[{"data":1,"prerenderedAt":795},["ShallowReactive",2],{"/en-us/blog/using-child-pipelines-to-continuously-deploy-to-five-environments":3,"navigation-en-us":43,"banner-en-us":442,"footer-en-us":452,"blog-post-authors-en-us-Olivier Dupré":692,"blog-related-posts-en-us-using-child-pipelines-to-continuously-deploy-to-five-environments":707,"assessment-promotions-en-us":746,"next-steps-en-us":785},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":30,"path":31,"publishedDate":20,"seo":32,"stem":36,"tagSlugs":37,"__hash__":42},"blogPosts/en-us/blog/using-child-pipelines-to-continuously-deploy-to-five-environments.yml","Using Child Pipelines To Continuously Deploy To Five Environments",[7],"olivier-dupr",null,"engineering",{"slug":11,"featured":12,"template":13},"using-child-pipelines-to-continuously-deploy-to-five-environments",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Using child pipelines to continuously deploy to five environments","Learn how to manage continuous deployment to multiple environments, including temporary, on-the-fly sandboxes, with a minimalist GitLab workflow.",[18],"Olivier Dupré","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097012/Blog/Hero%20Images/Blog/Hero%20Images/AdobeStock_397632156_3Ldy1urjMStQCl4qnOBvE0_1750097011626.jpg","2024-09-26","DevSecOps teams sometimes require the ability to manage continuous deployment across multiple environments — and they need to do so without changing their workflows. The [GitLab DevSecOps platform](https://about.gitlab.com/) supports this need, including temporary, on-the-fly sandboxes, with a minimalist approach. In this article, you'll learn how to run continuous deployment of infrastructure using Terraform, over multiple environments.\n\nThis strategy can easily be applied to any project, whether it is infrastructure as code (IaC) relying on another technology, such as [Pulumi](https://www.pulumi.com/) or [Ansible](https://www.ansible.com/), source code in any language, or a monorepo that mixes many languages.\n\nThe final pipeline that you will have at the end of this tutorial will deploy:\n\n* A temporary **review** environment for each feature branch.\n* An **integration** environment, easy to wipe out and deployed from the main branch.\n* A **QA** environment, also deployed from the main branch, to run quality assurance steps.\n* A **staging** environment, deployed for every tag. This is the last round before production.\n* A **production** environment, just after the staging environment. This one is triggered manually for demonstration, but can also be continuously deployed.\n\n>Here is the legend for the flow charts in this article:\n> * Round boxes are the GitLab branches.\n> * Square boxes are the environments.\n> * Text on the arrows are the actions to flow from one box to the next.\n> * Angled squares are decision steps.\n\n```mermaid\nflowchart LR\n    A(main) -->|new feature| B(feature_X)\n\n    B -->|auto deploy| C[review/feature_X]\n    B -->|merge| D(main)\n    C -->|destroy| D\n\n    D -->|auto deploy| E[integration]\n    E -->|manual| F[qa]\n\n    D -->|tag| G(X.Y.Z)\n    F -->|validate| G\n\n    G -->|auto deploy| H[staging]\n    H -->|manual| I{plan}\n    I -->|manual| J[production]\n\n```\nOn each step, you'll learn the [why](#why) and the [what](#what) before moving to the [how](#how). This will help you fully understand and replicate this tutorial.\n\n## Why\n\n* [Continuous integration](https://about.gitlab.com/topics/ci-cd/#what-is-continuous-integration-ci) is almost a de facto standard. Most companies have implemented CI pipelines or are willing to standardize their practice.\n\n* [Continuous delivery](https://about.gitlab.com/topics/ci-cd/#what-is-continuous-delivery-cd), which pushes artifacts to a repository or registry at the end of the CI pipeline, is also popular.\n\n* Continuous deployment, which goes further and deploys these artifacts automatically, is less widespread. When it has been implemented, we see it essentially in the application field. When discussing continuously deploying  infrastructure, the picture seems less obvious, and is more about managing several environments. In contrast, testing, securing, and verifying the infrastructure's code seems more challenging. And this is one of the fields where DevOps has not yet reached its maturity. One of the other fields is to shift security left, integrating security teams and, more importantly, security concerns, earlier in the delivery lifecycle, to upgrade from DevOps to ***DevSecOps***.\n\nGiven this high-level picture, in this tutorial, you will work toward a simple, yet efficient way to implement DevSecOps for your infrastructure through the example of deploying resources to five environments, gradually progressing from development to production.\n\n__Note:__ Even if I advocate embracing a FinOps approach and reducing the number of environments, sometimes there are excellent reasons to maintain more than just dev, staging, and production. So, please, adapt the examples below to match your needs.\n\n## What\n\nThe rise of cloud technology has driven the usage of IaC. Ansible and Terraform were among the first to pave the road here. OpenTofu, Pulumi, AWS CDK, Google Deploy Manager, and many others joined the party.\n\nDefining IaC is a perfect solution to feel safe when deploying infrastructure. You can test it, deploy it, and replay it again and again until you reach your goal.\n\nUnfortunately, we often see companies maintain several branches, or even repositories, for each of their target environments. And this is where the problems start. They are no longer enforcing a process. They are no longer ensuring that any change in the production code base has been accurately tested in previous environments. And they start seeing drifts from one environment to the other.\n\nI realized this tutorial was necessary when, at a conference I attended, every participant said they do not have a workflow that enforces the infrastructure to be tested thoroughly before being deployed to production. And they all agreed that sometimes they patch the code directly in production. Sure, this is fast, but is it safe? How do you report back to previous environments? How do you ensure there are no side effects? How do you control whether you are putting your company at risk with new vulnerabilities being pushed too quickly in production?\n\nThe question of *why* DevOps teams deploy directly to production is critical here. Is it because the pipeline could be more efficient or faster? Is there no automation? Or, even worse, because there is *no way to test accurately outside of production*?\n\nIn the next section, you will learn how to implement automation for your infrastructure and ensure that your DevOps team can effectively test what you are doing before pushing to any environment impacting others. You will see how your code is secured and its deployment is controlled, end-to-end.\n\n## How\n\nAs mentioned earlier, there are many IaC languages out there nowadays and we objectively cannot cover *all* of them in a single article. So, I will rely on a basic Terraform code running on Version 1.4. Please do not focus on the IaC language itself but instead on the process that you could apply to your own ecosystem.\n\n### The Terraform code\n\nLet's start with a fundamental Terraform code.\n\nWe are going to deploy to AWS, a virtual private cloud (VPC), which is a virtual network. In that VPC, we will deploy a public and a private subnet. As their name implies, they are subnets of the main VPC. Finally, we will add an Elastic Cloud Compute (EC2) instance (a virtual machine) in the public subnet.\n\nThis demonstrates the deployment of four resources without adding too much complexity. The idea is to focus on the pipeline, not the code.\n\nHere is the target we want to reach for your repository.\n\n![target for repository](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097033/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750097033415.png)\n\nLet’s do it step by step.\n\nFirst, we declare all resources in a `terraform/main.tf` file:\n\n```terraform\nprovider \"aws\" {\n  region = var.aws_default_region\n}\n\nresource \"aws_vpc\" \"main\" {\n  cidr_block = var.aws_vpc_cidr\n\n  tags = {\n    Name     = var.aws_resources_name\n  }\n}\n\nresource \"aws_subnet\" \"public_subnet\" {\n  vpc_id     = aws_vpc.main.id\n  cidr_block = var.aws_public_subnet_cidr\n\n  tags = {\n    Name = \"Public Subnet\"\n  }\n}\nresource \"aws_subnet\" \"private_subnet\" {\n  vpc_id     = aws_vpc.main.id\n  cidr_block = var.aws_private_subnet_cidr\n\n  tags = {\n    Name = \"Private Subnet\"\n  }\n}\n\nresource \"aws_instance\" \"sandbox\" {\n  ami           = var.aws_ami_id\n  instance_type = var.aws_instance_type\n\n  subnet_id = aws_subnet.public_subnet.id\n\n  tags = {\n    Name     = var.aws_resources_name\n  }\n}\n```\n\nAs you can see, there are a couple of variables that are needed for this code, so let's declare them in a `terraform/variables.tf` file:\n\n```terraform\nvariable \"aws_ami_id\" {\n  description = \"The AMI ID of the image being deployed.\"\n  type        = string\n}\n\nvariable \"aws_instance_type\" {\n  description = \"The instance type of the VM being deployed.\"\n  type        = string\n  default     = \"t2.micro\"\n}\n\nvariable \"aws_vpc_cidr\" {\n  description = \"The CIDR of the VPC.\"\n  type        = string\n  default     = \"10.0.0.0/16\"\n}\n\nvariable \"aws_public_subnet_cidr\" {\n  description = \"The CIDR of the public subnet.\"\n  type        = string\n  default     = \"10.0.1.0/24\"\n}\n\nvariable \"aws_private_subnet_cidr\" {\n  description = \"The CIDR of the private subnet.\"\n  type        = string\n  default     = \"10.0.2.0/24\"\n}\n\nvariable \"aws_default_region\" {\n  description = \"Default region where resources are deployed.\"\n  type        = string\n  default     = \"eu-west-3\"\n}\n\nvariable \"aws_resources_name\" {\n  description = \"Default name for the resources.\"\n  type        = string\n  default     = \"demo\"\n}\n```\n\nAlready, we are almost good to go on the IaC side. What's missing is a way to share the Terraform states. For those who don't know, Terraform works schematically doing the following:\n\n* `plan` checks the differences between the current state of the infrastructure and what is defined in the code. Then, it outputs the differences.\n* `apply` applies the differences in the `plan` and updates the state.\n\nFirst round, the state is empty, then it is filled with the details (ID, etc.) of the resources applied by Terraform.\n\nThe problem is: Where is that state stored? How do we share it so several developers can collaborate on code?\n\nThe solution is fairly simple: Leverage GitLab to store and share the state for you through a [Terraform HTTP backend](https://docs.gitlab.com/ee/user/infrastructure/iac/terraform_state.html).\n\nThe first step in using this backend is to create the most simple `terraform/backend.tf` file. The second step will be handled in the pipeline.\n\n```terraform\nterraform {\n  backend \"http\" {\n  }\n}\n```\n\nEt voilà! We have a bare minimum Terraform code to deploy these four resources. We will provide the variable values at the runtime, so let's do that later.\n\n### The workflow\n\nThe workflow that we are going to implement now is the following:\n\n```mermaid\nflowchart LR\n    A(main) -->|new feature| B(feature_X)\n\n    B -->|auto deploy| C[review/feature_X]\n    B -->|merge| D(main)\n    C -->|destroy| D\n\n    D -->|auto deploy| E[integration]\n    E -->|manual| F[qa]\n\n    D -->|tag| G(X.Y.Z)\n    F -->|validate| G\n\n    G -->|auto deploy| H[staging]\n    H -->|manual| I{plan}\n    I -->|manual| J[production]\n```\n\n1. Create a **feature** branch. This will continuously run all scanners on the code to ensure that it is still compliant and secured. This code will be continuously deployed to a temporary environment `review/feature_branch` with the name of the current branch. This is a safe environment where the developers and operations teams can test their code without impacting anybody. This is also where we will enforce the process, like enforcing code reviews and running scanners, to ensure that the quality and security of the code are acceptable and do not put your assets at risk. The infrastructure deployed by this branch is automatically destroyed when the branch is closed. This helps you keep your budget under control.\n\n```mermaid\nflowchart LR\n    A(main) -->|new feature| B(feature_X)\n\n    B -->|auto deploy| C[review/feature_X]\n    B -->|merge| D(main)\n    C -->|destroy| D\n```\n\n2. Once approved, the feature branch will be **merged** into the main branch. This is a [protected branch](https://docs.gitlab.com/ee/user/project/protected_branches.html) where no one can push. This is mandatory to ensure that every change request to production is thoroughly tested. That branch is also continuously deployed. The target here is the `integration` environment. To keep this environment slightly more stable, its deletion is not automated but can be triggered manually.\n\n```mermaid\nflowchart LR\n    D(main) -->|auto deploy| E[integration]\n```\n\n3. From there, manual approval is required to trigger the next deployment. This will deploy the main branch to the `qa` environment. Here, I have set a rule to prevent deletion from the pipeline. The idea is that this environment should be quite stable (after all, it's already the third environment), and I would like to prevent deletion by mistake. Feel free to adapt the rules to match your processes.\n\n```mermaid\nflowchart LR\n    D(main)-->|auto deploy| E[integration]\n    E -->|manual| F[qa]\n```\n\n4. To proceed, we will need to **tag** the code. We are relying on [protected tags](https://docs.gitlab.com/ee/user/project/protected_tags.html) here to ensure that only a specific set of users are allowed to deploy to these last two environments. This will immediately trigger a deployment to the `staging` environment.\n\n```mermaid\nflowchart LR\n    D(main) -->|tag| G(X.Y.Z)\n    F[qa] -->|validate| G\n\n    G -->|auto deploy| H[staging]\n```\n\n5. Finally, we are landing to `production`. When discussing infrastructure, it is often challenging to deploy progressively (10%, 25%, etc.), so we will deploy the whole infrastructure. Still, we control that deployment with a manual trigger of this last step. And to enforce maximum control on this highly critical environment, we will control it as a [protected environment](https://docs.gitlab.com/ee/ci/environments/protected_environments.html).\n\n```mermaid\nflowchart LR\n    H[staging] -->|manual| I{plan}\n    I -->|manual| J[production]\n```\n\n### The pipeline\n\nTo implement the above [workflow](#the-workflow), we are now going to implement a pipeline with two [downstream pipelines](https://docs.gitlab.com/ee/ci/pipelines/downstream_pipelines.html).\n\n#### The main pipeline\n\nLet's start with the main pipeline. This is the one that will be triggered automatically on any **push to a feature branch**, any **merge to the default branch**, or any **tag**. *The one* that will do true **continuous deployment** to the following environments: `dev`, `integration`, and `staging`. And it is declared in the `.gitlab-ci.yml` file at the root of your project.\n\n![the repository target](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097033/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097033417.png)\n\n```yml\nStages:\n  - test\n  - environments\n\n.environment:\n  stage: environments\n  variables:\n    TF_ROOT: terraform\n    TF_CLI_ARGS_plan: \"-var-file=../vars/$variables_file.tfvars\"\n  trigger:\n    include: .gitlab-ci/.first-layer.gitlab-ci.yml\n    strategy: depend            # Wait for the triggered pipeline to successfully complete\n    forward:\n      yaml_variables: true      # Forward variables defined in the trigger job\n      pipeline_variables: true  # Forward manual pipeline variables and scheduled pipeline variables\n\nreview:\n  extends: .environment\n  variables:\n    environment: review/$CI_COMMIT_REF_SLUG\n    TF_STATE_NAME: $CI_COMMIT_REF_SLUG\n    variables_file: review\n    TF_VAR_aws_resources_name: $CI_COMMIT_REF_SLUG  # Used in the tag Name of the resources deployed, to easily differenciate them\n  rules:\n    - if: $CI_COMMIT_BRANCH && $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH\n\nintegration:\n  extends: .environment\n  variables:\n    environment: integration\n    TF_STATE_NAME: $environment\n    variables_file: $environment\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n\nstaging:\n  extends: .environment\n  variables:\n    environment: staging\n    TF_STATE_NAME: $environment\n    variables_file: $environment\n  rules:\n    - if: $CI_COMMIT_TAG\n\n#### TWEAK\n# This tweak is needed to display vulnerability results in the merge widgets.\n# As soon as this issue https://gitlab.com/gitlab-org/gitlab/-/issues/439700 is resolved, the `include` instruction below can be removed.\n# Until then, the SAST IaC scanners will run in the downstream pipelines, but their results will not be available directly in the merge request widget, making it harder to track them.\n# Note: This workaround is perfectly safe and will not slow down your pipeline.\ninclude:\n  - template: Security/SAST-IaC.gitlab-ci.yml\n#### END TWEAK\n```\n\nThis pipeline runs only two stages: `test` and  `environments`. The former is needed for the *TWEAK* to run scanners. The later triggers a child pipeline with a different set of variables for each case defined above (push to the branch, merge to the default branch, or tag).\n\nWe are adding here a dependency with the keyword [strategy:depend](https://docs.gitlab.com/ee/ci/yaml/index.html#triggerstrategy) on our child pipeline so the pipeline view in GitLab will be updated only once the deployment is finished.\n\nAs you can see here, we are defining a base job, [hidden](https://docs.gitlab.com/ee/ci/jobs/#hide-jobs), and we are extending it with specific variables and rules to trigger only one deployment for each target environment.\n\nBesides the [predefined variables](https://docs.gitlab.com/ee/ci/variables/predefined_variables.html), we are using two new entries that we need to define:\n1. [The variables specific](#the-variable-definitions) to each environment: `../vars/$variables_file.tfvars`\n2. [The child pipeline](#the-child-pipeline), defined in `.gitlab-ci/.first-layer.gitlab-ci.yml`\n\nLet's start with the smallest part, the variable definitions.\n\n### The variable definitions\n\nWe are going here to mix two solutions to provide variables to Terraform:\n\n* The first one using [.tfvars files](https://developer.hashicorp.com/terraform/language/values/variables#variable-definitions-tfvars-files) for all non-sensitive input, which should be stored within GitLab.\n\n![solution one to provide variables to Terraform](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097034/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097033419.png)\n\n* The second using [environment variables](https://developer.hashicorp.com/terraform/language/values/variables#environment-variables) with the prefix `TF_VAR`. That second way to inject variables, associated with the GitLab capacity to [mask variables](https://docs.gitlab.com/ee/ci/variables/#mask-a-cicd-variable), [protect them](https://docs.gitlab.com/ee/ci/variables/#protect-a-cicd-variable), and [scope them to environments](https://docs.gitlab.com/ee/ci/environments/index.html#limit-the-environment-scope-of-a-cicd-variable) is a powerful solution to **prevent sensitive information leakages**. (If you consider your production’s private CIDR very sensitive, you could protect it like this, ensuring it is only available for the `production` environment, for pipelines running against protected branches and tags, and that its value is masked in the job’s logs.)\n\n![solution two to provide variables to Terraform](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097034/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750097033422.png)\n\nAdditionally, each variable file should be controlled through a [`CODEOWNERS` file](https://docs.gitlab.com/ee/user/project/codeowners/) to set who can modify each of them.\n\n```text\n[Production owners]\nvars/production.tfvars @operations-group\n\n[Staging owners]\nvars/staging.tfvars @odupre @operations-group\n\n[CodeOwners owners]\nCODEOWNERS @odupre\n```\n\nThis article is not a Terraform training, so we will go very fast and simply show here the `vars/review.tfvars` file. Subsequent environment files are, of course, very similar. Just set the non-sensitive variables and their values here.\n\n```shell\naws_vpc_cidr = \"10.1.0.0/16\"\naws_public_subnet_cidr = \"10.1.1.0/24\"\naws_private_subnet_cidr = \"10.1.2.0/24\"\n```\n\n#### The child pipeline\n\nThis one is where the actual work is done. So, it is slightly more complex than the first one. But there is no difficulty here that we cannot overcome together!\n\nAs we have seen in the definition of the [main pipeline](#the-main-pipeline), that downstream pipeline is declared in the file `.gitlab-ci/.first-layer.gitlab-ci.yml`.\n\n![Downstream pipeline declared in file](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097033/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097033424.png)\n\nLet's break it down into small chunks. We'll see the big picture at the end.\n\n##### Run Terraform commands and secure the code\n\nFirst, we want to run a pipeline for Terraform. We, at GitLab, are open source. So, our Terraform template is open source. And you simply need to include it. This can be achieved with the following snippet:\n\n```yml\ninclude:\n  - template: Terraform.gitlab-ci.yml\n\n```\n\nThis template runs for you the Terraform checks on the formatting and validates your code, before planning and applying it. It also allows you to destroy what you have deployed.\n\nAnd, because GitLab is the a single, unified DevSecOps platform, we are also automatically including two security scanners within that template to find potential threats in your code and warn you before you deploy it to the next environments.\n\nNow that we have checked, secured, built, and deployed our code, let's do some tricks.\n\n##### Share cache between jobs\n\nWe will cache the job results to reuse them in subsequent pipeline jobs. This is as simple as adding the following piece of code:\n\n```yml\ndefault:\n  cache:  # Use a shared cache or tagged runners to ensure terraform can run on apply and destroy\n    - key: cache-$CI_COMMIT_REF_SLUG\n      fallback_keys:\n        - cache-$CI_DEFAULT_BRANCH\n      paths:\n        - .\n\n```\n\nHere, we are defining a different cache for each commit, falling back to the main branch name if needed.\n\nIf we look carefully at the templates that we are using, we can see that it has some rules to control when jobs are run. We want to run all controls (both QA and security) on all branches. So, we are going to override these settings.\n\n##### Run controls on all branches\n\nGitLab templates are a powerful feature where one can override only a piece of the template. Here, we are interested only in overwriting the rules of some jobs to always run quality and security checks. Everything else defined for these jobs will stay as defined in the template.\n\n```yml\nfmt:\n  rules:\n    - when: always\n\nvalidate:\n  rules:\n    - when: always\n\nkics-iac-sast:\n  rules:\n    - when: always\n\niac-sast:\n  rules:\n    - when: always\n\n```\n\nNow that we have enforced the quality and security controls, we want to differentiate how the main environments (integration and staging) in the [workflow](#the-workflow) and review environments behave. Let's start by defining the main environment’s behavior, and we will tweak this configuration for the review environments.\n\n##### CD to integration and staging\n\nAs defined earlier, we want to deploy the main branch and the tags to these two environments. We are adding rules to control that on both the `build` and `deploy` jobs. Then, we want to enable `destroy` only for the `integration` as we have defined `staging` to be too critical to be deleted with a single click. This is error-prone and we don't want to do that.\n\nFinally, we are linking the `deploy` job to the `destroy` one, so we can `stop` the environment directly from GitLab GUI.\n\nThe `GIT_STRATEGY` is here to prevent retrieving the code from the source branch in the runner when destroying. This would fail if the branch has been deleted manually, so we are relying on the cache to get everything we need to run the Terraform instructions.\n\n```yml\nbuild:  # terraform plan\n  environment:\n    name: $TF_STATE_NAME\n    action: prepare\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n    - if: $CI_COMMIT_TAG\n\ndeploy: # terraform apply --> automatically deploy on corresponding env (integration or staging) when merging to default branch or tagging. Second layer environments (qa and production) will be controlled manually\n  environment:\n    name: $TF_STATE_NAME\n    action: start\n    on_stop: destroy\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n    - if: $CI_COMMIT_TAG\n\ndestroy:\n  extends: .terraform:destroy\n  variables:\n    GIT_STRATEGY: none\n  dependencies:\n    - build\n  environment:\n    name: $TF_STATE_NAME\n    action: stop\n  rules:\n    - if: $CI_COMMIT_TAG  # Do not destroy production\n      when: never\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH && $TF_DESTROY == \"true\" # Manually destroy integration env.\n      when: manual\n\n```\n\nAs said, this matches the need to deploy to `integration` and `staging`. But we are still missing a temporary environment where the developers can experience and validate their code without impacts on others. This is where the deployment to the `review` environment takes place.\n\n##### CD to review environments\n\nDeploying to review environment is not too different than deploying to `integration` and `staging`. So we will once again leverage GitLab's capacity to overwrite only pieces of job definition here.\n\nFirst, we set rules to run these jobs only on feature branches.\n\nThen, we link the `deploy_review` job to `destroy_review`. This will allow us to stop the environment **manually** from the GitLab user interface, but more importantly, it will **automatically trigger the environment destruction** when the feature branch is closed. This is a good FinOps practice to help you control your operational expenditures.\n\nSince Terraform needs a plan file to destroy an infrastructure, exactly like it needs one to build an infrastructure, then we are adding a dependency from `destroy_review` to `build_review`, to retrieve its artifacts.\n\nFinally, we see here that the environment's name is set to `$environment`. It has been set in the [main pipeline](#the-main-pipeline) to `review/$CI_COMMIT_REF_SLUG`, and forwarded to this child pipeline with the instruction `trigger:forward:yaml_variables:true`.\n\n```yml\nbuild_review:\n  extends: build\n  rules:\n    - if: $CI_COMMIT_TAG\n      when: never\n    - if: $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH\n      when: on_success\n\ndeploy_review:\n  extends: deploy\n  dependencies:\n    - build_review\n  environment:\n    name: $environment\n    action: start\n    on_stop: destroy_review\n    # url: https://$CI_ENVIRONMENT_SLUG.example.com\n  rules:\n    - if: $CI_COMMIT_TAG\n      when: never\n    - if: $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH\n      when: on_success\n\ndestroy_review:\n  extends: destroy\n  dependencies:\n    - build_review\n  environment:\n    name: $environment\n    action: stop\n  rules:\n    - if: $CI_COMMIT_TAG  # Do not destroy production\n      when: never\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH   # Do not destroy staging\n      when: never\n    - when: manual\n\n```\n\nSo, to recap, we now have a pipeline that can:\n\n* Deploy temporary review environments, which are automatically cleaned up when the feature branch is closed\n* Continuously deploy the **default branch** to `integration`\n* Continuously deploy the **tags** to `staging`\n\nLet's now add an extra layer, where we will deploy, based on a manual trigger this time, to `qa` and `production` environments.\n\n##### Continously deploy to QA and production\n\nBecause not everybody is willing to deploy continuously to production, we will add a manual validation to the next two deployments. From a purely **CD** perspective, we would not add this trigger, but take this as an opportunity to learn how to run jobs from other triggers.\n\nSo far, we have started a [child pipeline](#the-child-pipeline) from the [main pipeline](#the-main-pipeline) to run all deployments.\n\nSince we want to run other deployments from the default branch and the tags, we will add another layer dedicated to these additional steps. Nothing new here. We will just repeat exactly the same process as the one we only did for the [main pipeline](#the-main-pipeline). Going this way allows you to manipulate as many layers as you need. I have already seen up to nine environments in some places.\n\nWithout arguing once again on the benefits to have fewer environments, the process that we are using here makes it very easy to implement the same pipeline all the way from early stages to final delivery, while keeping your pipeline definition simple and split in small chunks that you can maintain at no cost.\n\nTo prevent variable conflicts here, we are just using new var names to identify the Terraform state and input file.\n\n```yml\n.2nd_layer:\n  stage: 2nd_layer\n  variables:\n    TF_ROOT: terraform\n  trigger:\n    include: .gitlab-ci/.second-layer.gitlab-ci.yml\n    # strategy: depend            # Do NOT wait for the downstream pipeline to finish to mark upstream pipeline as successful. Otherwise, all pipelines will fail when reaching the pipeline timeout before deployment to 2nd layer.\n    forward:\n      yaml_variables: true      # Forward variables defined in the trigger job\n      pipeline_variables: true  # Forward manual pipeline variables and scheduled pipeline variables\n\nqa:\n  extends: .2nd_layer\n  variables:\n    TF_STATE_NAME_2: qa\n    environment: $TF_STATE_NAME_2\n    TF_CLI_ARGS_plan_2: \"-var-file=../vars/$TF_STATE_NAME_2.tfvars\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n\nproduction:\n  extends: .2nd_layer\n  variables:\n    TF_STATE_NAME_2: production\n    environment: $TF_STATE_NAME_2\n    TF_CLI_ARGS_plan_2: \"-var-file=../vars/$TF_STATE_NAME_2.tfvars\"\n  rules:\n    - if: $CI_COMMIT_TAG\n\n```\n\n**One important trick here is the strategy used for the new downstream pipeline.** We leave that `trigger:strategy` to its default value; otherwise, the [main pipeline](#the-main-pipeline) would wait for your [grand-child pipeline](#the-grand-child-pipeline) to finish. With a manual trigger, this could last for a very long time and make your pipeline dashboard harder to read and understand.\n\nYou have probably already wondered what is the content of that `.gitlab-ci/.second-layer.gitlab-ci.yml` file we are including here.  We will cover that in the next section.\n\n##### The first layer complete pipeline definition\n\nIf you are looking for a complete view of this first layer (stored in `.gitlab-ci/.first-layer.gitlab-ci.yml`), just expand the section below.\n\n```yml\nvariables:\n  TF_VAR_aws_ami_id: $AWS_AMI_ID\n  TF_VAR_aws_instance_type: $AWS_INSTANCE_TYPE\n  TF_VAR_aws_default_region: $AWS_DEFAULT_REGION\n\ninclude:\n  - template: Terraform.gitlab-ci.yml\n\ndefault:\n  cache:  # Use a shared cache or tagged runners to ensure terraform can run on apply and destroy\n    - key: cache-$CI_COMMIT_REF_SLUG\n      fallback_keys:\n        - cache-$CI_DEFAULT_BRANCH\n      paths:\n        - .\n\nstages:\n  - validate\n  - test\n  - build\n  - deploy\n  - cleanup\n  - 2nd_layer       # Use to deploy a 2nd environment on both the main branch and on the tags\n\nfmt:\n  rules:\n    - when: always\n\nvalidate:\n  rules:\n    - when: always\n\nkics-iac-sast:\n  rules:\n    - if: $SAST_DISABLED == 'true' || $SAST_DISABLED == '1'\n      when: never\n    - if: $SAST_EXCLUDED_ANALYZERS =~ /kics/\n      when: never\n    - when: on_success\n\niac-sast:\n  rules:\n    - if: $SAST_DISABLED == 'true' || $SAST_DISABLED == '1'\n      when: never\n    - if: $SAST_EXCLUDED_ANALYZERS =~ /kics/\n      when: never\n    - when: on_success\n\n###########################################################################################################\n## Integration env. and Staging. env\n##  * Auto-deploy to Integration on merge to main.\n##  * Auto-deploy to Staging on tag.\n##  * Integration can be manually destroyed if TF_DESTROY is set to true.\n##  * Destroy of next env. is not automated to prevent errors.\n###########################################################################################################\nbuild:  # terraform plan\n  environment:\n    name: $TF_STATE_NAME\n    action: prepare\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n    - if: $CI_COMMIT_TAG\n\ndeploy: # terraform apply --> automatically deploy on corresponding env (integration or staging) when merging to default branch or tagging. Second layer environments (qa and production) will be controlled manually\n  environment:\n    name: $TF_STATE_NAME\n    action: start\n    on_stop: destroy\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n    - if: $CI_COMMIT_TAG\n\ndestroy:\n  extends: .terraform:destroy\n  variables:\n    GIT_STRATEGY: none\n  dependencies:\n    - build\n  environment:\n    name: $TF_STATE_NAME\n    action: stop\n  rules:\n    - if: $CI_COMMIT_TAG  # Do not destroy production\n      when: never\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH && $TF_DESTROY == \"true\" # Manually destroy integration env.\n      when: manual\n###########################################################################################################\n\n###########################################################################################################\n## Dev env.\n##  * Temporary environment. Lives and dies with the Merge Request.\n##  * Auto-deploy on push to feature branch.\n##  * Auto-destroy on when Merge Request is closed.\n###########################################################################################################\nbuild_review:\n  extends: build\n  rules:\n    - if: $CI_COMMIT_TAG\n      when: never\n    - if: $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH\n      when: on_success\n\ndeploy_review:\n  extends: deploy\n  dependencies:\n    - build_review\n  environment:\n    name: $environment\n    action: start\n    on_stop: destroy_review\n    # url: https://$CI_ENVIRONMENT_SLUG.example.com\n  rules:\n    - if: $CI_COMMIT_TAG\n      when: never\n    - if: $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH\n      when: on_success\n\ndestroy_review:\n  extends: destroy\n  dependencies:\n    - build_review\n  environment:\n    name: $environment\n    action: stop\n  rules:\n    - if: $CI_COMMIT_TAG  # Do not destroy production\n      when: never\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH   # Do not destroy staging\n      when: never\n    - when: manual\n###########################################################################################################\n\n###########################################################################################################\n## Second layer\n##  * Deploys from main branch to qa env.\n##  * Deploys from tag to production.\n###########################################################################################################\n.2nd_layer:\n  stage: 2nd_layer\n  variables:\n    TF_ROOT: terraform\n  trigger:\n    include: .gitlab-ci/.second-layer.gitlab-ci.yml\n    # strategy: depend            # Do NOT wait for the downstream pipeline to finish to mark upstream pipeline as successful. Otherwise, all pipelines will fail when reaching the pipeline timeout before deployment to 2nd layer.\n    forward:\n      yaml_variables: true      # Forward variables defined in the trigger job\n      pipeline_variables: true  # Forward manual pipeline variables and scheduled pipeline variables\n\nqa:\n  extends: .2nd_layer\n  variables:\n    TF_STATE_NAME_2: qa\n    environment: $TF_STATE_NAME_2\n    TF_CLI_ARGS_plan_2: \"-var-file=../vars/$TF_STATE_NAME_2.tfvars\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n\nproduction:\n  extends: .2nd_layer\n  variables:\n    TF_STATE_NAME_2: production\n    environment: $TF_STATE_NAME_2\n    TF_CLI_ARGS_plan_2: \"-var-file=../vars/$TF_STATE_NAME_2.tfvars\"\n  rules:\n    - if: $CI_COMMIT_TAG\n###########################################################################################################\n```\n\nAt this stage, we are already deploying safely to three environments. That is my personal ideal recommendation. However, if you need more environments, add them to your CD pipeline.\n\nYou have certainly already noted that we include a downstream pipeline with the keyword `trigger:include`. This includes the file `.gitlab-ci/.second-layer.gitlab-ci.yml`. We want to run almost the same pipeline so obviously, its content is very similar to the one we have detailed above. The main advantage here to define this [grand-child pipeline](#the-grand-child-pipeline) is that it lives on its own, making both variables and rules way easier to define.\n\n### The grand-child pipeline\n\nThis second layer pipeline is a brand new pipeline. Hence, it needs to mimic the first layer definition with:\n\n* [Inclusion of the Terraform template](#run-terraform-commands-and-secure-the-code).\n* [Enforcement of security checks](#run-controls-on-all-branches). Terraform validation would be duplicates of the first layer, but security scanners may find threats that did not yet exist when scanners previously ran (for example, if you deploy to production a couple of days after your deployment to staging).\n* [Overwrite build and deploy jobs to set specific rules](#cd-to-review-environments). Note that the `destroy` stage is no longer automated to prevent too fast deletions.\n\nAs explained above, the `TF_STATE_NAME` and `TF_CLI_ARGS_plan` have been provided from the [main pipeline](#the-main-pipeline) to the [child pipeline](#the-child-pipeline). We needed another variable name to pass these values from the [child pipeline](#the-child-pipeline) to here, the [grand-child pipeline](#the-grand-child-pipeline). This is why they are postfixed with `_2` in the child pipeline and the value is copied back to the appropriate variable during the `before_script` here.\n\nSince we have already broken down each step above, we can zoom out here directly to the broad view of the global second layer definition (stored in `.gitlab-ci/.second-layer.gitlab-ci.yml`).\n\n```yml\n# Use to deploy a second environment on both the default branch and the tags.\n\ninclude:\n  template: Terraform.gitlab-ci.yml\n\nstages:\n  - validate\n  - test\n  - build\n  - deploy\n\nfmt:\n  rules:\n    - when: never\n\nvalidate:\n  rules:\n    - when: never\n\nkics-iac-sast:\n  rules:\n    - if: $SAST_DISABLED == 'true' || $SAST_DISABLED == '1'\n      when: never\n    - if: $SAST_EXCLUDED_ANALYZERS =~ /kics/\n      when: never\n    - when: always\n\n###########################################################################################################\n## QA env. and Prod. env\n##  * Manually trigger build and auto-deploy in QA\n##  * Manually trigger both build and deploy in Production\n##  * Destroy of these env. is not automated to prevent errors.\n###########################################################################################################\nbuild:  # terraform plan\n  cache:  # Use a shared cache or tagged runners to ensure terraform can run on apply and destroy\n    - key: $TF_STATE_NAME_2\n      fallback_keys:\n        - cache-$CI_DEFAULT_BRANCH\n      paths:\n        - .\n  environment:\n    name: $TF_STATE_NAME_2\n    action: prepare\n  before_script:  # Hack to set new variable values on the second layer, while still using the same variable names. Otherwise, due to variable precedence order, setting new value in the trigger job, does not cascade these new values to the downstream pipeline\n    - TF_STATE_NAME=$TF_STATE_NAME_2\n    - TF_CLI_ARGS_plan=$TF_CLI_ARGS_plan_2\n  rules:\n    - when: manual\n\ndeploy: # terraform apply\n  cache:  # Use a shared cache or tagged runners to ensure terraform can run on apply and destroy\n    - key: $TF_STATE_NAME_2\n      fallback_keys:\n        - cache-$CI_DEFAULT_BRANCH\n      paths:\n        - .\n  environment:\n    name: $TF_STATE_NAME_2\n    action: start\n  before_script:  # Hack to set new variable values on the second layer, while still using the same variable names. Otherwise, due to variable precedence order, setting new value in the trigger job, does not cascade these new values to the downstream pipeline\n    - TF_STATE_NAME=$TF_STATE_NAME_2\n    - TF_CLI_ARGS_plan=$TF_CLI_ARGS_plan_2\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n    - if: $CI_COMMIT_TAG && $TF_AUTO_DEPLOY == \"true\"\n    - if: $CI_COMMIT_TAG\n      when: manual\n###########################################################################################################\n```\n\nEt voilà. **We are ready to go.** Feel free to change the way you control your job executions, leveraging for example GitLab's capacity to [delay a job](https://docs.gitlab.com/ee/ci/jobs/job_control.html#run-a-job-after-a-delay) before deploying to production.\n\n## Try it yourself\n\nWe finally reached our destination. We are now able to control **deployments to five different environments**, with only the **feature branches**, the **main branch**, and **tags**.\n* We are intensively reusing GitLab open source templates to ensure efficiency and security in our pipelines.\n* We are leveraging GitLab template capacities to overwrite only the blocks that need custom control.\n* We have split the pipeline in small chunks, controlling the downstream pipelines to match exactly what we need.\n\nFrom there, the floor is yours. You could, for example, easily update the main pipeline to trigger downstream pipelines for your software source code, with the [trigger:rules:changes](https://docs.gitlab.com/ee/ci/yaml/#ruleschanges) keyword. And use another [template](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/) depending on the changes that happened. 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statement",{"items":682},[683,686,689],{"text":684,"config":685},"Terms",{"href":512,"dataGaName":513,"dataGaLocation":460},{"text":687,"config":688},"Cookies",{"dataGaName":522,"dataGaLocation":460,"id":523,"isOneTrustButton":30},{"text":690,"config":691},"Privacy",{"href":517,"dataGaName":518,"dataGaLocation":460},[693],{"id":694,"title":695,"body":8,"config":696,"content":698,"description":8,"extension":28,"meta":702,"navigation":30,"path":703,"seo":704,"stem":705,"__hash__":706},"blogAuthors/en-us/blog/authors/olivier-dupr.yml","Olivier Dupr",{"template":697},"BlogAuthor",{"name":18,"config":699},{"headshot":700,"ctfId":701},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750713474/cj6odchlpoqxbibenvye.png","4VIckvQsyfNxEtz4pM42aP",{},"/en-us/blog/authors/olivier-dupr",{},"en-us/blog/authors/olivier-dupr","KYUHajPcOeVlPPyPD8D4H56u7iQpJSPInWLi38Y1NA0",[708,722,735],{"content":709,"config":720},{"title":710,"description":711,"authors":712,"heroImage":714,"date":715,"body":716,"category":9,"tags":717},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[713],"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 difficulat 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/)",[27,718,719],"product","features",{"featured":12,"template":13,"slug":721},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":723,"config":733},{"title":724,"description":725,"authors":726,"heroImage":728,"date":729,"category":9,"tags":730,"body":732},"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.",[727],"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",[264,614,731],"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/) 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":734,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":736,"config":744},{"title":737,"description":738,"authors":739,"heroImage":740,"date":741,"category":9,"tags":742,"body":743},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[727],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[614,264,718],"Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":745,"featured":30,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":747},[748,762,773],{"id":749,"categories":750,"header":752,"text":753,"button":754,"image":759},"ai-modernization",[751],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":755,"config":756},"Get your AI maturity score",{"href":757,"dataGaName":758,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":760},{"src":761},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":763,"categories":764,"header":765,"text":753,"button":766,"image":770},"devops-modernization",[718,560],"Are you just managing tools or shipping innovation?",{"text":767,"config":768},"Get your DevOps maturity score",{"href":769,"dataGaName":758,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":774,"categories":775,"header":777,"text":753,"button":778,"image":782},"security-modernization",[776],"security","Are you trading speed for security?",{"text":779,"config":780},"Get your security maturity score",{"href":781,"dataGaName":758,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":783},{"src":784},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":786,"blurb":787,"button":788,"secondaryButton":793},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":789,"config":790},"Get your free trial",{"href":791,"dataGaName":54,"dataGaLocation":792},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":498,"config":794},{"href":58,"dataGaName":59,"dataGaLocation":792},1773350828182]