[{"data":1,"prerenderedAt":794},["ShallowReactive",2],{"/en-us/blog/deploy-aws":3,"navigation-en-us":42,"banner-en-us":442,"footer-en-us":452,"blog-post-authors-en-us-Orit Golowinski":691,"blog-related-posts-en-us-deploy-aws":705,"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":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":41},"blogPosts/en-us/blog/deploy-aws.yml","Deploy Aws",[7],"orit-golowinski",null,"engineering",{"slug":11,"featured":12,"template":13},"deploy-aws",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How to deploy to AWS with GitLab","We believe deploying to the cloud should be easy and boring. The deployment process is the same regardless of what tech stack you're using so why not automate it?",[18],"Orit Golowinski","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672124/Blog/Hero%20Images/aws_rocket.jpg","2020-12-15","Cloud computing services are replacing traditional hardware technologies at an extremely fast pace. The majority of businesses worldwide are already moving their applications to the cloud — both public and private cloud — or plan to in the near future. Over a short period of time, this technology took over the market as businesses preferred remote access to data as well as the cloud's scalability, economy, and reach.\n\n## AWS Deployment: deploying applications to the cloud\n\nCOVID-19 and the resulting trend toward remote work forced organizations to adopt cloud technologies even if they hadn’t planned to originally. Software deployment to the cloud has also increased. Cloud is no longer just virtual machines, organizations are driving the use of [Containers as a Service (CaaS)](https://searchitoperations.techtarget.com/definition/Containers-as-a-Service-CaaS)\ndue to their growing interest in leveraging containers to ease development and testing, speed up deployment, scale operations, and increase the efficiency of workloads running in the cloud.\n\nSince deployment to the cloud has become a standard practice, at GitLab we want to make this repeatable and [boring](https://handbook.gitlab.com/handbook/values/#boring-solutions). In this blog post, we explain how we've made it easier to deploy to Amazon Web Services (AWS) as part of your deployment process. We invite users to replicate this example to deploy to other cloud providers in a similar way.\n\nSince we want cloud deployment to be as flexible as possible (similar to a microservices architecture), we constructed atomic Docker images that function as building blocks. Users can use these images as part of their custom `gitlab-ci.yml` file or use our predefined `.gitlab-ci.yml` templates. We also added the ability to use [Auto\nDevOps](https://docs.gitlab.com/ee/topics/autodevops/) with the new AWS deployment targets.\n\n## AWS Deployment: how to use GitLab's official AWS Docker Images\n\n### AWS CLI Docker image\n\nIn [GitLab 12.6](/releases/2019/12/22/gitlab-12-6-released/), we provided an official GitLab [AWS cloud-deploy](https://gitlab.com/gitlab-org/cloud-deploy/-/blob/master/aws/cloud_deploy/Dockerfile)\nDocker image that downloads and installs the [AWS\nCLI](https://aws.amazon.com/cli/). This allows users to run `aws` commands directly from their pipelines. For more information, see [Run AWS commands from GitLab\nCI/CD](https://docs.gitlab.com/ee/ci/cloud_deployment/#run-aws-commands-from-gitlab-cicd).\n\n### CloudFormation stack creation Docker image\n\nIn [GitLab 13.5](/releases/2020/10/22/gitlab-13-5-released/), we provided a\nDocker image that runs a script that [creates a stack with\nCloudFormation](https://gitlab.com/gitlab-org/cloud-deploy/-/blob/master/aws/src/bin/gl-cloudformation).\nThe `gl-cloudprovision create-stack` uses [aws cloudformation create-stack](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/template-anatomy.html)\nbehind the scenes. A JSON file based on the CloudFormation template must be passed to that command. For an example of this type of JSON file, see [`cf_create_stack.json`](https://gitlab.com/ebaque/jekyll-demo/-/blob/deploy-to-ec2/aws/cf_create_stack.json).\nWith this type of JSON file, the command creates the infrastructure on AWS, including an EC2 instance directly from the `.gitlab-ci.yml` file. The script exists once we get confirmation that the stack setup is complete or has failed (through periodic polling).\n\n### Push to S3 and Deploy to EC2 Docker image\n\nIn [GitLab 13.5](/releases/2020/10/22/gitlab-13-5-released/) we also provided a Docker image with [Push to S3 and Deploy to EC2 scripts](https://gitlab.com/gitlab-org/cloud-deploy/-/blob/master/aws/src/bin/gl-ec2).\nThe `gl-ec2 push-to-s3` script pushes source code to an S3 bucket. For an example of the JSON file to pass to the `aws deploy push` command, see [`s3_push.json`](https://gitlab.com/ebaque/jekyll-demo/-/blob/deploy-to-ec2/aws/s3_push.json).\nThis code can be whatever artifact is built from a preceding build job. The `gl-ec2 deploy-to-ec2` script uses `aws deploy create-deployment` behind the scenes to create a deployment to an EC2 instance directly from the `.gitlab-ci.yml` file. For an example of the JSON template to pass, see [`create_deployment.json`](https://gitlab.com/ebaque/jekyll-demo/-/blob/deploy-to-ec2/aws/create_deployment.json).\nThe script ends once we get confirmation that the deployment has succeeded or failed (via polling).\n\n## AWS Deployment: using GitLab CI templates to deploy to AWS\n\n### How to deploy to Elastic Container Service (ECS) with GitLab\n\nIn [GitLab 12.9](/releases/2020/03/22/gitlab-12-9-released/), we created a full `.gitlab-ci.yml` template called [`Deploy-ECS.giltab-ci.yml`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/Jobs/Deploy/ECS.gitlab-ci.yml)\nthat deploys to Amazon ECS and extends support for Fargate. Users can include the template in their configuration, specify a few variables, and their application will be deployed and ready to go in no time. This template can be customized for your specific needs. For example: Replacing the selected container registry, changing the path of the file location, etc.\n\n### How to deploy to Elastic Cloud Compute (EC2) with GitLab\n\nIn [GitLab 13.5](/releases/2020/10/22/gitlab-13-5-released/), we created a full `.gitlab-ci.yml` template called [`CF-Provision-and-Deploy-EC2.gitlab-ci.yml`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/Jobs/Deploy/EC2.gitlab-ci.yml)\nthat provisions the infrastructure by leveraging [AWS\nCloudFormation](https://aws.amazon.com/cloudformation/). It then pushes your previously-built artifact to an [AWS S3 bucket](https://aws.amazon.com/s3/)\nand deploys the pushed content to [AWS EC2](https://aws.amazon.com/ec2/).\n\n## AWS Deployment: security  considerations\n\n### Predefined AWS CI/CD variables\n\nIn order to deploy to AWS, you must use AWS security keys to connect to to your AWS instance. Users can define this security keys as [CI/CD environment](/topics/ci-cd/) variables that can be used by the deployment pipeline.\n\nIn [GitLab 12.9](/releases/2020/03/22/gitlab-12-9-released/), we added support for predefined AWS variables. This support function helps users know which variables are required for deploying to AWS and also prevents typos and spelling mistakes.\n\n| Env. variable name | Value|\n| --- | --- |\n| `AWS_ACCESS_KEY_ID` | Your Access key ID |\n| `AWS_SECRET_ACCESS_KEY` | Your Secret access key |\n| `AWS_DEFAULT_REGION` | Your region code |\n\n### \"Just-in-time\" guidance for AWS deployments\n\n[GitLab 13.1](/releases/2020/06/22/gitlab-13-1-released/) provides just-in-time guidance for users who wish to deploy to AWS. Setting up AWS deployments isn't always as easy as we'd like it to be, so we've added in-product links to our AWS templates and documentation when you start adding AWS CI/CD variables to make it easier for you to use our AWS features. This will help you get up and running faster.\n\n![In-product guidance for\nAWS](https://about.gitlab.com/images/blogimages/aws_guide.png)\n\nAWS guide from CI/CD variables\n\n### Added security for the GitLab's official AWS Docker images\n\nIn [GitLab 13.5](/releases/2020/10/22/gitlab-13-5-released/), we changed the image identifier from the release version number to the Docker image digest.\nDocker supports immutable image identifiers and we adopted this best practice to update our cloud-deploy images. When a new image is tagged, we also programmatically retrieve the image digest upon its build and create a release note to effectively communicate this digest to users. This guarantees that every instance of the service runs exactly the same code.\nYou can roll back to an earlier version of the image, even if that version wasn't tagged (or is no longer tagged). This can even prevent race conditions if a new image is pushed while a deploy is in progress.\n\n![Docker Image\nDigest](https://about.gitlab.com/images/blogimages/digest1.png)\n\nDocker image digest or release tag\n\n## AWS Deployment: auto DevOps support\n\nGitLab already supports Kubernetes users deploying to AWS EKS cluster. Click the link to read instructions about [how to deploy an application to a\nGitLab-managed Amazon EKS cluster with Auto\nDevOps](/blog/deploying-application-eks/#:~:text=The%20Auto%20DevOps%20function%20at,build%2C%20and%20deploy%20your%20application).\n\nWe also expanded Auto DevOps to support non-Kubernetes users. Users can specify their deployment target by adding the `AUTO_DEVOPS_PLATFORM_TARGET` variable under the CI/CD variables settings. Specifying the deployment target platform builds a full CI/CD pipeline that deploys to AWS targets.\n\nWe currently support:\n\n- `AUTO_DEVOPS_PLATFORM_TARGET: ECS` (added in GitLab 13.0)\n\n- `AUTO_DEVOPS_PLATFORM_TARGET: FARGATE` (added in GitLab 13.2)\n\n- `AUTO_DEVOPS_PLATFORM_TARGET: EC2` (added in GitLab 13.6)\n\nFor more information about Auto DevOps for AWS targets, see [requirements for Auto\nDevOps](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)\ndocumentation.\n\nHere's a quick recording for how to use Auto Deploy to Amazon ECS:\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/HzRhLLFlAos\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\nSpeed run on how to use auto deploy to EC2 (animation):\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/rVr-vZfNL6U\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\n## AWS Deployment: Future plans to extend deployment support via GitLab\n\nCheck out some of the open issues below to see our plans are for the future of deploying to AWS using GitLab.\n\n- [Show AWS deployment success code in\nlogs](https://gitlab.com/gitlab-org/gitlab/-/issues/215333): This will bring the success/failure codes from AWS into your GitLab pipeline logs, allowing you to see the deployment success code without needing to go into the AWS console to retrieve the logs.\n\n- [Show AWS deployment success code in pipeline\nview](https://gitlab.com/gitlab-org/gitlab/-/issues/232983): This will bring the success/failure codes from AWS into your GitLab pipeline, allowing you to see if the deployment job was successful in one view.\n\n- [Auto Deploy to AWS\nS3](https://gitlab.com/gitlab-org/gitlab/-/issues/219087): This will expand the supported deployment targets covered in this blog to include [S3 buckets](https://aws.amazon.com/s3/) as well.\n\n- [AWS integration per-environment role\nmanagement](https://gitlab.com/gitlab-org/gitlab/-/issues/27107): This returns a set of temporary security credentials you can use to access AWS resources that you normally might not be able to access. This is accomplished by using the [AWS\nIAM](https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_identifiers.html)\nroles.\n\n## More material on deploying to EKS and Lambda\n\n- [Demo of how to deploy to\nEKS](https://docs.google.com/presentation/d/1iXnB6lvTx2_-_0ASElLUDZwyFPWILCRx54XjJkMFuw0/edit#slide=id.g6bb36a7017_2_42).\n\n- [Whitepaper on how to deploy on AWS from\nGitLab](/blog/deploy-aws/).\n\nWe invite you to contribute to our other cloud provider solutions:\n\n- [Streamline GCP\ndeployments](https://gitlab.com/groups/gitlab-org/-/epics/2706).\n\n- [Streamline Azure\ndeployments](https://gitlab.com/groups/gitlab-org/-/epics/4846).\n\nAt GitLab, [everyone can contribute](https://handbook.gitlab.com/handbook/company/strategy/#contribute-with-gitlab). 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statement",{"items":681},[682,685,688],{"text":683,"config":684},"Terms",{"href":512,"dataGaName":513,"dataGaLocation":460},{"text":686,"config":687},"Cookies",{"dataGaName":522,"dataGaLocation":460,"id":523,"isOneTrustButton":29},{"text":689,"config":690},"Privacy",{"href":517,"dataGaName":518,"dataGaLocation":460},[692],{"id":693,"title":18,"body":8,"config":694,"content":696,"description":8,"extension":27,"meta":700,"navigation":29,"path":701,"seo":702,"stem":703,"__hash__":704},"blogAuthors/en-us/blog/authors/orit-golowinski.yml",{"template":695},"BlogAuthor",{"name":18,"config":697},{"headshot":698,"ctfId":699},"","ogolowinski",{},"/en-us/blog/authors/orit-golowinski",{},"en-us/blog/authors/orit-golowinski","g_opT-rlrAvUJqtvwZi1IMi9eS5RnsdTKwcP21y6bK0",[706,721,734],{"content":707,"config":719},{"title":708,"description":709,"authors":710,"heroImage":712,"date":713,"body":714,"category":9,"tags":715},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[711],"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/)",[716,717,718],"tutorial","product","features",{"featured":12,"template":13,"slug":720},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":722,"config":732},{"title":723,"description":724,"authors":725,"heroImage":727,"date":728,"category":9,"tags":729,"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.",[726],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[264,613,730],"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":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.",[726],"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",[613,264,717],"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":29,"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":246},"/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",[717,560],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":246},"/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":246},"/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":53,"dataGaLocation":791},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":498,"config":793},{"href":57,"dataGaName":58,"dataGaLocation":791},1773350832244]