[{"data":1,"prerenderedAt":795},["ShallowReactive",2],{"/en-us/blog/efficient-devsecops-workflows-with-rules-for-conditional-pipelines":3,"navigation-en-us":44,"banner-en-us":444,"footer-en-us":454,"blog-post-authors-en-us-Abubakar Siddiq Ango":693,"blog-related-posts-en-us-efficient-devsecops-workflows-with-rules-for-conditional-pipelines":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":37,"tagSlugs":38,"__hash__":43},"blogPosts/en-us/blog/efficient-devsecops-workflows-with-rules-for-conditional-pipelines.yml","Efficient Devsecops Workflows With Rules For Conditional Pipelines",[7],"abubakar-siddiq-ango",null,"engineering",{"slug":11,"featured":12,"template":13},"efficient-devsecops-workflows-with-rules-for-conditional-pipelines",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to create efficient DevSecOps workflows with rules for conditional CI/CD pipelines","CI/CD pipelines can be simple or complex, what makes them efficient are CI rules that define when and how they run.",[18],"Abubakar Siddiq Ango","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669673/Blog/Hero%20Images/engineering.png","2023-06-27","CI/CD pipelines can be simple or complex – what makes them efficient are rules that define when and how they run. By using rules, you create smarter CI/CD pipelines, which increase teams' productivity and allow organizations to iterate faster. In this tutorial, you will learn about the different types of CI/CD pipelines and rules and their use cases.\n\n## What is a pipeline?\nA pipeline is a top-level component of [continuous integration](https://docs.gitlab.com/ee/ci/introduction/index.html#continuous-integration) and [continuous delivery](https://docs.gitlab.com/ee/ci/introduction/index.html#continuous-delivery)/[continuous deployment](https://docs.gitlab.com/ee/ci/introduction/index.html#continuous-deployment), and it comprises [jobs](https://docs.gitlab.com/ee/ci/jobs/index.html), which are lists of tasks to be executed. Jobs are organized in [stages](https://docs.gitlab.com/ee/ci/yaml/index.html#stages), which define when the jobs run.\n\nA pipeline can be a [basic one](https://docs.gitlab.com/ee/ci/pipelines/pipeline_architectures.html#basic-pipelines) in which jobs run concurrently in each stage. Pipelines can also be complex, like [parent-child pipelines](https://docs.gitlab.com/ee/ci/pipelines/downstream_pipelines.html#parent-child-pipelines), [merge trains](https://docs.gitlab.com/ee/ci/pipelines/merge_trains.html), [multi-project pipelines](https://docs.gitlab.com/ee/ci/pipelines/downstream_pipelines.html#multi-project-pipelines), or the more advanced [Directed Acyclic Graph pipelines](https://docs.gitlab.com/ee/ci/directed_acyclic_graph/index.html) (DAG).\n\n![Complex pipeline showing dependencies](https://about.gitlab.com/images/blogimages/2023-06-15-efficient-devsecops-workflows-with-rules-for-conditional-pipelines/complex-pipelines.png)\n\nA [gitlab-runner pipeline](https://gitlab.com/gitlab-org/gitlab-runner/-/pipelines/798871212/) showing job dependencies.\n\n\n![Directed Acyclic Graph](https://about.gitlab.com/images/blogimages/2023-06-15-efficient-devsecops-workflows-with-rules-for-conditional-pipelines/dag-pipelines.png)\n\nDirected Acyclic Graph pipeline\n\n\nUse cases determine how complicated a pipeline can get. A use case might require testing an application and packaging it into a container; the pipeline can even further deploy the container to an orchestrator like Kubernetes or a container registry. Another use case might involve building applications that target different platforms with varying dependencies, which is where DAG pipelines shine.\n\n## What are CI/CD rules?\nCI/CD rules are the key to managing the flow of jobs in a pipeline. One of the powerful features of GitLab CI/CD is the ability to control when a CI/CD job runs, which can depend on context, changes made, [workflow](https://docs.gitlab.com/ee/ci/yaml/workflow.html) rules, values of CI/CD variables, or custom conditions. Aside from using `rules`, you can also control the flow of CI/CD pipelines using:\n\n* [`needs`](https://docs.gitlab.com/ee/ci/yaml/index.html#needs): establishes relationships between jobs and used in DAG pipelines\n* [`only`](https://docs.gitlab.com/ee/ci/yaml/index.html#only--except): defines when a job should run\n* [`except`](https://docs.gitlab.com/ee/ci/yaml/index.html#only--except): defines when a job should not run\n* [`workflow`](https://docs.gitlab.com/ee/ci/yaml/workflow.html): controls when pipelines are created\n\n`only` and `except` should not be used with `rules` as this can lead to unexpected behavior. It is recommended to use `rules`, learn more in the following sections.\n\n## What is the `rules` feature?\n`rules` determine when and if a job runs in a pipeline. If you have multiple rules defined, they are all evaluated in order until a matching rule is found and the job is executed according to the specified configuration.\n\n[Rules](https://docs.gitlab.com/ee/ci/yaml/#rules) can be defined using the keywords: `if`, `changes`, `exists`, `allow_failure`, `variables`, `when` and `needs`.\n\n### `rules:if`\nThe `if` keyword evaluates if a job should be added to a pipeline. The evaluation is done based on the values of [CI/CD variables](https://docs.gitlab.com/ee/ci/variables/index.html) defined in the scope of the job or pipeline and [predefined CI/CD variables](https://docs.gitlab.com/ee/ci/variables/predefined_variables.html).\n\n```yaml\njob:\n  script:\n    - echo $(date)\n  rules:\n    - if: $CI_MERGE_REQUEST_SOURCE_BRANCH_NAME == $CI_DEFAULT_BRANCH\n\n```\n\nIn the CI/CD script above, the job prints the current date and time with the `echo` command. The job is only executed if the source branch of a merge request (`CI_MERGE_REQUEST_SOURCE_BRANCH_NAME`) is the same as the project's default branch (`CI_DEFAULT_BRANCH`) in a [merge request pipeline](https://docs.gitlab.com/ee/ci/pipelines/merge_request_pipelines.html). You can use the `==` and `!=` operators for comparison, while `=~` and `!~` allow you to compare a variable to a regular expression. You can combine multiple expressions using the `&&` (AND), `||` (OR) operators, and parentheses for grouping expressions.\n\n### `rules:changes`\nWith the `changes` keyword, you can watch for changes to certain files or folders for a job to execute. GitLab uses the output of [Git diffstat](https://git-scm.com/docs/git-diff#Documentation/git-diff.txt\n\n```yaml\njob:\n  script:\n    - terraform plan\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n      changes:\n        - terraform/**/*.tf\n\n```\n\nIn this example, the `terraform plan` is only executed when files with the `.tf` extension are changed in the `terraform` folder and its subdirectories. An additional rule ensures the job is executed for [merge request pipelines](https://docs.gitlab.com/ee/ci/pipelines/merge_request_pipelines.html).\n\nThe `changes` rule can look for changes in specific files with `paths`:\n\n```yaml\njob:\n  script:\n    - terraform plan\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n      changes:\n        paths:\n          - terraform/main.tf\n\n```\n\nChanges to files in a source reference (branch, tag, commit) can also be compared against other references in the Git repository. The CI/CD job will only execute when the source reference differs from the [specified reference value defined in `rules:changes:compare_to`](https://docs.gitlab.com/ee/ci/yaml/#ruleschangescompare_to). This value can be a Git commit SHA, tag, or branch name. The following example compares the source reference to the current `production` branch (`refs/head/production`).\n\n```yaml\njob:\n  script:\n    - terraform plan\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n      changes:\n        paths:\n          - terraform/main.tf\n        compare_to: 'refs/head/production'\n\n```\n\n### `rules:exists`\nLike `changes`, you can execute CI/CD jobs only when specific files exist [using `rules:exists` rules](https://docs.gitlab.com/ee/ci/yaml/#rulesexists). For example, you can run a job that checks whether a `Gemfile.lock` file exists. The following example audits a Ruby project for vulnerable versions of gems or insecure gem sources using the [bundler-audit project](https://github.com/rubysec/bundler-audit).\n\n```yaml\njob:\n  script:\n    - bundle-audit check --format json --output bundle-audit.json\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n      changes:\n        exits:\n          - Gemfile.lock\n\n```\n\n### `rules:allow_failure`\nThere are scenarios where the failure of a job should not affect the following jobs and stages of the pipeline. This can be useful in use cases where non-blocking tasks are required as part of a project but don't impact the project in any way. The [`rules:allow_failure` rule](https://docs.gitlab.com/ee/ci/yaml/#rulesallow_failure) can be set to `true` or `false`. It defaults to `false` implicitly when the rule is not specified.\n\n```yaml\njob:\n  script:\n    - bundle-audit check --format json --output bundle-audit.json\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\" && $CI_MERGE_REQUEST_TARGET_BRANCH_PROTECTED == \"false\"\n      changes:\n        exits:\n          - Gemfile.lock\n      allow_failure: true\n\n```\n\nIn this example, the job can fail only if a merge request event triggers the pipeline and the target branch is not protected.\n\n### `rules:needs`\nDisabled by fault, [`rules:needs`](https://docs.gitlab.com/ee/ci/yaml/#rulesneeds) was introduced in [GitLab 16](https://about.gitlab.com/releases/2023/05/22/gitlab-16-0-released/) and can be enabled with the `introduce_rules_with_needs` [feature flag](https://docs.gitlab.com/ee/user/feature_flags.html). [`needs`](https://docs.gitlab.com/ee/ci/yaml/index.html#needs) is used to execute jobs out of order without waiting for other jobs in a stage to complete. When used with `rules`, it replaces the job's `needs` specification when the set conditions are met.\n\n```yaml\nstages:\n  - build\n  - qa\n  - deploy\n\nbuild-dev:\n  stage: build\n  rules:\n    - if: $CI_COMMIT_BRANCH != $CI_DEFAULT_BRANCH\n  script: echo \"Building dev version...\"\n\nbuild-prod:\n  stage: build\n  rules:\n    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH\n  script: echo \"Building production version...\"\n\nqa-checks:\n  stage: qa\n  script:\n    - echo \"Running QA checks before publishing to Production....\"\n\ndeploy:\n  stage: deploy\n  needs: ['build-dev']\n  rules:\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH\n      needs: ['build-prod', 'qa-checks']\n    - when: on_success # Run the job in other cases\n  script: echo \"Deploying application.\"\n\n```\n\nIn the example above, the deploy job has the `build-dev` job as a dependency before it runs; however, when the commit branch is the project's default branch, its dependency changes to `build-prod` and `qa-checks`. This can allow for extra checks to be implemented based on context.\n\n### `rules:variables`\nIn some situations, you only need certain variables in specific conditions, or their values change based on content; you can use the [`rules:variables`](https://docs.gitlab.com/ee/ci/yaml/#rulesvariables) rule to define variables when specific conditions are met. This also allows to create more dynamic CI/CD execution workflows.\n\n```yaml\njob:\n  variables:\n    DEPLOY_VERSION: \"dev\"\n  rules:\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH\n      variables:\n        DEPLOY_VERSION: \"stable\"\n  script:\n    - echo \"Deploying $DEPLOY_VERSION version\"\n\n```\n\n### `workflow:rules`\nSo far, we have looked at controlling when jobs run in a pipeline using the `rules` keyword. Sometimes, you want to control how the entire pipeline behaves: That's where [`workflow:rules` provide a powerful option](https://docs.gitlab.com/ee/ci/yaml/#workflowrules). `workflow:rules` are evaluated before jobs and take precedence over the job rules. For example, if a job has rules that allow it to run against a specific branch, but the workflow rules set jobs running against the branch to `when: never`, the jobs will not run.\n\nAll the features of `rules` mentioned in the previous sections work for `workflow:rules`.\n\n```yaml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n      when: never\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n      when: never\n    - when: always\n\n```\n\nIn the example above, the CI/CD pipeline runs except when a schedule or push event is triggered.\n\n## Use cases for CI/CD rules\nIn the previous section, we looked at different ways of using the `rules` feature of GitLab CI/CD. In this section, we will explore practical use cases.\n\n### Developer experience\nOne of the benefits of a DevSecOps platform is to allow developers to focus on what they do best: writing their code and doing as little operations as possible. A company's DevOps or Platform team can create CI/CD templates for different stages of their development lifecycle and use rules to add CI/CD jobs to handle specific tasks based on their technology stack. A developer only needs to include a default CI/CD script and pipelines are automatically created based on files detected, refs used, or defined variables, leading to increased productivity.\n\n### Security and quality assurance\nA major function of CI/CD pipelines is to catch bugs or vulnerabilities before they are deployed into production infrastructure. Using CI/CD rules, security and quality assurance teams can dynamically run extra checks on changes introduced when certain factors are introduced. For example, malware scans can be added when new file extensions not in an approved list are detected, or more advanced performance tests are automatically added when a certain level of change has been introduced to the codebase. With GitLab's built-in security, including security in your pipelines can be done with just a few lines of code.\n\n```yaml\ninclude:\n  # Static\n  - template: Jobs/Container-Scanning.gitlab-ci.yml\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n  - template: Jobs/SAST-IaC.gitlab-ci.yml\n  - template: Jobs/Code-Quality.gitlab-ci.yml\n  - template: Security/Coverage-Fuzzing.gitlab-ci.yml\n  # Dynamic\n  - template: Security/DAST.latest.gitlab-ci.yml\n  - template: Security/BAS.latest.gitlab-ci.yml\n  - template: Security/DAST-API.latest.gitlab-ci.yml\n  - template: API-Fuzzing.latest.gitlab-ci.yml\n\n```\n\n### Automation\nThe power of CI/CD rules shines through in the (nearly) limitless possibilities of automating your CI/CD pipelines. GitLab [AutoDevOps](https://docs.gitlab.com/ee/topics/autodevops/) is an example. It uses an opinionated best-practice collection of [GitLab CI/CD templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates) and rules to detect the technology stack used. AutoDevOps creates relevant jobs that take your application all the way to production from a push. You can review the [AutoDevOps template](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/Auto-DevOps.gitlab-ci.yml) to learn how it leverages CI/CD rules for greater efficiency.\n\n### Using CI/CD components\nGrowth comes with several iterations of work and creating best practices. While building CI/CD pipelines, your DevOps team would have made several CI/CD scripts that they repurpose across pipelines using the [`include`](https://docs.gitlab.com/ee/ci/yaml/#include) keyword. In [GitLab 16](https://about.gitlab.com/releases/2023/05/22/gitlab-16-0-released/), GitLab [introduced CI/CD Components](https://about.gitlab.com/releases/2023/05/22/gitlab-16-0-released/#cicd-components), an experimental feature that allows your team to create reusable CI/CD components and publish them as a catalog that can be used to build smarter CI/CD pipelines rapidly. You can learn more [about using CI/CD components](https://docs.gitlab.com/ee/ci/components/) and the [component catalog direction](https://about.gitlab.com/direction/verify/component_catalog/).\n\nGitLab CI/CD enables you to run smarter pipelines, and it does so together with [GitLab Duo, AI-powered workflows](/gitlab-duo/) to help you build more secure software, faster.\n",[23,24,25,26,27],"tutorial","CI","CD","DevSecOps","DevSecOps platform","yml",{},true,"/en-us/blog/efficient-devsecops-workflows-with-rules-for-conditional-pipelines",{"title":33,"description":16,"ogTitle":33,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":34,"ogSiteName":35,"ogType":36,"canonicalUrls":34},"DevSecOps workflows with conditional CI/CD pipeline 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statement",{"items":683},[684,687,690],{"text":685,"config":686},"Terms",{"href":514,"dataGaName":515,"dataGaLocation":462},{"text":688,"config":689},"Cookies",{"dataGaName":524,"dataGaLocation":462,"id":525,"isOneTrustButton":30},{"text":691,"config":692},"Privacy",{"href":519,"dataGaName":520,"dataGaLocation":462},[694],{"id":695,"title":18,"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/abubakar-siddiq-ango.yml",{"template":697},"BlogAuthor",{"name":18,"config":699},{"headshot":700,"ctfId":701},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660104/Blog/Author%20Headshots/abuango-headshot.jpg","abuango",{},"/en-us/blog/authors/abubakar-siddiq-ango",{},"en-us/blog/authors/abubakar-siddiq-ango","u5Jv4JxCpfmcGQuXEU4Lr5xVBJP9LAB2NkXRMLeYwPE",[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/)",[23,718,719],"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",[266,615,731],"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",[615,266,718],"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":248},"/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",[718,41],"Are you just managing tools or shipping innovation?",{"text":767,"config":768},"Get your DevOps maturity score",{"href":769,"dataGaName":758,"dataGaLocation":248},"/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":248},"/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":55,"dataGaLocation":792},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":500,"config":794},{"href":59,"dataGaName":60,"dataGaLocation":792},1773350818701]