[{"data":1,"prerenderedAt":794},["ShallowReactive",2],{"/en-us/blog/multi-account-aws-sam-deployments-with-gitlab-ci":3,"navigation-en-us":43,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Forrest Brazeal":692,"blog-related-posts-en-us-multi-account-aws-sam-deployments-with-gitlab-ci":706,"assessment-promotions-en-us":745,"next-steps-en-us":784},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":29,"isFeatured":12,"meta":30,"navigation":31,"path":32,"publishedDate":20,"seo":33,"stem":37,"tagSlugs":38,"__hash__":42},"blogPosts/en-us/blog/multi-account-aws-sam-deployments-with-gitlab-ci.yml","Multi Account Aws Sam Deployments With Gitlab Ci",[7],"forrest-brazeal",null,"engineering",{"slug":11,"featured":12,"template":13},"multi-account-aws-sam-deployments-with-gitlab-ci",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to set up multi-account AWS SAM deployments with GitLab CI/CD","Our guest author, an AWS Serverless hero, shares how to automate SAM deployments using GitLab CI/CD.",[18],"Forrest Brazeal","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749666959/Blog/Hero%20Images/gitlab-aws-cover.png","2019-02-04","I've been working with [serverless](/topics/serverless/) applications in AWS for about three years – that makes me an old salt in serverless terms! So I know that deploying and maintaining a serverless app can be tricky; the tooling often has critical gaps.\n\nAWS's [SAM (Serverless Application Model)](https://aws.amazon.com/serverless/sam/) is an open source framework that makes it easier to define AWS resources – such as Lambda functions, API Gateway APIs and DynamoDB tables – commonly used in serverless applications. Once you lay out your app in a SAM template, the next thing you need is a consistent, repeatable way to get that template off your laptop and deployed in the cloud.\n\nYou need CI/CD.\n\nI've used several different [CI/CD systems](/topics/ci-cd/) to automate SAM deployments, and I always look for the following features:\n\n- A single deployment pipeline that can build once and securely deploy to multiple AWS accounts (dev, staging, prod).\n- Dynamic feature branch deployments, so serverless devs can collaborate in the cloud without stepping on each other.\n- Automated cleanup of feature deployments.\n- Review of our SAM application directly integrated with the CI/CD tool's user interface.\n- Manual confirmation before code is released into production.\n\nIn this post, we'll find out how [GitLab CI](/solutions/continuous-integration/) can check these boxes on its way to delivering effective CI/CD for AWS SAM. You can follow along using [the official example code, available here](https://gitlab.com/gitlab-examples/aws-sam).\n\n## Multi-account AWS deployments\n\nWe'll want to set up our deployment pipeline across multiple AWS accounts, because accounts are the only true security boundary in AWS. We don't want to run any risk of deploying prod data in dev, or vice versa. Our multi-account setup will look something like this:\n\nAny time we work with multiple AWS accounts, we need cross-account [IAM roles](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html) in order to authorize deployments. We'll handle this task through the following steps. (All referenced scripts are available in the [example repo](https://gitlab.com/gitlab-examples/aws-sam))\n\n### 1\\. Establish three AWS accounts for development, staging, and production deployments\n\nYou can use existing AWS accounts if you have them, or [provision new ones under an AWS Organization](https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_accounts_create.html).\n\n### 2\\. Set up GitLab IAM roles in each account\n\nRun the following AWS CLI call with admin credentials in each of the three accounts:\n\n```shell\naws cloudformation deploy --stack-name GitLabCIRoles --template-file setup-templates/roles.yml --capabilities CAPABILITY_NAMED_IAM --parameter-overrides CIAccountID=\"\u003CAWS Account ID where your GitLab CI/CD runner lives>\" CIAccountSTSCondition=\"\u003CThe aws:userid for the IAM principal used by the Gitlab runner>\"\n  ```\n\nReplace `CIAccountID` and `CIAccountSTSCondition` as indicated with values from the AWS account where your GitLab CI/CD runner exists. (Need help finding the `aws:userid` for your runner’s IAM principal? Check out [this guide](https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_variables.html#principaltable).)\n\nThis CloudFormation template defines two roles: `SharedServiceRole` and `SharedDeploymentRole`. The `SharedServiceRole` is assumed by the GitLab CI/CD runner when calling the AWS CloudFormation service. This role trusts the GitLab CI/CD runner's role. It has permissions to call the CloudFormation service, pass a role via IAM, and access S3 and CloudFront: nothing else. This role is not privileged enough to do arbitrary AWS deployments on its own.\n\nThe `SharedDeploymentRole`, on the other hand, has full administrative access to perform any AWS action. A such, it cannot be assumed directly by the GitLab CI/CD runner. Instead, this role must be \"passed\" to CloudFormation using the service's `RoleArn` parameter. The CloudFormation service trusts the `SharedDeploymentRole` and can use it to deploy whatever resources are needed as part of the pipeline.\n\n### 3\\. Create an S3 bucket for CI artifacts\n\nGrab the AWS account ID for each of your development, staging, and production accounts, then deploy this CloudFormation template **in the account where your GitLab CI/CD Runner exists**:\n\n`aws cloudformation deploy --stack-name GitLabCIBucket --template-file setup-templates/ci-bucket.yml --parameter-overrides DevAwsAccountId=\"\u003CAWS Account ID for dev>\" StagingAwsAccountId=\"\u003CAWS Account ID for staging>\" ProdAwsAccountId=\"\u003CAWS Account ID for prod>\" ArtifactBucketName=\"\u003CA unique name for your bucket>\"`\n\nThis CloudFormation template creates a centralized S3 bucket which holds the artifacts created during your pipeline run. Artifacts are created once for each branch push and reused between staging and production. The bucket policy allows the development, test, and production accounts to reference the same artifacts when deploying CloudFormation stacks -- checking off our \"build once, deploy many\" requirement.\n\n### 4\\. Assume the `SharedServiceRole` before making any cross-account AWS calls\nWe have provided the script `assume-role.sh`, which will assume the provided role and export temporary AWS credentials to the current shell. It is sourced in the various `.gitlab-ci.yml` build scripts.\n\n## Single deployment pipeline\n\nThat brings us to the `.gitlab-ci.yml` file you can see at the root of our example repository. GitLab CI/CD is smart enough to dynamically create and execute the pipeline based on that template when we push code to GitLab. The file has a number of variables at the top that you can tweak based on your environment specifics.\n\n### Stages\n\nOur Gitlab CI/CD pipeline contains seven possible stages, defined as follows:\n\n![Multi-account AWS SAM deployment model with GitLab CI](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/deployment-model.png){: .shadow.medium.center}\n\n```yaml\nstages:\n - test\n - build-dev\n - deploy-dev\n - build-staging\n - deploy-staging\n - create-change-prod\n - execute-change-prod\n\n```\n\n![Deployment lifecycle stages](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/deployment-lifecycle-stages.png){: .shadow.medium.center}\n\n\"Stages\" are used as a control flow mechanism when building the pipeline. Multiple build jobs within a stage will run in parallel, but all jobs in a given stage must complete before any jobs belonging to the next stage in the list can be executed.\n\nAlthough seven stages are defined here, only certain ones will execute, depending on what kind of Git action triggered our pipeline. We effectively have three stages to any deployment: a \"test\" phase where we run unit tests and dependency scans against our code, a \"build\" phase that packages our SAM template, and a \"deploy\" phase split into two parts: creating a [CloudFormation change set](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-updating-stacks-changesets.html) and then executing that change set in the target environment.\n\n#### Test\n\nOur `.gitlab-ci.yml` file currently runs two types of tests: unit tests against our code, and dependency scans against our third-party Python packages.\n\n##### Unit tests\n\nUnit tests run on every branch pushed to the remote repository. This behavior is defined by the `only: branches` property in the job shown below:\n\n```yaml\ntest:unit:\n stage: test\n only:\n   - branches\n script: |\n   if test -f requirements.txt; then\n       pip install -r requirements.txt\n   fi\n   python -m pytest --ignore=functions/\n\n```\n\nEvery GitLab CI/CD job runs a script. Here, we install any dependencies, then execute Python unit tests.\n\n##### Dependency scans\n\n[Dependency scans](https://docs.gitlab.com/ee/user/application_security/dependency_scanning/), which can take a few minutes, run only on code pushed to the master branch; it would be counterproductive for developers to wait on them every time they want to test code.\n\nThese scans use a hardcoded, standard Docker image to mount the code and run \"Docker in Docker\" checks against a database of known package vulnerabilities. If a vulnerability is found, the pipeline will log the error without stopping the build (that's what the `allow-failure: true` property does).\n\n#### Build\n\nThe build stage turns our SAM template into CloudFormation and turns our Python code into a valid AWS Lambda deployment package. For example, here's the `build:dev` job:\n\n```yaml\nbuild:dev:\n stage: build-dev\n \u003C\u003C: *build_script\n variables:\n   \u003C\u003C: *dev_variables\n artifacts:\n   paths:\n     - deployment.yml\n   expire_in: 1 week\n only:\n   - branches\n except:\n   - master\n\n```\n\nWhat's going on here? Note first the combination of `only` and `except` properties to ensure that our development builds happen only on pushes to branches that aren't `master`. We're referring to `dev_variables`, the set of development-specific variables defined at the top of `.gitlab-ci.yml`. And we're running a script, pointed to by `build_script`, which packages our SAM template and code for deployment using the `aws cloudformation package` CLI call.\n\nThe artifact `deployment.yml` is the CloudFormation template output by our package command. It has all the implicit SAM magic expanded into CloudFormation resources. By managing it as an artifact, we can pass it along to further steps in the build pipeline, even though it isn't committed to our repository.\n\n#### Deploy\nOur deployments use AWS CloudFormation to deploy the packaged application in a target AWS environment.\n\nIn development and staging environments, we use the `aws cloudformation deploy` command to create a change set and immediately execute it. In production, we put a manual \"wait\" in the pipeline at this point so you have the opportunity to review the change set before moving onto the \"Execute\" step, which actually calls `aws cloudformation execute-changeset` to update the underlying stack.\n\nOur deployment jobs use a helper script, committed to the top level of the example repository, called `cfn-wait.sh`. This script is needed because the `aws cloudformation` commands don't wait for results; they report success as soon as the stack operation starts. To properly record the deployment results in our job, we need a script that polls the CloudFormation service and throws an error if the deployment or update fails.\n\n## Dynamic feature branch deployments and Review Apps\n\n![Dynamic feature branch deployments and Review Apps](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/dynamic-feature-branch-deployments.png){: .shadow.medium.center}\n\nWhen a non-master branch is pushed to GitLab, our pipeline runs tests, builds the [updated source code](/solutions/source-code-management/), and deploys and/or updates the changed CloudFormation resources in the development AWS account. When the branch is merged into master, or if someone clicks the \"Stop\" button next to the branch's environment in GitLab CI, the CloudFormation stack will be torn down automatically.\n\nIt is perfectly possible, and indeed desirable, to have multiple development feature branches simultaneously deployed as live environments for more efficient parallel feature development and QA. The serverless model makes this a cost-effective strategy for collaborating in the cloud.\n\nIf we are dynamically deploying our application on every branch push, we might like to view it as part of our interaction with the GitLab console (such as during a code review). GitLab supports this with a nifty feature called [Review Apps](https://docs.gitlab.com/ee/ci/review_apps/). Review Apps allow you to specify an \"environment\" as part of a deployment job, as seen in our `deploy:dev` job below:\n\n```yaml\ndeploy:dev:\n \u003C\u003C: *deploy_script\n stage: deploy-dev\n dependencies:\n   - build:dev\n variables:\n   \u003C\u003C: *dev_variables\n environment:\n   name: review/$CI_COMMIT_REF_NAME\n   url: https://${CI_COMMIT_REF_NAME}.${DEV_HOSTED_ZONE_NAME}/services\n   on_stop: stop:dev\n only:\n   - branches\n except:\n   - master\n\n```\n\nThe link specified in the `url` field of the `environment` property will be accessible in the `Environments` section of GitLab CI/CD or on any merge request of the associated branch. (In the case of the sample SAM application provided with our example, since we don't have a front end to view, the link just takes you to a GET request for the `/services` API endpoint and should display some raw JSON in your browser.)\n\n![Link to live environment](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/link-live-environment.png){: .shadow.medium.center}\n\nThe `on_stop` property specifies what happens when you \"shut down\" the environment in GitLab CI. This can be done manually or by deleting the associated branch. In the case above, we have stopped behavior for dev environments linked to a separate job called `stop:dev`:\n\n```yaml\nstop:dev:\n stage: deploy-dev\n variables:\n   GIT_STRATEGY: none\n   \u003C\u003C: *dev_variables\n \u003C\u003C: *shutdown_script\n when: manual\n environment:\n   name: review/$CI_COMMIT_REF_NAME\n   action: stop\n only:\n   - branches\n except:\n   - master\n\n```\n\nThis job launches the `shutdown_script` script, which calls `aws cloudformation teardown` to clean up the SAM deployment.\n\nFor safety's sake, there is no automated teardown of staging or production environments.\n\n## Production releases\n\n![Production releases](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/production-releases.png){: .shadow.medium.center}\n\nWhen a change is merged into the master branch, the code is built, tested (including dependency scans) and deployed to the staging environment. This is a separate, stable environment that developers, QA, and others can use to verify changes before attempting to deploy in production.\n\n![Staging environment](https://about.gitlab.com/images/blogimages/multi-account-aws-sam/staging-environment.png){: .shadow.medium.center}\n\nAfter deploying code to the staging environment, the pipeline will create a change set for the production stack, and then pause for a manual intervention. A human user must click a button in the Gitlab CI/CD \"Environments\" view to execute the final change set.\n\n## Now what?\n\nStep back and take a deep breath – that was a lot of information! Let's not lose sight of what we've done here: we've defined a secure, multi-account AWS deployment pipeline in our GitLab repo, integrated tests, builds and deployments, and successfully rolled a SAM-defined serverless app to the cloud. Not bad for a few lines of config!\n\nThe next step is to try this on your own. If you'd like to start with our sample \"AWS News\" application, you can simply run `sam init --location git+https://gitlab.com/gitlab-examples/aws-sam` to download the project on your local machine. The AWS News app contains a stripped-down, single-account version of the `gitlab-ci.yml` file discussed in this post, so you can try out deployments with minimal setup needed.\n\n## Further reading\n\nWe have barely scratched the surface of GitLab CI/CD and AWS SAM in this post. Here are some interesting readings if you would like to take your work to the next level:\n\n### SAM\n\n- [Implementing safe AWS Lambda deployments with AWS SAM and CodeDeploy](https://aws.amazon.com/blogs/compute/implementing-safe-aws-lambda-deployments-with-aws-codedeploy/)\n- [Running and debugging serverless applications locally using the AWS SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-test-and-debug.html)\n\n### GitLab CI\n\n- [Setting up a GitLab Runner on EC2](https://hackernoon.com/configuring-gitlab-ci-on-aws-ec2-using-docker-7c359d513a46)\n- [Scheduled pipelines](https://docs.gitlab.com/ee/ci/pipelines/schedules.html)\n- [ChatOps](https://docs.gitlab.com/ee/ci/chatops/)\n\nPlease [let me know](https://twitter.com/forrestbrazeal) if you have further questions!\n\n### About the guest author\n\nForrest Brazeal is an [AWS Serverless Hero](https://aws.amazon.com/developer/community/heroes/forrest-brazeal/). He currently works as a senior cloud architect at [Trek10](https://trek10.com), an AWS Advanced Consulting Partner. 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statement",{"items":682},[683,686,689],{"text":684,"config":685},"Terms",{"href":511,"dataGaName":512,"dataGaLocation":459},{"text":687,"config":688},"Cookies",{"dataGaName":521,"dataGaLocation":459,"id":522,"isOneTrustButton":31},{"text":690,"config":691},"Privacy",{"href":516,"dataGaName":517,"dataGaLocation":459},[693],{"id":694,"title":18,"body":8,"config":695,"content":697,"description":8,"extension":29,"meta":701,"navigation":31,"path":702,"seo":703,"stem":704,"__hash__":705},"blogAuthors/en-us/blog/authors/forrest-brazeal.yml",{"template":696},"BlogAuthor",{"name":18,"config":698},{"headshot":699,"ctfId":700},"","fbrazeal",{},"/en-us/blog/authors/forrest-brazeal",{},"en-us/blog/authors/forrest-brazeal","-LJoNl2kFQ2-t5P9UDj-5kdXlZaHlvc9b_rG5JBTI2w",[707,722,734],{"content":708,"config":720},{"title":709,"description":710,"authors":711,"heroImage":713,"date":714,"body":715,"category":9,"tags":716},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[712],"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/)",[717,718,719],"tutorial","product","features",{"featured":12,"template":13,"slug":721},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":723,"config":732},{"title":724,"description":725,"authors":726,"heroImage":728,"date":729,"category":9,"tags":730,"body":731},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[727],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[263,614,26],"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":733,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":735,"config":743},{"title":736,"description":737,"authors":738,"heroImage":739,"date":740,"category":9,"tags":741,"body":742},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[727],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[614,263,718],"Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":744,"featured":31,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":746},[747,761,772],{"id":748,"categories":749,"header":751,"text":752,"button":753,"image":758},"ai-modernization",[750],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":754,"config":755},"Get your AI maturity score",{"href":756,"dataGaName":757,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":759},{"src":760},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":762,"categories":763,"header":764,"text":752,"button":765,"image":769},"devops-modernization",[718,560],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":776,"text":752,"button":777,"image":781},"security-modernization",[775],"security","Are you trading speed for security?",{"text":778,"config":779},"Get your security maturity score",{"href":780,"dataGaName":757,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":785,"blurb":786,"button":787,"secondaryButton":792},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":788,"config":789},"Get your free trial",{"href":790,"dataGaName":54,"dataGaLocation":791},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":793},{"href":58,"dataGaName":59,"dataGaLocation":791},1773350822988]