configuration step, access key, secret key and the name of the Space that you created.\n\nWhen completed, restart GitLab Runner to make sure the configuration is being used:\n\n```bash\ngitlab-runner restart\n```\n\nIf you would like to learn about more all available options, including off-peak hours, you can read [GitLab’s advanced documentation](https://docs.gitlab.com/runner/configuration/autoscale.html).\n\n## Step 9 — Test Your GitLab Runner\n\nAt this point, our GitLab Runner bastion Droplet is configured and is able to create DigitalOcean Droplets on demand, as the CI queue fills up. We’ll need to test it to be sure it works by heading to your GitLab instance and the project we imported in Step 1.\n\nTo trigger a build, edit the `readme.md` file by clicking on it, then clicking **edit**, and add any relevant testing text to the file, then click **Commit changes**.\n\nNow a build will be automatically triggered, which can be found under the project’s **CI/CD** option in the left navigation.\n\nOn this page you should see a pipeline entry with the status of **running**. In your DigitalOcean account, you’ll see a number of Droplets automatically created by GitLab Runner in order to build this change.\n\nCongratulations! Your CI pipeline is cloud scalable and now manages its own resource usage. After the specified idle time, the machines should be automatically destroyed, but we recommend verifying this manually to ensure you aren’t unexpectedly billed.\n\n## Troubleshooting\n\nIn some cases, GitLab may report that the runner is unreachable and as a result perform no actions, including deploying new runners. You can troubleshoot this by stopping GitLab Runner, then starting it again in debug mode:\n\n```bash\ngitlab-runner stop\ngitlab-runner --debug start\n```\n\nThe output should throw an error, which will be helpful in determining which configuration is causing the issue.\n\nIf your configuration creates too many machines, and you wish to remove them all at the same time, you can run this command to destroy them all:\n\n```bash\ndocker-machine rm $(docker-machine ls -q)\n```\nFor more troubleshooting steps and additional configuration options, you can refer to [GitLab’s documentation](https://docs.gitlab.com/runner/).\n\n## Conclusion\n\nYou've successfully set up an automated CI/CD pipeline using GitLab Runner and Docker. From here, you could configure higher levels of caching with Docker Registry to optimize performance or explore the use of tagging code builds to specific GitLab code runners.\n\nFor more on GitLab Runner, [see the detailed documentation](https://docs.gitlab.com/runner/), or to learn more, you can read [GitLab’s series of blog posts](https://docs.gitlab.com/ee/ci/) on how to make the most of your continuous integration pipeline.\n\n[This post was originally published by DigitalOcean](https://www.digitalocean.com/community/tutorials/how-to-autoscale-gitlab-continuous-deployment-with-gitlab-runner-on-digitalocean) and is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).\n",[23,24],"CI","integrations","yml",{},true,"/en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":30,"ogSiteName":31,"ogType":32,"canonicalUrls":30},"https://about.gitlab.com/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean","https://about.gitlab.com","article","en-us/blog/autoscale-continuous-deployment-gitlab-runner-digital-ocean",[35,24],"ci","vdKwTeQxBWZw8SON4BZ5j9qodFSgK27oy5MaQtGssWE",{"data":38},{"logo":39,"freeTrial":44,"sales":49,"login":54,"items":59,"search":366,"minimal":397,"duo":416,"switchNav":425,"pricingDeployment":436},{"config":40},{"href":41,"dataGaName":42,"dataGaLocation":43},"/","gitlab logo","header",{"text":45,"config":46},"Get free trial",{"href":47,"dataGaName":48,"dataGaLocation":43},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":50,"config":51},"Talk to sales",{"href":52,"dataGaName":53,"dataGaLocation":43},"/sales/","sales",{"text":55,"config":56},"Sign in",{"href":57,"dataGaName":58,"dataGaLocation":43},"https://gitlab.com/users/sign_in/","sign in",[60,87,182,187,287,347],{"text":61,"config":62,"cards":64},"Platform",{"dataNavLevelOne":63},"platform",[65,71,79],{"title":61,"description":66,"link":67},"The intelligent orchestration platform for DevSecOps",{"text":68,"config":69},"Explore our Platform",{"href":70,"dataGaName":63,"dataGaLocation":43},"/platform/",{"title":72,"description":73,"link":74},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":75,"config":76},"Meet GitLab Duo",{"href":77,"dataGaName":78,"dataGaLocation":43},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":80,"description":81,"link":82},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":83,"config":84},"Learn more",{"href":85,"dataGaName":86,"dataGaLocation":43},"/why-gitlab/","why gitlab",{"text":88,"left":27,"config":89,"link":91,"lists":95,"footer":164},"Product",{"dataNavLevelOne":90},"solutions",{"text":92,"config":93},"View all Solutions",{"href":94,"dataGaName":90,"dataGaLocation":43},"/solutions/",[96,120,143],{"title":97,"description":98,"link":99,"items":104},"Automation","CI/CD and automation to accelerate deployment",{"config":100},{"icon":101,"href":102,"dataGaName":103,"dataGaLocation":43},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[105,109,112,116],{"text":106,"config":107},"CI/CD",{"href":108,"dataGaLocation":43,"dataGaName":106},"/solutions/continuous-integration/",{"text":72,"config":110},{"href":77,"dataGaLocation":43,"dataGaName":111},"gitlab duo agent platform - product menu",{"text":113,"config":114},"Source Code Management",{"href":115,"dataGaLocation":43,"dataGaName":113},"/solutions/source-code-management/",{"text":117,"config":118},"Automated Software Delivery",{"href":102,"dataGaLocation":43,"dataGaName":119},"Automated software delivery",{"title":121,"description":122,"link":123,"items":128},"Security","Deliver code faster without compromising security",{"config":124},{"href":125,"dataGaName":126,"dataGaLocation":43,"icon":127},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[129,133,138],{"text":130,"config":131},"Application Security Testing",{"href":125,"dataGaName":132,"dataGaLocation":43},"Application security testing",{"text":134,"config":135},"Software Supply Chain Security",{"href":136,"dataGaLocation":43,"dataGaName":137},"/solutions/supply-chain/","Software supply chain security",{"text":139,"config":140},"Software Compliance",{"href":141,"dataGaName":142,"dataGaLocation":43},"/solutions/software-compliance/","software compliance",{"title":144,"link":145,"items":150},"Measurement",{"config":146},{"icon":147,"href":148,"dataGaName":149,"dataGaLocation":43},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[151,155,159],{"text":152,"config":153},"Visibility & Measurement",{"href":148,"dataGaLocation":43,"dataGaName":154},"Visibility and Measurement",{"text":156,"config":157},"Value Stream Management",{"href":158,"dataGaLocation":43,"dataGaName":156},"/solutions/value-stream-management/",{"text":160,"config":161},"Analytics & Insights",{"href":162,"dataGaLocation":43,"dataGaName":163},"/solutions/analytics-and-insights/","Analytics and insights",{"title":165,"items":166},"GitLab for",[167,172,177],{"text":168,"config":169},"Enterprise",{"href":170,"dataGaLocation":43,"dataGaName":171},"/enterprise/","enterprise",{"text":173,"config":174},"Small Business",{"href":175,"dataGaLocation":43,"dataGaName":176},"/small-business/","small business",{"text":178,"config":179},"Public Sector",{"href":180,"dataGaLocation":43,"dataGaName":181},"/solutions/public-sector/","public sector",{"text":183,"config":184},"Pricing",{"href":185,"dataGaName":186,"dataGaLocation":43,"dataNavLevelOne":186},"/pricing/","pricing",{"text":188,"config":189,"link":191,"lists":195,"feature":274},"Resources",{"dataNavLevelOne":190},"resources",{"text":192,"config":193},"View all resources",{"href":194,"dataGaName":190,"dataGaLocation":43},"/resources/",[196,228,246],{"title":197,"items":198},"Getting started",[199,204,209,214,219,224],{"text":200,"config":201},"Install",{"href":202,"dataGaName":203,"dataGaLocation":43},"/install/","install",{"text":205,"config":206},"Quick start guides",{"href":207,"dataGaName":208,"dataGaLocation":43},"/get-started/","quick setup checklists",{"text":210,"config":211},"Learn",{"href":212,"dataGaLocation":43,"dataGaName":213},"https://university.gitlab.com/","learn",{"text":215,"config":216},"Product documentation",{"href":217,"dataGaName":218,"dataGaLocation":43},"https://docs.gitlab.com/","product documentation",{"text":220,"config":221},"Best practice videos",{"href":222,"dataGaName":223,"dataGaLocation":43},"/getting-started-videos/","best practice videos",{"text":225,"config":226},"Integrations",{"href":227,"dataGaName":24,"dataGaLocation":43},"/integrations/",{"title":229,"items":230},"Discover",[231,236,241],{"text":232,"config":233},"Customer success stories",{"href":234,"dataGaName":235,"dataGaLocation":43},"/customers/","customer success stories",{"text":237,"config":238},"Blog",{"href":239,"dataGaName":240,"dataGaLocation":43},"/blog/","blog",{"text":242,"config":243},"Remote",{"href":244,"dataGaName":245,"dataGaLocation":43},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":247,"items":248},"Connect",[249,254,259,264,269],{"text":250,"config":251},"GitLab Services",{"href":252,"dataGaName":253,"dataGaLocation":43},"/services/","services",{"text":255,"config":256},"Community",{"href":257,"dataGaName":258,"dataGaLocation":43},"/community/","community",{"text":260,"config":261},"Forum",{"href":262,"dataGaName":263,"dataGaLocation":43},"https://forum.gitlab.com/","forum",{"text":265,"config":266},"Events",{"href":267,"dataGaName":268,"dataGaLocation":43},"/events/","events",{"text":270,"config":271},"Partners",{"href":272,"dataGaName":273,"dataGaLocation":43},"/partners/","partners",{"backgroundColor":275,"textColor":276,"text":277,"image":278,"link":282},"#2f2a6b","#fff","Insights for the future of software development",{"altText":279,"config":280},"the source promo card",{"src":281},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":283,"config":284},"Read the latest",{"href":285,"dataGaName":286,"dataGaLocation":43},"/the-source/","the source",{"text":288,"config":289,"lists":291},"Company",{"dataNavLevelOne":290},"company",[292],{"items":293},[294,299,305,307,312,317,322,327,332,337,342],{"text":295,"config":296},"About",{"href":297,"dataGaName":298,"dataGaLocation":43},"/company/","about",{"text":300,"config":301,"footerGa":304},"Jobs",{"href":302,"dataGaName":303,"dataGaLocation":43},"/jobs/","jobs",{"dataGaName":303},{"text":265,"config":306},{"href":267,"dataGaName":268,"dataGaLocation":43},{"text":308,"config":309},"Leadership",{"href":310,"dataGaName":311,"dataGaLocation":43},"/company/team/e-group/","leadership",{"text":313,"config":314},"Team",{"href":315,"dataGaName":316,"dataGaLocation":43},"/company/team/","team",{"text":318,"config":319},"Handbook",{"href":320,"dataGaName":321,"dataGaLocation":43},"https://handbook.gitlab.com/","handbook",{"text":323,"config":324},"Investor relations",{"href":325,"dataGaName":326,"dataGaLocation":43},"https://ir.gitlab.com/","investor relations",{"text":328,"config":329},"Trust Center",{"href":330,"dataGaName":331,"dataGaLocation":43},"/security/","trust center",{"text":333,"config":334},"AI Transparency Center",{"href":335,"dataGaName":336,"dataGaLocation":43},"/ai-transparency-center/","ai transparency center",{"text":338,"config":339},"Newsletter",{"href":340,"dataGaName":341,"dataGaLocation":43},"/company/contact/#contact-forms","newsletter",{"text":343,"config":344},"Press",{"href":345,"dataGaName":346,"dataGaLocation":43},"/press/","press",{"text":348,"config":349,"lists":350},"Contact us",{"dataNavLevelOne":290},[351],{"items":352},[353,356,361],{"text":50,"config":354},{"href":52,"dataGaName":355,"dataGaLocation":43},"talk to sales",{"text":357,"config":358},"Support portal",{"href":359,"dataGaName":360,"dataGaLocation":43},"https://support.gitlab.com","support portal",{"text":362,"config":363},"Customer portal",{"href":364,"dataGaName":365,"dataGaLocation":43},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":367,"login":368,"suggestions":375},"Close",{"text":369,"link":370},"To search repositories and projects, login to",{"text":371,"config":372},"gitlab.com",{"href":57,"dataGaName":373,"dataGaLocation":374},"search login","search",{"text":376,"default":377},"Suggestions",[378,380,384,386,390,394],{"text":72,"config":379},{"href":77,"dataGaName":72,"dataGaLocation":374},{"text":381,"config":382},"Code Suggestions (AI)",{"href":383,"dataGaName":381,"dataGaLocation":374},"/solutions/code-suggestions/",{"text":106,"config":385},{"href":108,"dataGaName":106,"dataGaLocation":374},{"text":387,"config":388},"GitLab on AWS",{"href":389,"dataGaName":387,"dataGaLocation":374},"/partners/technology-partners/aws/",{"text":391,"config":392},"GitLab on Google Cloud",{"href":393,"dataGaName":391,"dataGaLocation":374},"/partners/technology-partners/google-cloud-platform/",{"text":395,"config":396},"Why GitLab?",{"href":85,"dataGaName":395,"dataGaLocation":374},{"freeTrial":398,"mobileIcon":403,"desktopIcon":408,"secondaryButton":411},{"text":399,"config":400},"Start free trial",{"href":401,"dataGaName":48,"dataGaLocation":402},"https://gitlab.com/-/trials/new/","nav",{"altText":404,"config":405},"Gitlab Icon",{"src":406,"dataGaName":407,"dataGaLocation":402},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":404,"config":409},{"src":410,"dataGaName":407,"dataGaLocation":402},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":412,"config":413},"Get Started",{"href":414,"dataGaName":415,"dataGaLocation":402},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/get-started/","get started",{"freeTrial":417,"mobileIcon":421,"desktopIcon":423},{"text":418,"config":419},"Learn more about GitLab Duo",{"href":77,"dataGaName":420,"dataGaLocation":402},"gitlab duo",{"altText":404,"config":422},{"src":406,"dataGaName":407,"dataGaLocation":402},{"altText":404,"config":424},{"src":410,"dataGaName":407,"dataGaLocation":402},{"button":426,"mobileIcon":431,"desktopIcon":433},{"text":427,"config":428},"/switch",{"href":429,"dataGaName":430,"dataGaLocation":402},"#contact","switch",{"altText":404,"config":432},{"src":406,"dataGaName":407,"dataGaLocation":402},{"altText":404,"config":434},{"src":435,"dataGaName":407,"dataGaLocation":402},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1773335277/ohhpiuoxoldryzrnhfrh.png",{"freeTrial":437,"mobileIcon":442,"desktopIcon":444},{"text":438,"config":439},"Back to pricing",{"href":185,"dataGaName":440,"dataGaLocation":402,"icon":441},"back to pricing","GoBack",{"altText":404,"config":443},{"src":406,"dataGaName":407,"dataGaLocation":402},{"altText":404,"config":445},{"src":410,"dataGaName":407,"dataGaLocation":402},{"title":447,"button":448,"config":453},"See how agentic AI transforms software delivery",{"text":449,"config":450},"Watch GitLab Transcend now",{"href":451,"dataGaName":452,"dataGaLocation":43},"/events/transcend/virtual/","transcend event",{"layout":454,"icon":455,"disabled":27},"release","AiStar",{"data":457},{"text":458,"source":459,"edit":465,"contribute":470,"config":475,"items":480,"minimal":687},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":460,"config":461},"View page source",{"href":462,"dataGaName":463,"dataGaLocation":464},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":466,"config":467},"Edit this page",{"href":468,"dataGaName":469,"dataGaLocation":464},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":471,"config":472},"Please contribute",{"href":473,"dataGaName":474,"dataGaLocation":464},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":476,"facebook":477,"youtube":478,"linkedin":479},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[481,528,582,626,653],{"title":183,"links":482,"subMenu":497},[483,487,492],{"text":484,"config":485},"View plans",{"href":185,"dataGaName":486,"dataGaLocation":464},"view plans",{"text":488,"config":489},"Why Premium?",{"href":490,"dataGaName":491,"dataGaLocation":464},"/pricing/premium/","why premium",{"text":493,"config":494},"Why Ultimate?",{"href":495,"dataGaName":496,"dataGaLocation":464},"/pricing/ultimate/","why ultimate",[498],{"title":499,"links":500},"Contact Us",[501,504,506,508,513,518,523],{"text":502,"config":503},"Contact sales",{"href":52,"dataGaName":53,"dataGaLocation":464},{"text":357,"config":505},{"href":359,"dataGaName":360,"dataGaLocation":464},{"text":362,"config":507},{"href":364,"dataGaName":365,"dataGaLocation":464},{"text":509,"config":510},"Status",{"href":511,"dataGaName":512,"dataGaLocation":464},"https://status.gitlab.com/","status",{"text":514,"config":515},"Terms of use",{"href":516,"dataGaName":517,"dataGaLocation":464},"/terms/","terms of use",{"text":519,"config":520},"Privacy statement",{"href":521,"dataGaName":522,"dataGaLocation":464},"/privacy/","privacy statement",{"text":524,"config":525},"Cookie preferences",{"dataGaName":526,"dataGaLocation":464,"id":527,"isOneTrustButton":27},"cookie preferences","ot-sdk-btn",{"title":88,"links":529,"subMenu":538},[530,534],{"text":531,"config":532},"DevSecOps platform",{"href":70,"dataGaName":533,"dataGaLocation":464},"devsecops platform",{"text":535,"config":536},"AI-Assisted Development",{"href":77,"dataGaName":537,"dataGaLocation":464},"ai-assisted development",[539],{"title":540,"links":541},"Topics",[542,547,552,557,562,567,572,577],{"text":543,"config":544},"CICD",{"href":545,"dataGaName":546,"dataGaLocation":464},"/topics/ci-cd/","cicd",{"text":548,"config":549},"GitOps",{"href":550,"dataGaName":551,"dataGaLocation":464},"/topics/gitops/","gitops",{"text":553,"config":554},"DevOps",{"href":555,"dataGaName":556,"dataGaLocation":464},"/topics/devops/","devops",{"text":558,"config":559},"Version Control",{"href":560,"dataGaName":561,"dataGaLocation":464},"/topics/version-control/","version control",{"text":563,"config":564},"DevSecOps",{"href":565,"dataGaName":566,"dataGaLocation":464},"/topics/devsecops/","devsecops",{"text":568,"config":569},"Cloud Native",{"href":570,"dataGaName":571,"dataGaLocation":464},"/topics/cloud-native/","cloud native",{"text":573,"config":574},"AI for Coding",{"href":575,"dataGaName":576,"dataGaLocation":464},"/topics/devops/ai-for-coding/","ai for coding",{"text":578,"config":579},"Agentic AI",{"href":580,"dataGaName":581,"dataGaLocation":464},"/topics/agentic-ai/","agentic ai",{"title":583,"links":584},"Solutions",[585,587,589,594,598,601,605,608,610,613,616,621],{"text":130,"config":586},{"href":125,"dataGaName":130,"dataGaLocation":464},{"text":119,"config":588},{"href":102,"dataGaName":103,"dataGaLocation":464},{"text":590,"config":591},"Agile development",{"href":592,"dataGaName":593,"dataGaLocation":464},"/solutions/agile-delivery/","agile delivery",{"text":595,"config":596},"SCM",{"href":115,"dataGaName":597,"dataGaLocation":464},"source code management",{"text":543,"config":599},{"href":108,"dataGaName":600,"dataGaLocation":464},"continuous integration & delivery",{"text":602,"config":603},"Value stream management",{"href":158,"dataGaName":604,"dataGaLocation":464},"value stream management",{"text":548,"config":606},{"href":607,"dataGaName":551,"dataGaLocation":464},"/solutions/gitops/",{"text":168,"config":609},{"href":170,"dataGaName":171,"dataGaLocation":464},{"text":611,"config":612},"Small business",{"href":175,"dataGaName":176,"dataGaLocation":464},{"text":614,"config":615},"Public sector",{"href":180,"dataGaName":181,"dataGaLocation":464},{"text":617,"config":618},"Education",{"href":619,"dataGaName":620,"dataGaLocation":464},"/solutions/education/","education",{"text":622,"config":623},"Financial services",{"href":624,"dataGaName":625,"dataGaLocation":464},"/solutions/finance/","financial services",{"title":188,"links":627},[628,630,632,634,637,639,641,643,645,647,649,651],{"text":200,"config":629},{"href":202,"dataGaName":203,"dataGaLocation":464},{"text":205,"config":631},{"href":207,"dataGaName":208,"dataGaLocation":464},{"text":210,"config":633},{"href":212,"dataGaName":213,"dataGaLocation":464},{"text":215,"config":635},{"href":217,"dataGaName":636,"dataGaLocation":464},"docs",{"text":237,"config":638},{"href":239,"dataGaName":240,"dataGaLocation":464},{"text":232,"config":640},{"href":234,"dataGaName":235,"dataGaLocation":464},{"text":242,"config":642},{"href":244,"dataGaName":245,"dataGaLocation":464},{"text":250,"config":644},{"href":252,"dataGaName":253,"dataGaLocation":464},{"text":255,"config":646},{"href":257,"dataGaName":258,"dataGaLocation":464},{"text":260,"config":648},{"href":262,"dataGaName":263,"dataGaLocation":464},{"text":265,"config":650},{"href":267,"dataGaName":268,"dataGaLocation":464},{"text":270,"config":652},{"href":272,"dataGaName":273,"dataGaLocation":464},{"title":288,"links":654},[655,657,659,661,663,665,667,671,676,678,680,682],{"text":295,"config":656},{"href":297,"dataGaName":290,"dataGaLocation":464},{"text":300,"config":658},{"href":302,"dataGaName":303,"dataGaLocation":464},{"text":308,"config":660},{"href":310,"dataGaName":311,"dataGaLocation":464},{"text":313,"config":662},{"href":315,"dataGaName":316,"dataGaLocation":464},{"text":318,"config":664},{"href":320,"dataGaName":321,"dataGaLocation":464},{"text":323,"config":666},{"href":325,"dataGaName":326,"dataGaLocation":464},{"text":668,"config":669},"Sustainability",{"href":670,"dataGaName":668,"dataGaLocation":464},"/sustainability/",{"text":672,"config":673},"Diversity, inclusion and belonging (DIB)",{"href":674,"dataGaName":675,"dataGaLocation":464},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":328,"config":677},{"href":330,"dataGaName":331,"dataGaLocation":464},{"text":338,"config":679},{"href":340,"dataGaName":341,"dataGaLocation":464},{"text":343,"config":681},{"href":345,"dataGaName":346,"dataGaLocation":464},{"text":683,"config":684},"Modern Slavery Transparency Statement",{"href":685,"dataGaName":686,"dataGaLocation":464},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":688},[689,692,695],{"text":690,"config":691},"Terms",{"href":516,"dataGaName":517,"dataGaLocation":464},{"text":693,"config":694},"Cookies",{"dataGaName":526,"dataGaLocation":464,"id":527,"isOneTrustButton":27},{"text":696,"config":697},"Privacy",{"href":521,"dataGaName":522,"dataGaLocation":464},[699],{"id":700,"title":18,"body":8,"config":701,"content":703,"description":8,"extension":25,"meta":707,"navigation":27,"path":708,"seo":709,"stem":710,"__hash__":711},"blogAuthors/en-us/blog/authors/owen-williams.yml",{"template":702},"BlogAuthor",{"name":18,"config":704},{"headshot":705,"ctfId":706},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","Owen-Williams",{},"/en-us/blog/authors/owen-williams",{},"en-us/blog/authors/owen-williams","13I935syOPTojvVRkZ-Xc4gV6wLKORSEFbKGKmYQw7s",[713,728,741],{"content":714,"config":726},{"body":715,"title":716,"description":717,"authors":718,"heroImage":720,"date":721,"category":9,"tags":722},"Most CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[719],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[106,723,724,725],"DevOps platform","tutorial","features",{"featured":27,"template":13,"slug":727},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":729,"config":739},{"title":730,"description":731,"authors":732,"heroImage":734,"date":735,"body":736,"category":9,"tags":737},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[733],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[724,738,725],"product",{"featured":12,"template":13,"slug":740},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":742,"config":752},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"category":9,"tags":749,"body":751},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[746],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[258,620,750],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":753,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":755},[756,770,781,793],{"id":757,"categories":758,"header":760,"text":761,"button":762,"image":767},"ai-modernization",[759],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":763,"config":764},"Get your AI maturity score",{"href":765,"dataGaName":766,"dataGaLocation":240},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":771,"categories":772,"header":773,"text":761,"button":774,"image":778},"devops-modernization",[738,566],"Are you just managing tools or shipping innovation?",{"text":775,"config":776},"Get your DevOps maturity score",{"href":777,"dataGaName":766,"dataGaLocation":240},"/assessments/devops-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":782,"categories":783,"header":785,"text":761,"button":786,"image":790},"security-modernization",[784],"security","Are you trading speed for security?",{"text":787,"config":788},"Get your security maturity score",{"href":789,"dataGaName":766,"dataGaLocation":240},"/assessments/security-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":794,"paths":795,"header":798,"text":799,"button":800,"image":805},"github-azure-migration",[796,797],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":801,"config":802},"See how GitLab compares to GitHub",{"href":803,"dataGaName":804,"dataGaLocation":240},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":806},{"src":780},{"header":808,"blurb":809,"button":810,"secondaryButton":815},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":811,"config":812},"Get your free trial",{"href":813,"dataGaName":48,"dataGaLocation":814},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":502,"config":816},{"href":52,"dataGaName":53,"dataGaLocation":814},1776454388512]