[{"data":1,"prerenderedAt":791},["ShallowReactive",2],{"/en-us/blog/making-ci-easier-with-gitlab":3,"navigation-en-us":38,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-Rob Ribeiro":688,"blog-related-posts-en-us-making-ci-easier-with-gitlab":702,"assessment-promotions-en-us":742,"next-steps-en-us":781},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/making-ci-easier-with-gitlab.yml","Making Ci Easier With Gitlab",[7],"rob-ribeiro",null,"engineering",{"slug":11,"featured":12,"template":13},"making-ci-easier-with-gitlab",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Making CI/CD easier with GitLab","The team at Trek10 tries to consider the need for automation and repeatability with everything they do. One team member gives a crash course in GitLab CI/CD and explains how they use it.",[18],"Rob Ribeiro","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749680423/Blog/Hero%20Images/making-ci-easier-with-gitlab.jpg","2017-07-13","At [Trek10](https://www.trek10.com/), we always try to consider the need for automation and repeatability with everything that we do. That’s why we focus on using tools like CloudFormation, [Serverless](/topics/serverless/), and CI/CD, as well as building other tools. Recently, I was tasked with doing various maintenance tasks on a number of internal tools/projects. Some needed upgrades from Node.js 0.10, some needed code fixes, and most needed CI/CD. Today, we’re just going to focus on the CI/CD part.\n\n\u003C!-- more -->\n\nIn spite of my past experience with Jenkins and TeamCity and our team’s experience with AWS (CodePipeline/CodeDeploy), I chose [GitLab CI/CD](/topics/ci-cd/) to standardize these projects. The biggest reason for this choice is history. As a project evolves, its CI/CD configuration may change. If you ever need to go back in time, you may have difficulty deploying again. Since GitLab CI/CD is based on a `.gitlab-ci.yml` config file that is committed with the code, as long as a commit built and deployed then, it stands a pretty good chance of building and deploying now. Being able to tweak CI/CD without leaving my editor was an additional bonus.\n\n### Crash course in GitLab CI/CD\n\nGitLab CI/CD relies on having a `.gitlab-ci.yml` file in the root of your repo. CI/CD for each commit is run against the `.gitlab-ci.yml` that is current for that commit. The fundamental unit of CI/CD for GitLab is a “job”. A job is a construct that runs a bash script against a commit in a particular context. You might have one job to run tests, other jobs to build for staging or production, and other jobs to deploy to particular environments. In the config file, jobs are represented by top level maps (aka “objects”) that are not otherwise “reserved” GitLab CI/CD maps. Examples of reserved top level maps: `image` (Docker image in which your jobs run), `services` (other Docker images that need to run while your jobs run), `before_script` (runs before every `script`), `after_script` (runs after every `script`), `stages` (redefines the stage names and order), `variables` (variables available to all jobs), and `cache` (controls what is cached between CI/CD runs; good for stuff from your package manager).\n\nEvery job must belong to a stage (if left out, `test` is the default). Stages are run in a sequence, and all of the jobs in a stage run with max parallelism available. The default stage sequence is: `build`, `test`, `deploy`. Each job also has `before_script`, `after_script`, `variables`, and `cache`. Defining these at a job level will override the top-level configuration. The most important of these is `variables`, because your variables are what make the production deploy job’s context different from the staging deploy job’s context. `variables` is just a map with a bunch of key value pairs. Variables are consumed with a syntax similar to bash: `${myVar}`. There are some limitations that you should know:\n\n* Variables do not support bash variable expansions, substitutions, defaults, etc.\n* Variables do not recurse or have a sense of order of evaluation, but top level variables can be used in job level variables. See the following examples:\n\n```text\n# You CANNOT do this (referencing a sibling variable in the same map)\nvariables:\n    PROD_STAGE_NAME: prod\n    PROD_URL: https://thisismywebsite.com/${PROD_STAGE_NAME}\n\n```\n\n```text\n# You CAN do this (referencing a top-level variable from a job's variables map)\nvariables:\n    PROD_STAGE_NAME: prod\n\nmy_job:\n    variables:\n        STAGE_NAME: ${PROD_STAGE_NAME}\n\n```\n\n```text\n# But you CANNOT do something like this (nested variables)\nvariables:\n    CURRENT_STAGE: PROD\n    PROD_STAGE_NAME: prod\n\nmy_job:\n    variables:\n        STAGE_NAME: ${${CURRENT_STAGE}_STAGE_NAME}\n\n```\n\nThat last example gives us a ton of power. We’ll be sure to abuse that as we go.\n\nAs mentioned before, jobs run a bash script in a context. So every job must have a `script`. The last big thing that you need is “flow control”. By default, a job will run on every commit. Using the `only`, `except`, and `when` keys allows you to control how jobs are triggered. `only` and `except` accept the following options:\n\n* Branch names, e.g. `master` or `develop`\n* Tag names\n* JS style RegExp literals to evaluate against branch/tag names\n* These special keywords: `api`, `branches`, `external`, `tags`, `pushes`, `schedules`, `triggers`, and `web`\n* Using `branches` and `tags` with `only` cause a job to be run for every branch or tag, respectively\n* Repo path filters to deal with repo forks\n\nOne more important fact: jobs that start with a period character are disabled, e.g.: `.my_disabled_job`\n\nThat should be enough to get us started. You can find more [GitLab CI/CD documentation here](https://docs.gitlab.com/ee/ci/). The most useful bit is the `.gitlab-ci.yml` reference found [here](https://docs.gitlab.com/ee/ci/yaml/).\n\nAs with any new tool, I got to read and re-read the documentation and make some mistakes getting things right. By the time I was knee-deep in this, I realized there was a need to prevent anyone from having to do this again, myself included. The solution requires two things: a well-designed CI/CD template and a way to get that template into all of your new repositories. Let’s tackle template design next.\n\n### Designing a template\n\nThis part is hard to talk about in a completely generic manner. Instead, let’s walk through our use case. Looking at our projects past and present, I could usually bet on these characteristics:\n\n* Deploys to AWS (we are an AWS consultancy after all…)\n* Uses Serverless framework with Node.js or Python\n* May deploy production to multiple regions\n* May deploy different stages to different accounts\n\nIn addition, I realized that I needed these other options:\n\n* May need to “disable” dev/staging from doing real work\n* May want one dev environment per branch\n\nFinally, we decided on the following deployment strategy:\n\n* Production deploys via tags on `master`\n* Staging deploys on commits/merges to `master`\n* Dev deploys should work for all other branches (we’re not going to implement this one in this post)\n\nMy roots are as a software developer, so making things reusable is a core skill at this point. A good template is going to make it super easy for the intended cases and be fairly adaptable for other uses. Here is the goal:\n\n* One script per stage. That means only one test script, one build script, and one deploy script. Oh, and keep it DRY.\n* Jobs should be as similar as possible, and differences should be tweaked by top level variables.\n\nLet’s focus on that single script per stage. We’re not going to cover how to write the deployment script, but we’ll focus on the deploy stage. But let’s say we start with a deployment job like this:\n\n```text\ndeploy:production:\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME} ${REGION}\n    variables:\n        DEPLOYMENT_ROLE: arn:aws:iam::1234567890:role/gitlab-ci-deployment\n        STAGE_NAME: prod\n        REGION: us-east-1\n        ACCOUNT: \"1234567890\"\n    only:\n        - tags\n\n```\n\n\nNow we could copy and tweak this for staging and dev, but that’s not what we’re after. First, let’s break the script off to a reusable chunk and use it in our staging deploy:\n\n```text\n.deployment_script: &deployment_script\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME} ${REGION}\n\ndeploy:production:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: arn:aws:iam::1234567890:role/gitlab-ci-deployment\n        STAGE_NAME: prod\n        REGION: us-east-1\n        ACCOUNT: \"1234567890\"\n        PRODUCTION: \"true\"\n    only:\n        - tags\n\ndeploy:staging:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: arn:aws:iam::0987654321:role/gitlab-ci-deployment\n        STAGE_NAME: staging\n        REGION: us-east-1\n        ACCOUNT: \"0987654321\"\n    only:\n        - master\n\n```\n\nUsing YAML anchors and references, we can inject the script into all of our deployment jobs. Notice that the deployment script is disabled. This is because we don’t want it to run in parallel with all of our intended jobs. We also added a `PRODUCTION` environment variable to just the production deploy to allow our script to pick that up too. If your code knows about this, you can use this to turn on/off production-only features. Now, we can make this cleaner and easier for our developers by pulling all of the `variables` to a top-level variables map at the top of the file:\n\n```text\nvariables:\n    PROD_ACCOUNT: \"1234567890\"\n    PROD_STAGE_NAME: prod\n    PROD_REGION: us-east-1\n    STAGING_ACCOUNT: \"0987654321\"\n    STAGING_STAGE_NAME: staging\n    STAGING_REGION: us-east-1\n\n.deployment_script: &deployment_script\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME}, ${REGION}, and ${ACCOUNT}\n\ndeploy:production:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: \"arn:aws:iam::${PROD_ACCOUNT}:role/gitlab-ci-deployment\"\n        STAGE_NAME: ${PROD_STAGE_NAME}\n        REGION: ${PROD_REGION}\n        ACCOUNT: ${PROD_ACCOUNT}\n        PRODUCTION: \"true\"        \n    only:\n        - tags\n\ndeploy:staging:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: \"arn:aws:iam::${STAGING_ACCOUNT}:role/gitlab-ci-deployment\"\n        STAGE_NAME: ${STAGING_STAGE_NAME}\n        REGION: ${STAGING_REGION}\n        ACCOUNT: ${STAGING_ACCOUNT}\n    only:\n        - master\n\n```\n\n\nNow, that’s looking more reusable, and we have accomplished our second goal of making the jobs very similar and controlled by top-level variables. This makes it easy for anyone who fits the template’s use case perfectly to reuse it. We could easily add the dev environment, but we’ll skip that in favor of illustrating multi-region production deploys:\n\n```text\nvariables:\n    PROD_ACCOUNT: \"1234567890\"\n    PROD_STAGE_NAME: prod\n    PROD1_REGION: us-east-1\n    PROD2_REGION: us-west-2\n    STAGING_ACCOUNT: \"0987654321\"\n    STAGING_STAGE_NAME: staging\n    STAGING_REGION: us-east-1\n\n.deployment_script: &deployment_script\n    stage: deploy\n    script: |\n        # assume ${DEPLOYMENT_ROLE} in AWS\n        # install dependencies\n        # run serverless deployment with ${STAGE_NAME}, ${REGION}, and ${ACCOUNT}\n\n.production_variables\n    DEPLOYMENT_ROLE: \"arn:aws:iam::${PROD_ACCOUNT}:role/gitlab-ci-deployment\"\n    STAGE_NAME: ${PROD_STAGE_NAME}\n    ACCOUNT: ${PROD_ACCOUNT}\n    PRODUCTION: \"true\"    \n\ndeploy:production_1: &deploy_production\n    \u003C\u003C: *deployment_script\n    variables:\n        \u003C\u003C: *production_variables\n        REGION: ${PROD1_REGION}\n    only:\n        - tags\n\ndeploy:production_2:\n    \u003C\u003C: *deploy_production\n    variables:\n        \u003C\u003C: *production_variables\n        REGION: ${PROD2_REGION}        \n\ndeploy:staging:\n    \u003C\u003C: *deployment_script\n    variables:\n        DEPLOYMENT_ROLE: \"arn:aws:iam::${STAGING_ACCOUNT}:role/gitlab-ci-deployment\"\n        STAGE_NAME: ${STAGING_STAGE_NAME}\n        REGION: ${STAGING_REGION}\n        ACCOUNT: ${STAGING_ACCOUNT}\n    only:\n        - master\n\n```\n\nNotice that we have changed the job names to reflect having multiple regions. In addition, we are making use of YAML anchors and references to copy the entire `deploy:production_1` job into `deploy:production_2` and then we just override the `REGION` variable. This makes adding additional regions super easy.\n\nWhat’s more useful at this point is that, as long as you have made your script flexible enough, you can now distribute this to your development team as a template. If their project fits the script and configuration perfectly, they should just have to fill in the correct values for the top-level variables and go. For those needing something different, they should hopefully be able to just tweak the script. Now, we just need to solve the problem of making sure that they actually use the template…\n\n### Automatic CI/CD injection with GitLab and AWS Lambda\n\nI was inspired by GitHub’s option to select a .gitignore and license during the repo creation process. What if we could have that for CI? Forking GitLab and figuring out how to hack this in did not sound like a quick or easy thing to do. However, after a little research, I found that we could use a system hook to trigger a Lambda that could inject the desired template via the commit API. This part is not as interesting to read about, so we did one better: we have open sourced this tool so you can deploy it in your environment. Check out the repo [here](https://github.com/trek10inc/gitlab-boilerplate-injector). And if you’re looking for someone to help you implement these and other awesome automations and AWS solutions, we would love to talk to you. Feel free to reach out to us at info@trek10.com for more. Thanks for reading!\n\n## About the Guest Author\n\nRob has spent his career honing his interpersonal, technical, and problem solving skills. He spent five years in customer service and management, followed by over five years in software development and consulting. He has experience working and consulting for everything from startups to Fortune 500 enterprises in a variety of industries including manufacturing, healthcare, and finance. Rob has earned a MS in Applied Mathematics and Computer Science from Indiana University and a BS in Pharmaceutical Sciences from Purdue University.\n",[23,24],"CI/CD","user stories","yml",{},true,"/en-us/blog/making-ci-easier-with-gitlab",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":30,"ogSiteName":31,"ogType":32,"canonicalUrls":30},"https://about.gitlab.com/blog/making-ci-easier-with-gitlab","https://about.gitlab.com","article","en-us/blog/making-ci-easier-with-gitlab",[35,36],"cicd","user-stories","BWL2KZ5aG0IDSPIw_qhqpYmQb-ePEt1JbgtwtrluKuw",{"data":39},{"logo":40,"freeTrial":45,"sales":50,"login":55,"items":60,"search":367,"minimal":398,"duo":417,"pricingDeployment":427},{"config":41},{"href":42,"dataGaName":43,"dataGaLocation":44},"/","gitlab logo","header",{"text":46,"config":47},"Get free 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statement",{"items":678},[679,682,685],{"text":680,"config":681},"Terms",{"href":507,"dataGaName":508,"dataGaLocation":455},{"text":683,"config":684},"Cookies",{"dataGaName":517,"dataGaLocation":455,"id":518,"isOneTrustButton":27},{"text":686,"config":687},"Privacy",{"href":512,"dataGaName":513,"dataGaLocation":455},[689],{"id":690,"title":18,"body":8,"config":691,"content":693,"description":8,"extension":25,"meta":697,"navigation":27,"path":698,"seo":699,"stem":700,"__hash__":701},"blogAuthors/en-us/blog/authors/rob-ribeiro.yml",{"template":692},"BlogAuthor",{"name":18,"config":694},{"headshot":695,"ctfId":696},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","Rob-Ribeiro",{},"/en-us/blog/authors/rob-ribeiro",{},"en-us/blog/authors/rob-ribeiro","rjQ2ysDJP8c1YT6lp5Q92MWb_KgUcH1rL107w_Ow3Iw",[703,718,731],{"content":704,"config":716},{"title":705,"description":706,"authors":707,"heroImage":709,"date":710,"body":711,"category":9,"tags":712},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[708],"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/)",[713,714,715],"tutorial","product","features",{"featured":12,"template":13,"slug":717},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":719,"config":729},{"title":720,"description":721,"authors":722,"heroImage":724,"date":725,"category":9,"tags":726,"body":728},"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.",[723],"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",[259,610,727],"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":730,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":732,"config":740},{"title":733,"description":734,"authors":735,"heroImage":736,"date":737,"category":9,"tags":738,"body":739},"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.",[723],"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",[610,259,714],"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":741,"featured":27,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":743},[744,758,769],{"id":745,"categories":746,"header":748,"text":749,"button":750,"image":755},"ai-modernization",[747],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":751,"config":752},"Get your AI maturity score",{"href":753,"dataGaName":754,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":756},{"src":757},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":759,"categories":760,"header":761,"text":749,"button":762,"image":766},"devops-modernization",[714,556],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":770,"categories":771,"header":773,"text":749,"button":774,"image":778},"security-modernization",[772],"security","Are you trading speed for security?",{"text":775,"config":776},"Get your security maturity score",{"href":777,"dataGaName":754,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":782,"blurb":783,"button":784,"secondaryButton":789},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":785,"config":786},"Get your free trial",{"href":787,"dataGaName":49,"dataGaLocation":788},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":493,"config":790},{"href":53,"dataGaName":54,"dataGaLocation":788},1773350815603]