[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/automating-boring-git-operations-gitlab-ci":3,"navigation-en-us":39,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Kristian Larsson":699,"blog-related-posts-en-us-automating-boring-git-operations-gitlab-ci":713,"blog-promotions-en-us":755,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/automating-boring-git-operations-gitlab-ci.yml","Automating Boring Git Operations Gitlab Ci",[7],"kristian-larsson",null,"engineering",{"slug":11,"featured":12,"template":13},"automating-boring-git-operations-gitlab-ci",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitBot – automating boring Git operations with CI","Guest author Kristian Larsson shares how he automates some common Git operations, like rebase, using GitLab CI.",[18],"Kristian Larsson","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672374/Blog/Hero%20Images/gitbot-automate-git-operations.jpg","2017-11-02","Git is super useful for anyone doing a bit of development work or just trying to\nkeep track of a bunch of text files. However, as your project grows you might\nfind yourself doing lots of boring repetitive work just around Git itself. At\nleast that’s what happened to me and so I automated some boring Git stuff using our\n[continuous integration (CI) system](/solutions/continuous-integration/).\n\n\u003C!-- more -->\n\nThere are probably all sorts of use cases for automating various Git operations\nbut I’ll talk about a few that I’ve encountered. We’re using GitLab and [GitLab\nCI](/solutions/continuous-integration/) so that’s what my examples\nwill include, but most of the concepts should apply to other systems as well.\n\n## Automatic rebase\n\nWe have some Git repos with source code that we receive from vendors, who we can think\nof as our `upstream`. We don’t actually share a Git repo with the vendor but\nrather we get a tar ball every now and then. The tar ball is extracted into a\nGit repository, on the `master` branch which thus tracks the software as it is\nreceived from upstream. In a perfect world the software we receive would be\nfeature complete and bug free and so we would be done, but that’s usually not\nthe case. We do find bugs and if they are blocking we might decide to implement\na patch to fix them ourselves. The same is true for new features where we might\nnot want to wait for the vendor to implement it.\n\nThe result is that we have some local patches to apply. We commit such patches\nto a separate branch, commonly named `ts` (for TeraStream), to keep them\nseparate from the official software. Whenever a new software version is released,\nwe extract its content to `master` and then rebase our `ts` branch onto `master`\nso we get all the new official features together with our patches. Once we’ve\nimplemented something we usually send it upstream to the vendor for inclusion.\nSometimes they include our patches verbatim so that the next version of the code\nwill include our exact patch, in which case a rebase will simply skip our patch.\nOther times there are slight or major (it might be a completely different design)\nchanges to the patch and then someone typically needs to sort out the patches\nmanually. Mostly though, rebasing works just fine and we don’t end up with conflicts.\n\nNow, this whole rebasing process gets a tad boring and repetitive after a while,\nespecially considering we have a dozen of repositories with the setup described\nabove. What I recently did was to automate this using our CI system.\n\nThe workflow thus looks like:\n\n- human extracts zip file, git add + git commit on master + git push\n- CI runs for `master` branch\n   - clones a copy of itself into a new working directory\n   - checks out `ts` branch (the one with our patches) in working directory\n   - rebases `ts` onto `master`\n   - push `ts` back to `origin`\n- this event will now trigger a CI build for the `ts` branch\n- when CI runs for the `ts` branch, it will compile, test and save the binary output as “build artifacts”, which can be included in other repositories\n- GitLab CI, which is what we use, has a CI_PIPELINE_ID that we use to version built container images or artifacts\n\nTo do this, all you need is a few lines in a .gitlab-ci.yml file, essentially;\n\n```text\nstages:\n  - build\n  - git-robot\n\n... build jobs ...\n\ngit-rebase-ts:\n  stage: git-robot\n  only:\n    - master\n  allow_failure: true\n  before_script:\n    - 'which ssh-agent || ( apt-get update -y && apt-get install openssh-client -y )'\n    - eval $(ssh-agent -s)\n    - ssh-add \u003C(echo \"$GIT_SSH_PRIV_KEY\")\n    - git config --global user.email \"kll@dev.terastrm.net\"\n    - git config --global user.name \"Mr. Robot\"\n    - mkdir -p ~/.ssh\n    - cat gitlab-known-hosts >> ~/.ssh/known_hosts\n  script:\n    - git clone git@gitlab.dev.terastrm.net:${CI_PROJECT_PATH}.git\n    - cd ${CI_PROJECT_NAME}\n    - git checkout ts\n    - git rebase master\n    - git push --force origin ts\n  ```\n\nWe’ll go through the Yaml file a few lines at a time. Some basic knowledge about GitLab CI is assumed.\n\nThis first part lists the stages of our pipeline.\n```yaml\nstages:\n  - build\n  - git-robot\n  ```\n\nWe have two stages, first the `build` stage, which does whatever you want it to\ndo (ours compiles stuff, runs a few unit tests and packages it all up), then the\n`git-robot` stage which is where we perform the rebase.\n\nThen there’s:\n\n```yaml\ngit-rebase-ts:\n  stage: git-robot\n  only:\n    - master\n  allow_failure: true\n  ```\n\nWe define the stage in which we run followed by the only statement which limits\nCI jobs to run only on the specified branch(es), in this case `master`.\n\n`allow_failure` simply allows the CI job to fail but still passing the pipeline.\n\nSince we are going to clone a copy of ourselves (the repository checked out in\nCI) we need SSH and SSH keys set up. We’ll use ssh-agent with a password-less key\nto authenticate. Generate a key using ssh-keygen, for example:\n```text\nssh-keygen\n\nkll@machine ~ $ ssh-keygen -f foo\nGenerating public/private rsa key pair.\nEnter passphrase (empty for no passphrase):\nEnter same passphrase again:\nYour identification has been saved in foo.\nYour public key has been saved in foo.pub.\nThe key fingerprint is:\nSHA256:6s15MZJ1/kUsDU/PF2WwRGA963m6ZSwHvEJJdsRzmaA kll@machine\nThe key's randomart image is:\n+---[RSA 2048]----+\n|            o**.*|\n|           ..o**o|\n|           Eo o%o|\n|          .o.+o O|\n|        So oo.o+.|\n|       .o o.. o+o|\n|      .  . o..o+=|\n|     . o ..  .o= |\n|      . +.    .. |\n+----[SHA256]-----+\nkll@machine ~ $\n```\nAdd the public key as a deploy key under Project Settings\n\u003Ci class=\"fas fa-arrow-right\" aria-hidden=\"true\">\u003C/i> Repository \u003Ci class=\"fas fa-arrow-right\" aria-hidden=\"true\">\u003C/i>\nDeploy Keys. Make sure you enable write access or you won’t be able to have your\nGit robot push commits. We then need to hand over the private key so that it can\nbe accessed from within the CI job. We’ll use a secret environment variable for\nthat, which you can define under Project Settings\n\u003Ci class=\"fas fa-arrow-right\" aria-hidden=\"true\">\u003C/i> Pipelines \u003Ci class=\"fas fa-arrow-right\" aria-hidden=\"true\">\u003C/i>\nEnvironment variables). I’ll use the environment variable GIT_SSH_PRIV_KEY for this.\n\nNext part is the before_script:\n```markdown\n  before_script:\n    - 'which ssh-agent || ( apt-get update -y && apt-get install openssh-client -y )'\n    - eval $(ssh-agent -s)\n    - ssh-add \u003C(echo \"$GIT_SSH_PRIV_KEY\")\n    - git config --global user.email \"kll@dev.terastrm.net\"\n    - git config --global user.name \"Mr. Robot\"\n    - mkdir -p ~/.ssh\n    - cat gitlab-known-hosts >> ~/.ssh/known_hosts\n  ```\n\nFirst ssh-agent is installed if it isn’t already. We then start up ssh-agent and\nadd the key stored in the environment variable GIT_SSH_PRIV_KEY (which we set up\npreviously). The Git user information is set and we finally create .ssh and add\nthe known host information about our GitLab server to our known_hosts file. You\ncan generate the gitlab-known-hosts file using the following command:\n\n```text\nssh-keyscan my-gitlab-machine >> gitlab-known-hosts\n```\n\nAs the name implies, the before_script is run before the main `script` part and\nthe ssh-agent we started in the before_script will also continue to run for the\nduration of the job. The ssh-agent information is stored in some environment\nvariables which are carried across from the before_script into the main script,\nenabling it to work. It’s also possible to put this SSH setup in the main script,\nI just thought it looked cleaner splitting it up between before_script and script.\nNote however that it appears that after_script behaves differently so while it’s\npossible to pass environment vars from before_script to script, they do not\nappear to be passed to after_script. Thus, if you want to do Git magic in the\nafter_script you also need to perform the SSH setup in the after_script.\n\nThis brings us to the main script. In GitLab CI we already have a checked-out\nclone of our project but that was automatically checked out by the CI system\nthrough the use of magic (it actually happens in a container previous to the one\nwe are operating in, that has some special credentials) so we can’t really use\nit, besides, checking out other branches and stuff would be really weird as it\ndisrupts the code we are using to do this, since that’s available in the Git\nrepository that’s checked out. It’s all rather meta.\n\nAnyway, we’ll be checking out a new Git repository where we’ll do our work, then\nchange the current directory to the newly checked-out repository, after which\nwe’ll check out the `ts` branch, do the rebase and push it back to the origin remote.\n\n```markdown\n\n    - git clone git@gitlab.dev.terastrm.net:${CI_PROJECT_PATH}.git\n    - cd ${CI_PROJECT_NAME}\n    - git checkout ts\n    - git rebase master\n    - git push --force origin ts\n  ```\n\n… and that’s it. We’ve now automated the rebasing of a branch in our config file. Occasionally it\nwill fail due to problems rebasing (most commonly merge conflicts) but then you\ncan just step in and do the above steps manually and be interactively prompted\non how to handle conflicts.\n\n## Automatic merge requests\n\nAll the repositories I mentioned in the previous section are NEDs, a form of\ndriver for how to communicate with a certain type of device, for Cisco NSO (a\nnetwork orchestration system). We package up Cisco NSO, together with these NEDs\nand our own service code, in a container image. The build of that image is\nperformed in CI and we use a repository called `nso-ts` to control that work.\n\nThe NEDs are compiled in CI from their own repository and the binaries are saved\nas build artifacts. Those artifacts can then be pulled in the CI build of `nso-ts`.\nThe reference to which artifact to include is the name of the NED as well as the\nbuild version. The version number of the NED is nothing more than the pipeline\nid (which you’ll access in CI as ${CI_PIPELINE_ID}) and by including a specific\nversion of the NED, rather than just use “latest” we gain a much more consistent\nand reproducible build.\n\nWhenever a NED is updated a new build is run that produces new binary artifacts.\nWe probably want to use the new version but not before we test it out in CI. The\nactual versions of NEDs to use is stored in a file in the `nso-ts` repository and\nfollows a simple format, like this:\n```text\nned-iosxr-yang=1234\nned-junos-yang=4567\n...\n```\nThus, updating the version to use is a simple job to just rewrite this text file\nand replace the version number with a given CI_PIPELINE_ID version number. Again,\nwhile NED updates are more seldom than updates to `nso-ts`, they do occur and\nhandling it is bloody boring. Enter automation!\n```text\ngit-open-mr:\n  image: gitlab.dev.terastrm.net:4567/terastream/cisco-nso/ci-cisco-nso:4.2.3\n  stage: git-robot\n  only:\n    - ts\n  tags:\n    - no-docker\n  allow_failure: true\n  before_script:\n    - 'which ssh-agent || ( apt-get update -y && apt-get install openssh-client -y )'\n    - eval $(ssh-agent -s)\n    - ssh-add \u003C(echo \"$GIT_SSH_PRIV_KEY\")\n    - git config --global user.email \"kll@dev.terastrm.net\"\n    - git config --global user.name \"Mr. Robot\"\n    - mkdir -p ~/.ssh\n    - cat gitlab-known-hosts >> ~/.ssh/known_hosts\n  script:\n    - git clone git@gitlab.dev.terastrm.net:TeraStream/nso-ts.git\n    - cd nso-ts\n    - git checkout -b robot-update-${CI_PROJECT_NAME}-${CI_PIPELINE_ID}\n    - for LIST_FILE in $(ls ../ned-package-list.* | xargs -n1 basename); do NED_BUILD=$(cat ../${LIST_FILE}); sed -i packages/${LIST_FILE} -e \"s/^${CI_PROJECT_NAME}.*/${CI_PROJECT_NAME}=${NED_BUILD}/\"; done\n    - git diff\n    - git commit -a -m \"Use ${CI_PROJECT_NAME} artifacts from pipeline ${CI_PIPELINE_ID}\"\n    - git push origin robot-update-${CI_PROJECT_NAME}-${CI_PIPELINE_ID}\n    - HOST=${CI_PROJECT_URL} CI_COMMIT_REF_NAME=robot-update-${CI_PROJECT_NAME}-${CI_PIPELINE_ID} CI_PROJECT_NAME=TeraStream/nso-ts GITLAB_USER_ID=${GITLAB_USER_ID} PRIVATE_TOKEN=${PRIVATE_TOKEN} ../open-mr.sh\n```\n\nSo this time around we check out a Git repository into a separate working\ndirectory again, it’s just that it’s not the same Git repository as we are\nrunning on simply because we are trying to do changes to a repository that is\nusing the output of the repository we are running on. It doesn’t make much of a\ndifference in terms of our process. At the end, once we’ve modified the files we\nare interested in, we also open up a merge request on the target repository.\nHere we can see the MR (which is merged already) to use a new version of the\nNED `ned-snabbaftr-yang`.\n\n\u003Cimg src=\"https://about.gitlab.com/images/blogimages/gitbot-ned-update-mr.png\" alt=\"MR using new version of NED\" style=\"width: 700px;\"/>\n\nWhat we end up with is that whenever there is a new version of a NED, a single merge\nrequest is opened on our `nso-ts` repository to start using the new NED. That\nmerge request is using changes on a new branch and CI will obviously run for\n`nso-ts` on this new branch, which will then test all of our code using the new\nversion of the NED. We get a form of version pinning, with the form of explicit\nchanges that it entails, yet it’s a rather convenient and non-cumbersome\nenvironment to work with thanks to all the automation.\n\n## Getting fancy\n\nWhile automatically opening an MR is sweet… we can do ~~better~~fancier. Our `nso-ts`\nrepository is based on Cisco NSO (Tail-F NCS), or actually the `nso-ts` Docker\nimage is based on a `cisco-nso` Docker image that we build in a separate\nrepository. We put the version of NSO as the tag of the `cisco-nso` Docker\nimage, so `cisco-nso:4.2.3` means Cisco NSO 4.2.3. This is what the `nso-ts`\nDockerfile will use in its `FROM` line.\n\nUpgrading to a new version of NCS is thus just a matter of rewriting the tag…\nbut what version of NCS should we use? There’s 4.2.4, 4.3.3, 4.4.2 and 4.4.3\navailable and I’m sure there’s some other version that will pop up its evil\nhead soon enough. How do I know which version to pick? And will our current code\nwork with the new version?\n\nTo help myself in the choice of NCS version I implemented a script that gets the\nREADME file of a new NCS version and cross references the list of fixed issues\nwith the issues that we currently have open in the Tail-F issue tracker. The\noutput of this is included in the merge request description so when I look at\nthe merge request I immediately know what bugs are fixed or new features are\nimplemented by moving to a specific version. Having this automatically generated\nfor us is… well, it’s just damn convenient. Together with actually testing our\ncode with the new version of NCS gives us confidence that an upgrade will be smooth.\n\nHere are the merge requests currently opened by our GitBot:\n\n\u003Cimg src=\"https://about.gitlab.com/images/blogimages/automate-git-merge-requests.png\" alt=\"Merge requests automated by Git bot\" style=\"width: 700px;\"/>\n\nWe can see how the system have generated MRs to move to all the different\nversions of NSO currently available. As we are currently on NSO v4.2.3 there’s\nno underlying branch for that one leading to an errored build. For the other\nversions though, there is a branch per version that executes the CI pipeline to\nmake sure all our code runs with this version of NSO.\n\nAs there have been a few commits today, these branches are behind by six commits\nbut will be rebased this night so we get an up-to-date picture if they work or\nnot with our latest code.\n\n\u003Cimg src=\"https://about.gitlab.com/images/blogimages/automate-git-commits.png\" alt=\"Commits\" style=\"width: 700px;\"/>\n\nIf we go back and look at one of these merge requests, we can see how the\ndescription includes information about what issues that we currently have open\nwith Cisco / Tail-F would be solved by moving to this version.\n\n\u003Cimg src=\"https://about.gitlab.com/images/blogimages/automate-git-mr-description.png\" alt=\"Merge request descriptions\" style=\"width: 700px;\"/>\n\nThis is from v4.2.4 and as we are currently on v4.2.3 we can see that there are\nonly a few fixed issues.\n\nIf we instead look at v4.4.3 we can see that the list is significantly longer.\n\n\u003Cimg src=\"https://about.gitlab.com/images/blogimages/automate-git-mr-description-list.png\" alt=\"Merge request descriptions\" style=\"width: 700px;\"/>\n\nPretty sweet, huh? :)\n\nAs this involves a bit more code I’ve put the relevant files in a [GitHub gist](https://gist.github.com/plajjan/42592665afd5ae045ee36220e19919aa).\n\n## This is the end\n\nIf you are reading this, chances are you already have your reasons for why you\nwant to automate some Git operations. Hopefully I’ve provided some inspiration\nfor how to do it.\n\nIf not or if you just want to discuss the topic in general or have more specific\nquestions about our setup, please do reach out to me on [Twitter](https://twitter.com/plajjan).\n\n_[This post](http://plajjan.github.io/automating-git/) was originally published on [plajjan.github.io](http://plajjan.github.io/)._\n\n## About the Guest Author\n\nKristian Larsson is a network automation systems architect at Deutsche Telekom.\nHe is working on automating virtually all aspects of running TeraStream, the\ndesign for Deutsche Telekom's next generation fixed network, using robust and\nfault tolerant software. He is active in the IETF as well as being a\nrepresenting member in OpenConfig. Previous to joining Deutsche Telekom,\nKristian was the IP & opto network architect for Tele2's international backbone\nnetwork.\n\n\"[BB-8 in action](https://unsplash.com/photos/C8VWyZhcIIU) by [Joseph Chan](https://unsplash.com/@yulokchan) on Unsplash\n",[23,24,25],"CI/CD","user stories","git","yml",{},true,"/en-us/blog/automating-boring-git-operations-gitlab-ci",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":31,"ogSiteName":32,"ogType":33,"canonicalUrls":31},"https://about.gitlab.com/blog/automating-boring-git-operations-gitlab-ci","https://about.gitlab.com","article","en-us/blog/automating-boring-git-operations-gitlab-ci",[36,37,25],"cicd","user-stories","Ds45efuEvD4ppSZBUWgNtFtEYz83Gf1uE1MctWUSp20",{"data":40},{"logo":41,"freeTrial":46,"sales":51,"login":56,"items":61,"search":368,"minimal":399,"duo":418,"switchNav":427,"pricingDeployment":438},{"config":42},{"href":43,"dataGaName":44,"dataGaLocation":45},"/","gitlab 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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.",[720],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[23,724,725,726],"DevOps platform","tutorial","features",{"featured":28,"template":13,"slug":728},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":730,"config":740},{"title":731,"description":732,"authors":733,"heroImage":735,"date":736,"body":737,"category":9,"tags":738},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[734],"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/)",[725,739,726],"product",{"featured":12,"template":13,"slug":741},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":743,"config":753},{"title":744,"description":745,"authors":746,"heroImage":748,"date":749,"category":9,"tags":750,"body":752},"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.",[747],"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",[260,621,751],"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":754,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":756},[757,771,782,794],{"id":758,"categories":759,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[760],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[739,567],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"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":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":781},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":50,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":817},{"href":54,"dataGaName":55,"dataGaLocation":815},1776449932136]