[{"data":1,"prerenderedAt":822},["ShallowReactive",2],{"/en-us/blog/eks-fargate-runner":3,"navigation-en-us":41,"banner-en-us":451,"footer-en-us":461,"blog-post-authors-en-us-Darwin Sanoy":701,"blog-related-posts-en-us-eks-fargate-runner":717,"blog-promotions-en-us":759,"next-steps-en-us":812},{"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":35,"tagSlugs":36,"__hash__":40},"blogPosts/en-us/blog/eks-fargate-runner.yml","Eks Fargate Runner",[7],"darwin-sanoy",null,"engineering",{"slug":11,"featured":12,"template":13},"eks-fargate-runner",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Get started with GitLab EKS Fargate Runners in 1 hour and zero code, Iteration 1","This detailed tutorial answers the question of how to leverage Amazon's AWS Fargate container technology for GitLab Runners.",[18],"Darwin Sanoy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663373/Blog/Hero%20Images/jeremy-lapak-CVvFVQ_-oUg-700unsplash.jpg","2023-05-24","Leveraging Amazon's AWS Fargate container technology for [GitLab Runners](https://docs.gitlab.com/runner/) has been a longstanding ask from our customers. This tutorial gets you up and running with the GitLab EKS Fargate Runner combo in less than an hour.\n\nGitLab has a pattern for this task for [Fargate](https://docs.aws.amazon.com/AmazonECS/latest/userguide/what-is-fargate.html) runners under AWS Elastic Container Service (ECS). The primary challenge with this solution is that AWS ECS itself does not allow for the overriding of what image is used when calling an ECS task. Therefore, each GitLab Runner manager ignores the gitlab-ci.yml `image:` tag and runs on the image preconfigured in the task during deployment of the runner manager. As a result, you'll end up creating runner container images that contain every dependency for all the software built by the runner, or you'll create a lot of runner managers per image — or both.\n\nI have long wondered if Fargate-backed Elastic Kubernetes Service (EKS) could get around this limitation since, by nature, Kubernetes must be able to run any image given to it.\n\n## The approach\n\nNothing takes the joy out of learning faster than a lot of complex setup before being able to get to the point of the exercise. To address this, this tutorial uses four things to dramatically reduce the time and steps required to get from zero to hero.\n\n1. AWS CloudShell to minimize the EKS Admin Tooling setup. This also leaves your local machine environment untouched so that other tooling configurations don't get modified.\n2. A project called **AWS CloudShell ”Run From Web” Configuration Scripts** to rapidly add additional tooling to CloudShell. This includes some hacks to get large Terraform templates to work on AWS CloudShell.\n3. EKS Blueprints — specifically, a Terraform example that implements both the [Karpenter autoscaler](https://aws.amazon.com/blogs/aws/introducing-karpenter-an-open-source-high-performance-kubernetes-cluster-autoscaler/) and Fargate, including for the kube-system namespace.\n4. A simple Helm install for GitLab Runner.\n\nAlthough you will be running CLI commands and editing config files, no coding is required in the sense that you won't have to build something complex from scratch and then maintain it yourself.\n\n## The results\n\nIt works! It can run 2 x 200 (max allowed per job) parallel “Hello, World” jobs on AWS Fargate-backed EKS in about 4 minutes, which demonstrates the unlimited scalability. It can also run a simple Auto DevOps pipeline, which proves out the ability to run a bunch of different containers.\n\nThe fact that the entire cluster - including kube-system - is Fargate backed reduces the Kubernetes specific long term SRE work to a much lower value approaching that of ECS Fargate clusters. Later on we discuss that this trade-off has a cost and how it can be reconfigured.\n\n## What makes it possible: Product-managed IaC that is an extensible framework\n\nToolkitting made up of Infrastructure as Code (IaC) is frequently referred to as “templates,” and these templates have a reputation of not aging well because there is no active stewardship of the codebase — they are thought of as a one-and-done effort. However, this term does not reflect reality well when the underlying IaC code is actually being product-managed. You can tell if something is being product-managed by using these markers:\n\n- It has a scope-bounded vision of what it wants to do for the community being served (customer).\n- It has active stewardship that keeps the codebase moving along, even if it is open source.\n- It seeks to incorporate strategic enhancements, a.k.a. new features.\n- Things that are broken are considered bugs and are actively eliminated.\n- There is a cadence of taking underlying version updates and for supporting new versions of the primary things they deploy.\n\nAs an extensible framework, EKS Blueprints:\n\n- Are purposefully architected to be extended by anyone.\n- Already have many extensions built.\n\nWhen implementing using EKS Blueprints and you come upon a new need, it is important to check if EKS Blueprints already handles that consideration - similarly to how you would look for Ruby Gems, NPM Modules or Python PyPI packages before building functionality from scratch.\n\nAll of the above are aspects of how the AWS EKS team is product-managing EKS Blueprints. They deserve a big round of applause because product-managing anything to prevent it from becoming yet another community-maintained shelfware project is a strong commitment that requires tenacity!\n\n## Reproducing the experiment\n\n### 1. Set up AWS CloudShell\n\n> **Note:** If you already have a fully persistent environment setup (like your laptop) with: AWS CLI, kubectl, Terraform, then you can avoid environment rebuilds when AWS CloudShell times out by using that instead.\n\nAWS CloudShell comes with kubectl, Git, and AWS CLI, which are all needed. However, we also need a few other scripts. More information about these scripts can be read in [my blog post on AWS CloudShell “Run For Web” Configuration Scripts](https://missionimpossiblecode.io/aws-cloudshell-run-from-web-configuration-scripts).\n\n> **Note:** The steps in this section up through the `git clone` from GitLab step (second clone operation) in the next section can be accomplished by running this: `s=prep-eksblueprint-karpenter.sh ; curl -sSL https://gitlab.com/guided-explorations/aws/aws-cloudshell-configs/-/raw/main/${s} -o /tmp/${s}; chmod +x /tmp/${s}; bash /tmp/${s}*` .\n\n1. Use the web console to login to an AWS account where you have admin permissions.\n2. Switch to the region of your choosing.\n3. In the bottom left of the console click the “CloudShell” icon.\n4. Copy and paste the following one-liner into the console to install Helm, Terraform, and the Nano text editor:\n   `curl -sSL https://gitlab.com/guided-explorations/aws/aws-cloudshell-configs/-/raw/main/add-all.sh -o $HOME/add-all.sh; chmod +x $HOME/add-all.sh; bash $HOME/add-all.sh`\n5. Since our Terraform template will grow larger than the 1GB limit of space in the $HOME directory, we need a workaround to use the template in one directory, but store the Terraform state in $HOME where it will be kept as long as 120 days. The following one-liner triggers a script that performs that setup for us, after which we can use the /terraform directory for our template:\n   `curl -sSL https://gitlab.com/guided-explorations/aws/aws-cloudshell-configs/-/raw/main/prep-for-terraform.sh -o $HOME/prep-for-terraform.sh; chmod +x $HOME/prep-for-terraform.sh; bash $HOME/prep-for-terraform.sh`\n\n### 2. Run Terraform EKS Blueprint\n\n> **Note:** If at any time you leave your AWS CloudShell long enough for your session to end, the /terraform directory will be tossed. Simply run the last script above and the first four steps below to make it operable again. This will most likely be necessary when it comes time to teardown the Terraform created AWS resources.\n>\n> Sometimes your AWS CloudShell credentials may expire with a message like: `Error: Kubernetes cluster unreachable: Get \">CLUSTER URL>\": getting credentials: exec: executable aws failed with exit code 255`. Simply refresh the entire browser tab where AWS CloudShell is running and you’ll generally have new credentials.\n\n#### Version safety\n\nThis tutorial uses a specific release of the EKS Blueprint project so that you have the known state at the time of publishing. The project version also cascades into the versions of all the many dependent modules. While it may also work with the latest version, the version at the time of writing was Version 4.29.0.\n\nThis tutorial also uses Terraform binary Version 1.4.5.\n\n#### Procedures\n\nIf, while using AWS CloudShell, you experience this error: `Error: configuring Terraform AWS Provider: no valid credential sources for Terraform AWS Provider found`, you will need to refresh your browser to update the cached credentials in the terminal session.\n\nPerform the following commands on the AWS CloudShell session:\n\n1. `git clone https://github.com/aws-ia/terraform-aws-eks-blueprints.git --no-checkout /terraform/terraform-aws-eks-blueprints`\n2. `cd /terraform/terraform-aws-eks-blueprints/`\n3. `git reset --hard tags/v4.29.0` #Version pegging to the code that this article was authored with.\n4. `git clone https://gitlab.com/guided-explorations/aws/eks-runner-configs/gitlab-runner-eks-fargate.git /terraform/terraform-aws-eks-blueprints/examples/glrunner`\n\n   **Note:** Like other EKS Blueprints examples, the GitLab EKS Fargate Runner example references EKS Blueprint modules with a relative directory reference. This is why we are cloning it into a subdirectory of the EKS Blueprints project.\n5. `cd /terraform/terraform-aws-eks-blueprints/examples/glrunner`\n6. `terraform init`\n\n   **Important**: If you are using AWS CloudShell and your session times out, the /terraform folder and the installed utilities will be gone. You would have to reproduce the above steps to get the Terraform template in a usable state again. This is most likely to happen when you go to use Terraform to delete the stack after playing with it for some days.\n\n   The next few instructions are from: **https://github.com/aws-ia/terraform-aws-eks-blueprints/blob/main/examples/karpenter/README.md#user-content-deploy**. Note the `-state` switch ensures our state is in persistent storage.\n7. `terraform apply -target module.vpc -state=$HOME/tfstate/runner.tfstate`\n8. `terraform apply -target module.eks -state=$HOME/tfstate/runner.tfstate`\n9. **Note:** If you receive “Error: The configmap ”aws-auth” does not exist”, re-run the same command - it will usually update successfully.\n10. `terraform apply -state=$HOME/tfstate/runner.tfstate`\n\nThe previous command will output a kubeconfig command that needs to be run to ensure subsequent kubectl commands work. Run that command. If you are in AWS CloudShell and did not copy the command, this command should work and map to the correct region:\n    `aws eks update-kubeconfig --region $AWS_DEFAULT_REGION --name \"glrunner\"`\n\nIf everything was done correctly, you will have an EKS cluster named `karpenter` in the CloudShell region web console like this:\n\n![codecountingcilog](https://about.gitlab.com/images/blogimages/eks-fargate-runner/eksclusterinconsole.png)\n\nAnd the output of this console command `kubectl get pods -A` will look like this:\n\n![codecountingcilog](https://about.gitlab.com/images/blogimages/eks-fargate-runner/cliplaincluster.png)\n\nThe output of this console command `kubectl get nodes -A` will show the Fargate prefix:\n\n![codecountingcilog](https://about.gitlab.com/images/blogimages/eks-fargate-runner/clinodesarefargate.png)\n\n> **Note:** Notice that all the EKS extras (coredns, ebs-cni, and karpenter itself) are also running on Fargate. If you are willing to tolerate some regular Kubernetes nodes, you may be able to save cost by running always-on pods on regular Kubernetes hosts. Since this cluster runs Karpenter, you will not need to manually scale those hosts and EKS makes control plane and node updates easier.\n\n### 3. Install GitLab Runner\n\nThese and other commands are available in the GitLab documentation for [GitLab Runner Helm Chart](https://docs.gitlab.com/runner/install/kubernetes.html#additional-configuration).\n\n1. Create an empty GitLab project.\n2. Retrieve a GitLab Runner Token from the project. Keep in mind that using a project token is the easiest way to ensure your experiment runs only on the EKS Fargate Runner. Using a group token may cause your job to run on other runners already setup at your company. You can follow [“Obtain a token”](https://docs.gitlab.com/runner/register/#requirements) from the documentation if you need to.\n3. Perform the following commands back in the AWS CloudShell session.\n4. `nano runnerregistration.yaml`\n5. Paste the following:\n\n   ```yaml\n   gitlabUrl: https://_YOUR_GITLAB_URL_HERE_.com\n   runnerRegistrationToken: _YOUR_GITLAB_RUNNER_TOKEN_HERE_\n   concurrent: 200\n   rbac:\n     create: true\n   runners:\n     tags: eks-fargate\n     runUntagged: true\n     imagePullPolicy: if-not-present\n   envVars:\n     - name: KUBERNETES_POLL_TIMEOUT\n       value: 90\n   ```\n\n   **Note:** Many more settings are discussed in the documentation for the [Kubernetes Executor](https://docs.gitlab.com/runner/executors/kubernetes.html).\n\n**Hard Lesson:** Using a setting for `concurrent` that is lower than our `parallel` setting in the GitLab job below results in all kinds of failures due to some job pods having to wait for an execution slot. Since it’s Fargate, there is no savings to keeping it lower and no negative impact to making it the complete parallel amount.\n\n6. Replace \\_YOUR_GITLAB_URL_HERE_ with your actual GitLab URL.\n7. Replace \\_YOUR_GITLAB_RUNNER_TOKEN_HERE_ with your actual runner token.\n8. Press CTRL-X to exit and press Y to the save prompt.\n9. `helm repo add gitlab https://charts.gitlab.io`\n10. `helm repo update gitlab`\n11. `helm install --namespace gitlab-runner --create-namespace runner1 -f runnerregistration.yaml gitlab/gitlab-runner`\n12. Wait for a few minutes and check the project’s list of runners for a new one with the tag `eks-fargate`\n\nIn AWS CloudShell the command `kubectl get pods -n gitlab-runner` should produce output similar to this:\n\n![codecountingcilog](https://about.gitlab.com/images/blogimages/eks-fargate-runner/runnerlist.png)\n\nAnd in the GitLab Runner list, it will look similar to this:\n\n![codecountingcilog](https://about.gitlab.com/images/blogimages/eks-fargate-runner/glrunnerlist.png)\n\n### 4. Run a test job\n\nThe simplest way to test GitLab Runner scaling is using the `parallel:` keyword to schedule multiple copies of a job. It can also be used to create a job matrix where not all jobs do the same thing.\n\nOne or more GitLab Runner Helm deployments can live in any namespace, so you have many to many mapping flexibility for how you think of runners and their Kubernetes context.\n\nIn the GitLab project where you created the runner, use the web IDE to create .gitlab-ci.yml and populate it with the following content:\n   ```yaml\n   parallel-fargate-hello-world:\n     image: public.ecr.aws/docker/library/bash\n     stage: build\n     parallel: 200\n     script:\n       - echo \"Hello Fargate World\"\n   ```\n\n**Hard Lesson:** After hitting the Docker hub image pull rate limit, I shifted to the same container in the AWS Public Elastic Container Registry (ECR), which has an [image pull rate limit](https://docs.aws.amazon.com/AmazonECR/latest/public/public-service-quotas.html) of 10 per second for this scenario.\n\nIf the job does not automatically start, use the pipeline page to force it to run.\n\nIf everything is configured correctly, your final pipeline status panel should look something like this:\n\n![codecountingcilog](https://about.gitlab.com/images/blogimages/eks-fargate-runner/completedjobs.png)\n\n### 5. Runner scaling experimentation\n\nThese and other commands are available in the GitLab documentation for [GitLab Runner Helm Chart](https://docs.gitlab.com/runner/install/kubernetes.html#additional-configuration).\n\nAdditional runners can be added by re-running the install command with a different name for the runner (if using the same token you’ll have two runners in the same group or project):\n\n`helm install --namespace gitlab-runner runner2 -f runnerregistration.yaml gitlab/gitlab-runner`\n\n200 jobs takes just under 2 minutes.\n\n#### 400 parallel jobs\n\nBy setting up a second identical job (with a unique job name), I was able to process 400 total jobs.\n\n**Hard Lesson:** The runner likes to schedule all jobs in a parallel job on the same runner instance. It does not seem to want to split a large job across multiple runners registered in the same project. So in order to get more than 200 jobs to process, I had to have two registered runners set to `concurrent:200` and two seperate jobs set to `parallel: 200`\n\n400 jobs takes just over 3 minutes.\n\n#### More than 400 parallel jobs\n\nAs I tried to scale higher, jobs started to hang. I tried specifically routing jobs to five runners each capable of 300 parallel jobs. I also tried multiple stages and used a hack of `needs []` to get simultaneous execution of jobs in multiple stages.\n\nI was not successful and there could be a wide variety of reasons why — a riddle for a future iteration.\n\nThis command can be used to update a runner's settings after editing the Helm values file (including the token to move the runner to another context):\n\n`helm upgrade --namespace gitlab-runner -f runnerregistration.yaml runner2 gitlab/gitlab-runner`\n\nI found that when I pushed the limits, I would sometimes end up with hung pods until I understood what needed adjusting. Leaving hung Fargate pods will add up to a lot of cash because the pricing assumes very short execution times. This command helps you terminate job pods without accidentally terminating the runner manager pods:\n\n`kubectl get pods --all-namespaces --no-headers |  awk '{if ($2 ~ \"_YOUR_JOB_POD_PREFACE_*\") print $2}' | xargs kubectl -n _YOUR_RUNNER_NAMESPACE_ delete pod`\n\nDon't forget to replace \\_YOUR_RUNNER_NAMESPACE_ and \\_YOUR_JOB_POD_PREFACE_ “_YOUR_JOB_POD_PREFACE\\_” is the unique preface of ONLY the jobs from a given runner followed by the wildcard star character => \\*\n\nTo uninstall a runner, use:\n\n`helm delete --namespace gitlab-runner runner1`\n\n#### Testing Auto DevOps to prove `image:` tag is honored\n\nTechnically testing Auto DevOps to prove the `image:` tag is honored this isn’t entirely necessary since the above job loads the bash container without the container being specified in any of the runner or infrastructure setup. However, I performed this as a litmus test anyway.\n\nFollow these steps:\n\n1. Create a new project by clicking the “+” sign in the top bar of GitLab.\n2. On the next page, select “New Project/Repository”.\n3. Then “Create from template”.\n4. Select “Ruby on Rails” (first choice).\n5. Once the project creation is complete, register an EKS runner to it (or re-register the existing runner to the new project).\n6. In the project, select “Settings (Gear Icon)” => “CI/CD” => Auto DevOps => Default to Auto DevOps pipeline.\n7. Click “Save changes”.\n\nThe Auto DevOps pipeline should run. If you don’t have a cluster wired up, it will mainly do security scanning, which is sufficient to prove that arbitrary containers can be used by the Fargate-backed GitLab Runner.\n\n### 6. Solution tuning via extensible platform\n\nEKS Blueprints is not only product-managed, it is also an extensible platform or framework. In the spirit of fully leveraging the extensible product managed EKS Blueprints project, you will always want to check if Blueprints is already instrumented for your scenario before writing code. Additionally, if you must write code, you can consider contributing it as an EKS Blueprint extension so the community can take on some responsibility for maintaining it.\n\n1. The EKS Blueprints Managed IaC has a dizzing number of tuning parameters and optional extensions. For instance, if you want the full GitLab Runner logs collected to AWS CloudWatch, it is a simple configuration to add fluentd log agent to push custom logs to CloudWatch.\n2. Using Fargate for always-on containers is a trade-off of compute costs to get rid of Kubernetes node management overhead. This trade-off can be easily reversed in this example by removing the \"kube-system\" from \"fargate_profiles\" - since Karpenter is also installed and configured, the hosts will autoscale for load.\n\n### 7. Teardown\n\nThe next few instructions are from https://github.com/aws-ia/terraform-aws-eks-blueprints/blob/main/examples/karpenter/README.md#user-content-destroy.\n\nIf you are using AWS CloudShell and the /terraform directory no longer exists, perform these steps to re-prepare AWS CloudShell to perform teardown.\n\nIf you are not using AWS CloudShell, skip forward to “Teardown steps”.\n\n1. `curl -sSL https://gitlab.com/guided-explorations/aws/aws-cloudshell-configs/-/raw/main/add-all.sh -o $HOME/add-all.sh; chmod +x $HOME/add-all.sh; bash $HOME/add-all.sh`\n2. `curl -sSL https://gitlab.com/guided-explorations/aws/aws-cloudshell-configs/-/raw/main/prep-for-terraform.sh -o $HOME/prep-for-terraform.sh; chmod +x $HOME/prep-for-terraform.sh; bash $HOME/prep-for-terraform.sh`\n3. `git clone https://github.com/aws-ia/terraform-aws-eks-blueprints.git --no-checkout /terraform/terraform-aws-eks-blueprints`\n4. `cd /terraform/terraform-aws-eks-blueprints/`\n5. `git reset --hard tags/v4.29.0`\n6. `git clone https://gitlab.com/guided-explorations/aws/eks-runner-configs/gitlab-runner-eks-fargate.git /terraform/terraform-aws-eks-blueprints/examples/glrunner`\n\n   > **Note:** The above steps can be accomplished by running this: `s=prep-eksblueprint-karpenter.sh ; curl -sSL https://gitlab.com/guided-explorations/aws/aws-cloudshell-configs/-/raw/main/${s} -o /tmp/${s}; chmod +x /tmp/${s}; bash /tmp/${s}` .\n\n7. `cd /terraform/terraform-aws-eks-blueprints/examples/glrunner`\n8. `terraform init`\n\nFollow these teardown steps:\n\n1. `helm delete --namespace gitlab-runner runner1`\n2. `helm delete --namespace gitlab-runner runner2`\n3. `terraform destroy -target=\"module.eks_blueprints_kubernetes_addons\" -auto-approve -state=$HOME/tfstate/runner.tfstate`\n4. `terraform destroy -target=\"module.eks\" -auto-approve -state=$HOME/tfstate/runner.tfstate`\n5. **Note:** If you receive an error about refreshing cached credentials, simply re-run the command again and it will usually update successfully.\n6. `terraform destroy -auto-approve -state=$HOME/tfstate/runner.tfstate`\n\n### Iteration _n_ : We would love your input\n\nThis blog is \"Iteration 1\" precisely because it has not been production load-tested nor specifically cost-engineered. And obviously a “Hello, World” script is not testing much in the way of real work. I really set out to understand if we could run arbitrary containers in a GitLab Fargate setup (and we can) and then got curious about what parallel job scaling might look like with Fargate (and it looks good). The Kubernetes Runner executor has many, many available customizations and it is likely that scaling a production loaded implementation on EKS will reveal the need to tune more of these parameters.\n\n#### **Collaborative contribution challenges**\n\nHere are some ideas for further collaborative work on this project:\n\n- To push the limits, create a configuration that can scale to 1000 simultaneous jobs.\n- An aws-logging config map that uploads runner pod logs to AWS CloudWatch.\n- A cluster configuration where runner managers and everything that is not a runner job run on non-Fargate nodes – if and only if it will be cheaper than Fargate running 24 x 7.\n- A Fargate Spot configuration. It’s important that compute type be noted as a runner tag and it’s important that the same cluster has non-spot instances because some jobs should not run on spot compute and the decision whether to do so should be available to the GitLab CI Developer who is creating an pipeline.\n\n#### Other runner scaling initiatives\n\nWhile GitLab is building the Next Runner Auto-scaling Architecture, [Kubernetes refinements are not a part of this architectural initiative](https://docs.gitlab.com/ee/architecture/blueprints/runner_scaling/#proposal).\n\n#### Everyone can contribute\n\nThis tutorial, as well as code for additional examples, will be maintained as open source as a GitLab Alliances Solution and we’d love to have your contributions as you iterate and discover the configurations necessary for your real-world scenarios. 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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.",[724],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[110,728,729,730],"DevOps platform","tutorial","features",{"featured":28,"template":13,"slug":732},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":734,"config":744},{"title":735,"description":736,"authors":737,"heroImage":739,"date":740,"body":741,"category":9,"tags":742},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[738],"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/)",[729,743,730],"product",{"featured":12,"template":13,"slug":745},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":747,"config":757},{"title":748,"description":749,"authors":750,"heroImage":752,"date":753,"category":9,"tags":754,"body":756},"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.",[751],"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",[263,623,755],"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":758,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":760},[761,775,786,798],{"id":762,"categories":763,"header":765,"text":766,"button":767,"image":772},"ai-modernization",[764],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":768,"config":769},"Get your AI maturity score",{"href":770,"dataGaName":771,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":776,"categories":777,"header":778,"text":766,"button":779,"image":783},"devops-modernization",[743,569],"Are you just managing tools or shipping innovation?",{"text":780,"config":781},"Get your DevOps maturity score",{"href":782,"dataGaName":771,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":787,"categories":788,"header":790,"text":766,"button":791,"image":795},"security-modernization",[789],"security","Are you trading speed for security?",{"text":792,"config":793},"Get your security maturity score",{"href":794,"dataGaName":771,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":796},{"src":797},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":799,"paths":800,"header":803,"text":804,"button":805,"image":810},"github-azure-migration",[801,802],"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":806,"config":807},"See how GitLab compares to GitHub",{"href":808,"dataGaName":809,"dataGaLocation":245},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":811},{"src":785},{"header":813,"blurb":814,"button":815,"secondaryButton":820},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":816,"config":817},"Get your free trial",{"href":818,"dataGaName":52,"dataGaLocation":819},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":507,"config":821},{"href":56,"dataGaName":57,"dataGaLocation":819},1776442953574]