[{"data":1,"prerenderedAt":801},["ShallowReactive",2],{"/en-us/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines":3,"navigation-en-us":39,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-William Arias|Daniel Helfand":687,"blog-related-posts-en-us-variable-and-artifact-sharing-in-gitlab-parent-child-pipelines":713,"assessment-promotions-en-us":752,"next-steps-en-us":791},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":28,"isFeatured":12,"meta":29,"navigation":12,"path":30,"publishedDate":22,"seo":31,"stem":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines.yml","Variable And Artifact Sharing In Gitlab Parent Child Pipelines",[7,8],"william-arias","daniel-helfand",null,"engineering",{"featured":12,"template":13,"slug":14},true,"BlogPost","variable-and-artifact-sharing-in-gitlab-parent-child-pipelines",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"Variable and artifact sharing in GitLab parent-child pipelines","Learn how to simplify complex CI/CD pipelines with these best practices for sharing data in more modular pipeline setups. ",[19,20],"William Arias","Daniel Helfand","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664198/Blog/Hero%20Images/Self-Hosted_1800x945.png","2025-10-16","Software projects have different evolving needs and requirements. Some have\nsaid that *software is never finished, merely abandoned*. Some software\nprojects are small and others are large with complex integrations. Some have\ndependencies on external projects, while others are self-contained.\nRegardless of the size and complexity, the need to validate and ensure\nfunctionality remains paramount. \n\nCI/CD pipelines can help with the challenge of building and validating software projects consistently, but, much like the software itself, these pipelines can become complex with many dependencies. This is where ideas like [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) and data exchange in CI/CD setups become incredibly important.\n\nIn this article, we will cover common CI/CD data exchange challenges users may encounter with parent-child pipelines in GitLab — and how to solve them. You'll learn how to turn complex CI/CD processes into more manageable setups. \n\n## Using parent-child pipelines\n\nThe pipeline setup in the image below illustrates a scenario where a project could require a large, complex pipeline. The whole project resides in one repository and contains different modules. Each module requires its own set of build and test automation steps. \n\nOne approach to address the CI/CD configuration in a scenario like this is to break down the larger pipeline into smaller ones (i.e., child pipelines) and keep a common CI/CD process that is shared across all modules in charge of the whole orchestration (i.e., parent pipeline).\n\n![CI/CD configuration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760617772/hizwvhmgxn6exbmvsnrv.png)\n\nThe parent-child pipeline pattern allows a single pipeline to orchestrate one or many downstream pipelines. Similar to how a single pipeline coordinates the execution of multiple [jobs](https://docs.gitlab.com/ci/jobs/), the parent pipeline coordinates the running of full pipelines with one or more jobs.\n\nThis pattern has been shown to be helpful in a variety of use cases:\n\n* Breaking down large, complex pipelines into smaller, manageable pieces  \n\n* Conditionally executing certain pipelines as part of a larger CI/CD process  \n\n* Executing pipelines in parallel  \n\n* Helping manage user permissions to access and run certain pipelines \n\nGitLab’s current CI/CD structure supports this pattern and makes it simple to implement parent-child pipelines. While there are many benefits when using the parent-child pipeline pattern with GitLab, one question we often get is how to share data between the parent and child pipelines. In the next sections, we’ll go over how to make use of GitLab variables and artifacts to address this concern.\n\n### Sharing variables\n\nThere are cases where it is necessary to pass the output from a parent pipeline job to a child pipeline. These outputs can be shared as variables, [artifacts](https://docs.gitlab.com/ci/jobs/job_artifacts/), and [inputs](https://docs.gitlab.com/ci/inputs/).\n\nConsider a case where we create a custom variable `var_1` during the runtime of a job:\n\n```yaml\n\nstages:\n  - build\n  - triggers\n\n# This job only creates a variable \n\ncreate_var_job:\n  stage: build\n  script:\n    - var_1=\"Hi, I'm a Parent pipeline variable\"\n    - echo \"var_1=$var_1\" >> var.env\n  artifacts:\n    reports:\n      dotenv: var.env\n```\n\nNotice that the variable is created as part of the script steps in the job (during runtime). In this example, we are using a simple string `\"Hi, I'm a Parent pipeline variable\"` to illustrate the main syntax required to later share this variable with a child pipeline. Let's break down the `create_var_job`  and analyze the main steps from this GitLab job \n\nFirst, we need to save `var_1` as `dotenv`:\n\n```yaml\n  script:\n    - var_1=\"Hi, I'm a pipeline variable\"\n    - echo \"var_1=$var_1\" >> var.env\n```\n\nAfter saving `var_1` as `var.env`, the next important step is to make this variable available as an artifact produced by the `create_var_job`. To do that, we use the following syntax: \n\n```yaml\n\nartifacts:\n    reports:\n      dotenv: var.env\n```\n\nUp to this point, we have created a variable during runtime and saved it as a `dotenv` report. Now let's add the job that should trigger the child pipeline:\n\n```yaml\n\ntelco_service_a:\n  stage: triggers\n  trigger:\n    include: service_a/.gitlab-ci.yml\n  rules:\n    - changes:\n        - service_a/*\n```\n\nThe goal of `telco_service_a`  job is to find the `.gitlab-ci.yml` configuration of the child pipeline,  which is defined in this case as `service_a,` and trigger its execution. Let's examine this job: \n\n```yaml\n\ntelco_service_a:\n  stage: triggers\n  trigger:\n    include: service_a/.gitlab-ci.yml\n```\n\nWe see it belongs to another `stage` of the pipeline named `triggers.`This job will run only after `create_var_job` from the first stage successfully finishes and where the variable  `var_1` we want to pass is created.\n\nAfter defining the stage, we use the reserved words `trigger` and `include` to tell GitLab where to search for the child pipeline configuration, as illustrated in the YAML below:\n\n```yaml\n  trigger:\n    include: service_a/.gitlab-ci.yml\n```\n\nOur child-pipeline YAML configuration is under `service_a/.gitlab-ci.yml` folder in the GitLab repository, for this example. \n\n![child-pipeline YAML configuration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760617772/ujkirpbifthpuujkcm6f.png)\n\n\u003Cp>\u003C/p>\n\n\u003Ccenter>\u003Ci>Child pipelines folders with configurations\u003C/i>\u003C/center>\n\n\u003Cp>\u003C/p>\n\nTake into consideration that the repository structure depicted above can vary. What matters is properly pointing the  `triggers: include` properties at the location of your child-pipeline configuration in your repository.\n\nFinally, we use `rules: changes` to indicate to GitLab that this child pipeline should be triggered only if there is any change in any file in the `service_a/.gitlab-ci.yml` directory, as illustrated in the following code snippet:\n\n```yaml\n\nrules:\n    - changes:\n        - service_a/*\n```\n\nUsing this rule helps to optimize cost by triggering the child pipeline job only when necessary. This approach is particularly valuable in a monorepo architecture where specific modules contain numerous components, allowing us to avoid running their dedicated pipelines when no changes have been made to their respective codebases.\n\n#### Configuring the parent pipeline \n\nUp to this point, we have put together our parent pipeline. Here's the full code snippet for this segment:\n\n```yaml\n\n# Parent Pipeline Configuration\n\n# This pipeline creates a custom variable and triggers a child pipeline\n\n\nstages:\n  - build\n  - trigger\n\ncreate_var_job:\n  stage: build\n  script:\n    - var_1=\"Hi, I'm a Parent pipeline variable\"\n    - echo \"var_1=$var_1\" >> var.env\n  artifacts:\n    reports:\n      dotenv: var.env\n\ntelco_service_a:\n  stage: triggers\n  trigger:\n    include: service_a/.gitlab-ci.yml\n  rules:\n    - changes:\n        - service_a/*\n```\n\nWhen GitLab executes the YAML configuration in the GitLab UI, the parent pipeline gets rendered as follows:\n\n![parent pipeline rendering](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760617771/e1azkkr0rnzd42dzkw1x.png)\n\nNotice the label \"trigger job,\" which indicates this job will start the execution of another pipeline configuration.\n\n#### Configuring the child pipeline \n\nMoving forward, let's now focus on the child pipeline configuration, where we expect to inherit and print the value of the `var_1` created in the parent pipeline.\n\nThe pipeline configuration in `service_a/.gitlab_ci.yml` has the following definition:\n\n```yaml\n\nstages:\n  - build\n\nbuild_a:\n  stage: build\n  script:\n    - echo \"this job inherits the variable from the Parent pipeline:\"\n    - echo $var_1\n  needs:\n    - project: gitlab-da/use-cases/7-4-parent-child-pipeline\n      job: create_var_job\n      ref: main\n      artifacts: true\n```\n\nLike before, let's break down this pipeline and highlight the main parts to achieve our goal. This pipeline only contains one stage (i.e., `build)` and one job (i.e., `build_a)`. The script in the job contains two steps:\n\n```yaml\n\nbuild_a:\n  stage: build\n  script:\n    - echo \"this job inherits the variable from the Parent pipeline:\"\n    - echo $var_1\n```\n\nThese two steps print output during the execution. The most interesting one is the second step, `echo $var_1`, where we expect to print the variable value inherited from the parent pipeline. Remember, this was a simple string with value: `\"Hi, I'm a Parent pipeline variable.\"` \n\n#### Inheriting variables using needs\n\nTo set and link this job to inherit variables from the parent pipeline, we use the reserved GitLab CI properties `needs` as depicted in the following snippet:\n\n```yaml\n\nneeds:\n    - project: gitlab-da/use-cases/7-4-parent-child-pipeline\n      job: create_var_job\n      ref: main\n      artifacts: true\n```\n\nUsing the \"needs\" keyword, we define dependencies that must be completed before running this job. In this case, we pass four different values. Let's walk through each one  of them:\n\n* **Project:** The complete namespace of the project where the main `gitlab-ci.yml` containing the parent pipeline YAML is located. Make sure to include the absolute path.  \n\n* **Job:** The specific job name in the parent pipeline from where we want to inherit the variable.   \n\n* **Ref:** The name of the branch where the main `gitlab-ci.yml` containing the parent pipeline YAML is located.   \n\n* **Artifacts:** Where we set a boolean value, indicating that artifacts from the parent pipeline job should be downloaded and made available to this child pipeline job.\n\n**Note:** This specific approach using the needs property is only available to GitLab Premium and Ultimate users. We will cover another example for GitLab community users later on. \n\n#### Putting it all together \n\nNow let's assume we make a change to any of the files under `service_a` folder and commit the changes to the repository. When GitLab detects the change, the rule we set up will trigger the child job pipeline execution. This gets displayed in the GitLab UI as follows:\n\n![Rule triggering the child job pipeline execution](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760617771/e1azkkr0rnzd42dzkw1x.png)\n\nClicking on the `telco_service_a`  will take us to the jobs in the child pipeline:\n\n![Jobs in pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760617773/vftjkg7ct2wqmew1e3yk.png)\n\nWe can see the parent-child relationship, and finally, by clicking on the `build_a job`, we can visually verify the variable inheritance in the job execution log:\n\n![Verifying the variable inheritance in the job execution log](https://res.cloudinary.com/about-gitlab-com/image/upload/v1760617758/hxfkfmev9hebbqhgcvoh.png)\n\nThis output confirms the behavior we expected. The custom runtime variable `var_1` created in the parent job is inherited in the child job, unpacked from the `dotenv` report, and its value accessible as can be confirmed in Line 26 above.\n\nThis use case illustrates how to share custom variables that can contain any value between pipelines. This example is intentionally simple and can be extrapolated to more realistic scenarios. Take, for instance, the following CI/CD configuration, where the custom variable we need to share is the tag of a Docker image:\n\n```yaml\n\n# Pipeline \n\n\nbuild-prod-image:\n  tags: [ saas-linux-large-amd64 ]\n  image: docker:20.10.16\n  stage: build\n  services:\n    - docker:20.10.16-dind\n  \n  script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - docker build -t $PRODUCTION_IMAGE .\n    - docker push $PRODUCTION_IMAGE\n    - echo \"UPSTREAM_CONTAINER_IMAGE=$PRODUCTION_IMAGE\" >> prodimage.env\n\n  artifacts:\n    reports:\n      dotenv: prodimage.env\n\n  rules:\n      - if: '$CI_COMMIT_BRANCH == \"main\"'\n        when: always\n      - when: never\n```\n\nAnd use the variable with the Docker image tag, in another job that updates a Helm manifest file:\n\n```sql\n\nupdate-helm-values:\n    stage: update-manifests\n    image:\n        name: alpine:3.16\n        entrypoint: [\"\"]\n  \n    before_script:\n          - apk add --no-cache git curl bash yq\n          - git remote set-url origin https://${CI_USERNAME}:${GITOPS_USER}@${SERVER_PATH}/${PROJECT_PATH}\n          - git config --global user.email \"gitlab@gitlab.com\"\n          - git config --global user.name \"GitLab GitOps\"\n          - git pull origin main\n    script:\n          - cd src\n          - echo $UPSTREAM_CONTAINER_IMAGE\n          - yq eval -i \".spec.template.spec.containers[0].image |= \\\"$UPSTREAM_CONTAINER_IMAGE\\\"\" store-deployment.yaml\n          - cat store-deployment.yaml\n          - git pull origin main\n          - git checkout -B main\n          - git commit -am '[skip ci] prod image update'\n          - git push origin main\n    needs:\n      - project: gitlab-da/use-cases/devsecops-platform/simply-find/simply-find-front-end\n        job: build-prod-image\n        ref: main\n        artifacts: true\n```\n\nMastering how to share variables between pipelines while maintaining the relationship between them enables us to create more sophisticated workflow orchestration that can meet our software building needs. \n\n### Using GitLab Package Registry to share artifacts\n\nWhile the needs feature mentioned above works great for Premium and Ultimate users, GitLab also has features to help achieve similar results for Community Edition users. One suggested approach is to store artifacts in the [GitLab Package Registry](https://docs.gitlab.com/user/packages/package_registry/). \n\nUsing a combination of the variables provided in GitLab CI/CD jobs and the GitLab API, you can upload artifacts to the GitLab Package Registry from a parent pipeline. In the child pipeline, you can then access the uploaded artifact from the package registry using the same variables and API to access the artifact. Let’s take a look at the example pipeline and some supplementary scripts that illustrate this:\n\n**gitlab-ci.yml (parent pipeline)**\n\n```yaml\n\n# Parent Pipeline Configuration\n\n# This pipeline creates an artifact, uploads it to Package Registry, and triggers a child pipeline\n\n\nstages:\n  - create-upload\n  - trigger\n\nvariables:\n  PACKAGE_NAME: \"pipeline-artifacts\"\n  PACKAGE_VERSION: \"$CI_PIPELINE_ID\"\n  ARTIFACT_FILE: \"artifact.txt\"\n\n# Job 1: Create and upload artifact to Package Registry\n\ncreate-and-upload-artifact:\n  stage: create-upload\n  image: alpine:latest\n  before_script:\n    - apk add --no-cache curl bash\n  script:\n    - bash scripts/create-artifact.sh\n    - bash scripts/upload-to-registry.sh\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n\n# Job 2: Trigger child pipeline\n\ntrigger-child:\n  stage: trigger\n  trigger:\n    include: child-pipeline.yml\n    strategy: depend\n  variables:\n    PARENT_PIPELINE_ID: $CI_PIPELINE_ID\n    PACKAGE_NAME: $PACKAGE_NAME\n    PACKAGE_VERSION: $PACKAGE_VERSION\n    ARTIFACT_FILE: $ARTIFACT_FILE\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n```\n\n**child-pipeline.yml**\n\n```yaml\n\n# Child Pipeline Configuration\n\n# This pipeline downloads the artifact from Package Registry and processes it\n\n\nstages:\n  - download-process\n\nvariables:\n  # These variables are passed from the parent pipeline\n  PACKAGE_NAME: \"pipeline-artifacts\"\n  PACKAGE_VERSION: \"$PARENT_PIPELINE_ID\"\n  ARTIFACT_FILE: \"artifact.txt\"\n\n# Job 1: Download and process artifact from Package Registry\n\ndownload-and-process-artifact:\n  stage: download-process\n  image: alpine:latest\n  before_script:\n    - apk add --no-cache curl bash\n  script:\n    - bash scripts/download-from-registry.sh\n    - echo \"Processing downloaded artifact...\"\n    - cat $ARTIFACT_FILE\n    - echo \"Artifact processed successfully!\"\n```\n\n**upload-to-registry.sh**\n\n```bash\n\n#!/bin/bash\n\nset -e\n\n# Configuration\n\nPACKAGE_NAME=\"${PACKAGE_NAME:-pipeline-artifacts}\"\n\nPACKAGE_VERSION=\"${PACKAGE_VERSION:-$CI_PIPELINE_ID}\"\n\nARTIFACT_FILE=\"${ARTIFACT_FILE:-artifact.txt}\"\n\n# Validate required variables\n\nif [ -z \"$CI_PROJECT_ID\" ]; then\n    echo \"Error: CI_PROJECT_ID is not set\"\n    exit 1\nfi\n\nif [ -z \"$CI_JOB_TOKEN\" ]; then\n    echo \"Error: CI_JOB_TOKEN is not set\"\n    exit 1\nfi\n\nif [ -z \"$CI_API_V4_URL\" ]; then\n    echo \"Error: CI_API_V4_URL is not set\"\n    exit 1\nfi\n\nif [ ! -f \"$ARTIFACT_FILE\" ]; then\n    echo \"Error: Artifact file '$ARTIFACT_FILE' not found\"\n    exit 1\nfi\n\n# Construct the upload URL\n\nUPLOAD_URL=\"${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/packages/generic/${PACKAGE_NAME}/${PACKAGE_VERSION}/${ARTIFACT_FILE}\"\n\n# Upload the file using curl\n\nresponse=$(curl -w \"%{http_code}\" -o /tmp/upload_response.json \\\n    --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n    --upload-file \"$ARTIFACT_FILE\" \\\n    \"$UPLOAD_URL\")\n\nif [ \"$response\" -eq 201 ]; then\n    echo \"Upload successful!\"\nelse\n    echo \"Upload failed with HTTP code: $response\"\n    exit 1\nfi\n\n```\n\n**download-from-regsitry.sh**\n\n```bash\n\n#!/bin/bash\n\n\nset -e\n\n\n# Configuration\n\nPACKAGE_NAME=\"${PACKAGE_NAME:-pipeline-artifacts}\"\n\nPACKAGE_VERSION=\"${PACKAGE_VERSION:-$PARENT_PIPELINE_ID}\"\n\nARTIFACT_FILE=\"${ARTIFACT_FILE:-artifact.txt}\"\n\n\n# Validate required variables\n\nif [ -z \"$CI_PROJECT_ID\" ]; then\n    echo \"Error: CI_PROJECT_ID is not set\"\n    exit 1\nfi\n\n\nif [ -z \"$CI_JOB_TOKEN\" ]; then\n    echo \"Error: CI_JOB_TOKEN is not set\"\n    exit 1\nfi\n\n\nif [ -z \"$CI_API_V4_URL\" ]; then\n    echo \"Error: CI_API_V4_URL is not set\"\n    exit 1\nfi\n\n\nif [ -z \"$PACKAGE_VERSION\" ]; then\n    echo \"Error: PACKAGE_VERSION is not set\"\n    exit 1\nfi\n\n\n# Construct the download URL\n\nDOWNLOAD_URL=\"${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/packages/generic/${PACKAGE_NAME}/${PACKAGE_VERSION}/${ARTIFACT_FILE}\"\n\n\n# Download the file using curl\n\nresponse=$(curl -w \"%{http_code}\" -o \"$ARTIFACT_FILE\" \\\n    --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n    --fail-with-body \\\n    \"$DOWNLOAD_URL\")\n\nif [ \"$response\" -eq 200 ]; then\n    echo \"Download successful!\"\nelse\n    echo \"Download failed with HTTP code: $response\"\n    exit 1\nfi\n\n```\n\nIn this example, the parent pipeline uploads a file to the GitLab Package Registry by calling a script named `upload-to-registry.sh`. The script gives the artifact a name and version and constructs the API call to upload the file to the package registry. The parent pipeline is able to authenticate using a `$CI_JOB_TOKEN` to push the artifact.txt file to the registry. \n\nThe child pipeline operates the same as the parent pipeline by using a script to construct the API call to download the artifact.txt file from the package registry. It also is able to authenticate to the registry using the `$CI_JOB_TOKEN`. \n\nSince the GitLab Package Registry is available to all GitLab users, it helps to serve as a central location for storing and versioning artifacts. It is a great option for users working with many kinds of artifacts and needing to version artifacts for workflows even beyond CI/CD. \n\n### Using inputs to pass variables to a child pipeline\n\nIf you made it this far in this tutorial, and you have plans to start creating new pipeline configurations, you might want to start by evaluating if your use case can benefit from using **inputs** to pass variables to other pipelines. \n\nUsing inputs is a recommended way to pass variables when you need to define specific values in a CI/CD job and have those values remain fixed during the pipeline run. Inputs might offer certain advantages over the method we implemented before. For example, with inputs, you can include data validation through options (i.e., values must be one of these: \\[‘staging', ‘prod’\\]), variable descriptions, type checking, and assign default values before the pipeline run. \n\n#### Configuring CI/CD inputs\n\nConsider the following parent pipeline configuration:\n\n```text\n\n# .gitlab-ci.yml (main file)\n\nstages:\n  - trigger\n\ntrigger-staging:\n  stage: trigger\n  trigger:\n    include:\n      - local: service_a/.gitlab-ci.yml\n        inputs:\n          environment: staging\n          version: \"1.0.0\"\n```\n\nLet's zoom in at the main difference between the code snippet above and the previous parent pipeline examples in this tutorial: \n\n```yaml\n\ntrigger:\n    include:\n      - local: service_a/.gitlab-ci.yml\n        inputs:\n          environment: staging\n          version: \"1.0.0\"\n```\n\nThe main difference is using the reserved word \"inputs\". This part of the YAML configuration can be read in natural language as: “trigger the child pipeline defined in `service_a.gitlab-ci.yml` and make sure to pass ‘environment: staging’ and ‘version:1.0.0’ as input variables that the child pipeline will know how to use.\n\n#### Reading CI/CD inputs in child pipelines\n\nMoving to the child pipeline, it must contain in its declaration a spec that defines the inputs it can take. For each input, it is possible to add a little description, a set of predefined options the input value can take, and the type of value it will take. This is illustrated as follows: \n\n```yaml\n\n# target pipeline or child-pipeline in this case\n\n\nspec:\n  inputs:\n    environment:\n      description: \"Deployment environment\"\n      options: [staging, production]\n    version:\n      type: string\n      description: \"Application version\"\n\n\n---\n\n\nstages:\n  - deploy\n# Jobs that will use the inputs\n\ndeploy:\n  stage: deploy\n  script:\n      -  echo \"Deploying version $[[ inputs.version ]] to $[[ inputs.environment ]]\"\n\n```\n\nNotice from the code snippet that after defining the spec, there is a YAML document separator \"---\"  followed by the actual child pipeline definition where we access the variables `$[[ inputs.version ]]` and `$[[ inputs.environment ]]\"` from the defined inputs using input interpolation.\n\n## Get hands-on with parent-child pipelines, artifacts, and more\n\nWe hope this article has helped with navigating the challenge of sharing variables and artifacts in parent-child pipeline setups.\n\nTo try these examples for yourself, feel free to view or fork the [Premium/Ultimate](https://gitlab.com/gitlab-da/use-cases/devsecops-platform/devops-platform-wave/scenarios/scenario7-deep-dive-into-build-automation-and-ci/7-4-parent-child-pipeline/-/tree/main) and the [GitLab Package Registry](https://gitlab.com/gitlab-da/playground/dhelfand/parent-child-pipeline-with-package-registry-artifacts) examples of sharing artifacts.\n\nYou can also sign up for a [30-day free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to experience all the features GitLab has to offer. 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to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[719],"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/)",[27,724,725],"product","features",{"featured":33,"template":13,"slug":727},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":729,"config":739},{"title":730,"description":731,"authors":732,"heroImage":734,"date":735,"category":10,"tags":736,"body":738},"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.",[733],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[260,609,737],"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":740,"featured":33,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":742,"config":750},{"title":743,"description":744,"authors":745,"heroImage":746,"date":747,"category":10,"tags":748,"body":749},"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.",[733],"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",[609,260,724],"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":751,"featured":12,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":753},[754,768,779],{"id":755,"categories":756,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[757],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[724,37],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":783,"text":759,"button":784,"image":788},"security-modernization",[782],"security","Are you trading speed for security?",{"text":785,"config":786},"Get your security maturity score",{"href":787,"dataGaName":764,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":792,"blurb":793,"button":794,"secondaryButton":799},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":795,"config":796},"Get your free trial",{"href":797,"dataGaName":50,"dataGaLocation":798},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":800},{"href":54,"dataGaName":55,"dataGaLocation":798},1773350832819]