[{"data":1,"prerenderedAt":812},["ShallowReactive",2],{"/en-us/blog/100-runners-in-less-than-10mins-and-less-than-10-clicks":3,"navigation-en-us":45,"banner-en-us":445,"footer-en-us":455,"blog-post-authors-en-us-Darwin Sanoy|Nupur Sharma":695,"blog-related-posts-en-us-100-runners-in-less-than-10mins-and-less-than-10-clicks":723,"next-steps-en-us":763,"assessment-promotions-en-us":773},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":29,"isFeatured":13,"meta":30,"navigation":31,"path":32,"publishedDate":22,"seo":33,"stem":38,"tagSlugs":39,"__hash__":44},"blogPosts/en-us/blog/100-runners-in-less-than-10mins-and-less-than-10-clicks.yml","100 Runners In Less Than 10mins And Less Than 10 Clicks",[7,8],"darwin-sanoy","nupur-sharma",null,"engineering",{"slug":12,"featured":13,"template":14},"100-runners-in-less-than-10mins-and-less-than-10-clicks",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"How to provision 100 AWS Graviton GitLab Spot Runners in 10 Minutes for $2/hour","Utilizing the GitLab HA Scaling Runner Vending Machine for AWS Automation to setup 100 GitLab runners on AWS Spot.",[19,20],"Darwin Sanoy","Nupur Sharma","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669882/Blog/Hero%20Images/hundredgitlabspotrunner.png","2021-08-17","Managing elastically scaled or highly available compute infrastructures is one of the key challenges the cloud was built for. Application scaling concerns can be handled by cloud services that are purpose designed, rigorously tested, and continually improved. This article dives into some specific enablement automation that brings the benefits of AWS Autoscaling Groups (ASG) to runner management. There are benefits to both the largest fleets and single instance runners.\n\nEmbedded in this article is a YouTube video that demonstrates the deployment of 100 GitLab runners on Amazon EC2 Spot compute in less than 10 minutes using less than 10 clicks. The video also shows updating this entire fleet in under 10 minutes to emphasize the time savings of built-in maintenace.\n\nThe information and automation in this article applies to GitLab Private Runners which are deployed on your own compute resources. Self-managed GitLab instances require private runners, but they can also be configured and used with GitLab.com SaaS accounts.\n\n## Well-architected runner management\n\nThere are many different reasons that a customer might need to deploy multiple runners with various characteristics. Some of the more popular ones are:\n\n- Workloads that require large-scale runner fleets.\n- To gain cost savings through Spot compute, uptime scheduling, and ARM architecture.\n- Projects with high demand of CI activity to make sure that the runner is not being held up by jobs on another project.\n- Jobs that have special security requirements, e.g., security credentials, role-based access or managed identities for Continuous Delivery (CD). These security requirements can enable instance-level (AWS IAM Instance Profile) security by allowing runners with sufficient rights to deploy in specific target environments. For example, a CD runner for non-production environments and a different runner for production.\n- Implementing role-based access control rather than user-based. This means users don't have to use secrets to manage security requirements for CI jobs to accomplish their tasks.\n- Development teams can be confident the runner has the same capabilities for CI and CD automation they test through their interactive logins by leveraging a common IAM role.\n\n### The challenges of building production-grade elastic GitLab Runners\n\n[The GitLab Runner](https://docs.gitlab.com/runner/) is the workhorse of GitLab CI and CD capabilities. The runner can handle numerous operating environments and automation functions for a GitLab instance. The GitLab Runner has become very sophisticated due to the broad range of supported environments. In order to successfully configure the GitLab Runner as a set-it-and-forget-it service, the user has to work through many different decisions and considerations. We summarize some of the GitLab Runner-specific considerations that can be challenging:\n\n- There are a lot of configuration options and scenarios to sort through. It can be an iterative process to discover what needs to be done to set up GitLab Runners.\n- Ensuring runners are a production-grade capability requires Infrastructure as Code (IaC) development so that high availability and scaling can be achieved by automatically spawning new instances.\n- Ensuring that runner deregistration happens correctly when GitLab Runners are automatically scaled in.\n- Additional cost-saving configurations, such as Spot compute and scheduled runner uptime, can complicate the automation requirements for AWS Autoscaling Groups (ASGs).\n- Large organizations often want developers to be able to easily self-service deploy runners with various configurations. Service Management Automation (SMA) has been made popular with products like Service Now, AWS Service Catalog, and AWS Control Tower. This automation is compatible with SMA.\n- It can be difficult to map runners to AWS and map AWS to runners in large organizations with numerous runners and AWS accounts.\n\n### Introducing the GitLab HA Scaling Runner Vending Machine for AWS\n\nAn effective way to handle multiple design considerations is to make a reusable tool. To help you with best practice runner deployments on AWS, we created the [GitLab HA Scaling Runner Vending Machine for AWS](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/) (\"The GitLab Runner Vending Machine\"). It is created in AWS’ Infrastructure as Code, known as CloudFormation.\n\n> **Designed with AWS Well Architected:** This automation has many features beyond the scope of this blog post. The primary focus of this blog post is on managing costs. See the [full list of features here](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/-/blob/main/FEATURES.md).\n\nThe GitLab Runner Vending Machine has the following cost management and scaling management benefits, exposed as a variety of parameters:\n\n- The ability to leverage Spot compute instances. This is important because it leaves CI/CD pipeline developers in charge of whether specific Gitlab CI/CD jobs run on Spot compute or not.\n- ASG-scheduled scaling so that a runner or runner fleet can be completely shutdown when not in use.\n- The GitLab Runner Vending Machine can leverage ARM compute for Linux - which runs faster and costs less.\n- It can also use ASG to update all runners in a fleet with the latest machine images and GitLab Runner version (or a specific version). When maintenance is not built-in, the labor cost of keeping things up-to-date can be significant.\n- Runner naming and tagging in AWS and GitLab, which eases the burden of locating runner instances and managing orphaned runners registrations, whether it is manual or automated.\n\n### How to save money with The GitLab Runner Vending Machine\n\nSignificant savings are possible with this IaC, whether your team wants to save on a single runner or a fleet of them.\n\nThe savings calculations below are for a single runner and should be linear for a given workload. To calculate your savings for more runners, simply multiply the final result by the number of runner instances. The available \"Runner Minutes\" per hour is calculated as the runner's job concurrency setting multiplied by the minutes in an hour. For this exercise, we'll use job concurrency of \"10\". This number should be changed depending on the instance types you are using and the load testing of your typical CI/CD workloads.\n\nJust like most performance analysis, we are assuming that hardware resource utilization is optimal and consistent. If a runner cluster can sustain respectable performance with 80% CPU loading, this calculation assumes that would be maintained regardless of the size of the cluster.\n\n#### AWS Graviton ARM and Spot savings\n\nThe GitLab Runner engineering team has completed performance testing that demonstrates performance gains of more than 30% on some AWS Graviton (ARM-based) instance types. Assuming that runners are performance-managed for optimized utilization, this gain is a direct cost savings. Just recently, we shared [how deploying GitLab on Arm-based AWS Graviton2 resulted in cost savings of 23% and 36% performance gains](/blog/achieving-23-cost-savings-and-36-performance-gain-using-gitlab-and-gitlab-runner-on-arm-neoverse-based-aws-graviton2-processor/).\n\n![ARM Efficiency Test Results For GitLab Runner](https://about.gitlab.com/images/blogimages/hundred-runners/hundredrunners-image1.png)\nGitLab Runner testing results for ARM-efficiency gains.\n\n\n#### Scheduling savings\n\nThe savings can be dramatic when teams are able to turn off runners when not in use. For instance: Scheduling a runner to operate for 40-hours per week saves 76% when compared to the cost of running it for 168 hours. Runners that are just in use for 10 hours per week saves 94%.\n\n#### Combining scheduling, Spot, and ARM to save 97%\n\nJust for fun, let's see what savings are possible by comparing a standard runner scenario with deploying runners in customized, stand-alone instances to the maximum savings automation can deliver.\n\nImagine I am a developer who set up a custom GitLab Runner on an m5.xlarge instance, which is x86 the architecture, for a development team that works for 40 hours on the same time zone. Since there is no automation, the GitLab Runner runs 24/7. We will assume a job concurrency of 10, which gives 600 \"runner minutes\" per hour of run time. Scheduling uptime, running on Spot, and leveraging ARM can all be achieved quickly by redeploying the runner with The GitLab Runner Vending Machine.\n\nHere is the calculation to run the configuration described above, for one week: On Demand, x86, Always On: 1 x m5.xlarge = .192/hr x 168 hrs/week = **$32/week or $1664/year**\n\nHere are the savings that come from running Spot, ARM, and scheduling the Runner to be up just 40hrs/week: 1 x m6g.large Spot = .0419 x 40hrs/week x 64% (36% better performance) = **$1/week**\n\n$1/$32 x 100 = 3.125% of the original cost for the same work. In other words, **we just saved 97%** without ever impacting the ability to get the job done.\n\nIn short, The GitLab Runner Vending Machine intends to bring the many cost saving mechanisms of AWS Cloud computing to your GitLab Runner fleets.\n\nYou can save costs by using ARM/Graviton instances, Spot compute, or by scheduling uptime. In many cases, you can combine all three savings mechanisms for maximum impact.\n\n### Special pipeline building concerns for Spot Runners\n\nSpot instances can disappear with as little as two minutes of warning. This inevitably means some runners will be terminated while jobs are still in progress. CI/CD pipeline developers must take into account whether a job ought to run on compute resources that can disappear with short notice (so short as to be considered \"no notice\"). This comes down to deciding what jobs are OK to run on Spot and what jobs should instead run on AWS' persistent compute known as \"On-Demand\".\n\nThe GitLab Runner Vending Machine accounts for these constraints by tagging runner instances in GitLab with `computetype-spot` or `computetype-ondemand` – indicating in the \"tags\" segment of GitLab CI/CD jobs if a job should run on Spot compute.\n\nSome types of CI workloads, e.g., mass performance testing or large unit testing suites, may already have work queues and work tracking that make it ideal for Spot compute. Other activities, e.g., polling another system for a deployment status, could suffer a material discrepancy if terminated permaturely. Others, such as building the application, are sort of in the middle. Usually, restarting the build is sufficient.\n\n### Job configuration for Spot\n\nIf you need to reschedule terminated work, it is helpful to configure GitLab’s job `retry:` keyword. When working with a dispatching engine or work queue that automatically accounts for incompleted work by processing agents, the retry configuration is unnecessary.\n\nHere is an example that implements both of these concepts:\n\n```yaml\nmy-scaled-test-suite:\n  parallel: 100\n  tags:\n  - computetype-Spot\n  retry:\n    max: 2\n    when:\n      - runner_system_failure\n      - unknown_failure\n\n```\n\nThe usage and limitations of `retry:` are discussed in greater detail in the [GitLab CI documentation on retry](https://docs.gitlab.com/ee/ci/yaml/#retry).\n\n### How to get started\n\nThe CloudFormation templates for the [GitLab Runner Vending Machine are managed in a public project on GitLab.com](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/). There is a lot of information in the project about how the solution works and what problems it aims to solve, and will be useful for very experienced AWS builders.\n\nBut to keep it simple for users who want the quickest path to creating runners of all sizes, it also has an \"easy button\" page that has a table that looks like this:\n\n![Easy Button Page Sample](https://about.gitlab.com/images/blogimages/hundred-runners/hundredrunners-image2.png)\nThe easy buttons launch a CloudFormation Quick Create that only requires filling in a few fields.\n\n\nKeep in mind that easy buttons intentionally hide the high degree of customization that is possible with this automation by setting the parameters for the most common scenarios in advance. Advanced AWS users should read more of the documentation in the repository to understand that the GitLab Runner Vending Machine is also capable of creating sophisticated runner fleets.\n\nFirst, click the CloudFormation icons to launch the Easy Button template directly into the CloudFormation Quick Create console. The Quick Create console is designed for simplicity to enable you to complete the prompts and then click one button to launch the stack.\n\n![CloudFormation Quick Create Example](https://about.gitlab.com/images/blogimages/hundred-runners/hundredrunners-image3.png){: .shadow.medium.center}\nThis is a typical Quick Create form for the GitLab Vending Machine easy buttons.\n\n\nNext, select the deploy region by using the drop down menu in the upper right of the console (where the screenshot says \"Oregon\").\n\nIn most cases, you will only need to add your GitLab instance URL (GitLab.com is fine if that is where your repositories are), and the runner token, which you retrieve from the group level or project you wish to attach the runners to. If you are registering against a self-managed instance, you can use the instance-level tokens from the administrator console to register the runner for use across the entire instance. Read on for [instructions for finding Runner Registration Tokens](https://docs.gitlab.com/runner/register/#requirements).\n\nA few other customization parameters are available for your convenience.\n\nNote that the automation attempts to use the default VPC of the region in which you deploy and the default security group for the VPC. In some organizations, default VPCs and/or their security groups are locked. You can deploy to custom VPCs by using the full template instead of an easy button. On the easy button page look for the footnote \"Not any easy button person?\"\" to find a link to the full template.\n\nWatch the video below to see the deployment of provisioning 100 GitLab Spot Runners on AWS in less than 10 minutes and in less than 10 clicks for just $5 per hour.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube-nocookie.com/embed/EW4RJv5zW4U\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nCheck out the YouTube playlist for more relevant videos about [GitLab and AWS](https://youtube.com/playlist?list=PL05JrBw4t0Ko30Bkf8bAvR-8E441Fy2G9)\n\n### This automation does much, much more\n\nWhile this article focused how much you can saving while using Spot for scaled runners, the underlying automation is capable of many other scenarios. Below is a summary of the additional features and benefits covered in the documentation.\n\n- Scaled runners that are persistent (not Spot) ([see more easy buttons here](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/-/blob/main/easybuttons.md)).\n- Supports small, single runner setups and scaled ones.\n- Supports GitLab.com SaaS or self-managed instances.\n- Automates OS patching and Runner version upgrading.\n- Supports Windows and Linux.\n- Can be reused with Amazon provisioning services such as Service Catalog and Control Tower.\n- Implements least privilege security throughout.\n- Supports deregistering runners on scale-in or Spot termination.\n\nA full feature list is in the document [Features of GitLab HA Scaling Runner Vending Machine for AWS](https://gitlab.com/guided-explorations/aws/gitlab-runner-autoscaling-aws-asg/-/blob/main/FEATURES.md)\n\n### Easy running\n\nWe hope that this automation will make deployment of runners of all sizes simple for you. 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statement",{"items":685},[686,689,692],{"text":687,"config":688},"Terms",{"href":515,"dataGaName":516,"dataGaLocation":463},{"text":690,"config":691},"Cookies",{"dataGaName":525,"dataGaLocation":463,"id":526,"isOneTrustButton":31},{"text":693,"config":694},"Privacy",{"href":520,"dataGaName":521,"dataGaLocation":463},[696,711],{"id":697,"title":19,"body":9,"config":698,"content":700,"description":9,"extension":29,"meta":706,"navigation":31,"path":707,"seo":708,"stem":709,"__hash__":710},"blogAuthors/en-us/blog/authors/darwin-sanoy.yml",{"template":699},"BlogAuthor",{"role":701,"name":19,"config":702},"Field Chief Cloud Architect",{"headshot":703,"linkedin":704,"ctfId":705},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659751/Blog/Author%20Headshots/Darwin-Sanoy-headshot-395-square-gitlab-teampage-avatar.png","https://linkedin.com/in/darwinsanoy","DarwinJS",{},"/en-us/blog/authors/darwin-sanoy",{},"en-us/blog/authors/darwin-sanoy","UkMMwmU5o2e6Y-wBltA9E_z96LvHuB-bG6VW9DsLzIY",{"id":712,"title":20,"body":9,"config":713,"content":714,"description":9,"extension":29,"meta":718,"navigation":31,"path":719,"seo":720,"stem":721,"__hash__":722},"blogAuthors/en-us/blog/authors/nupur-sharma.yml",{"template":699},{"name":20,"config":715},{"headshot":716,"ctfId":717},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","6p7RQDl0cDWnAxU8yu2vVK",{},"/en-us/blog/authors/nupur-sharma",{},"en-us/blog/authors/nupur-sharma","W1cwk5soVjeBGguklBuk4kAvUCs-8zwbXzCNpD6ju3g",[724,739,752],{"content":725,"config":737},{"title":726,"description":727,"authors":728,"heroImage":730,"date":731,"body":732,"category":10,"tags":733},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[729],"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/)",[734,735,736],"tutorial","product","features",{"featured":13,"template":14,"slug":738},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":740,"config":750},{"title":741,"description":742,"authors":743,"heroImage":745,"date":746,"category":10,"tags":747,"body":749},"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.",[744],"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",[267,617,748],"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":751,"featured":13,"template":14},"how-iit-bombay-students-code-future-with-gitlab",{"content":753,"config":761},{"title":754,"description":755,"authors":756,"heroImage":757,"date":758,"category":10,"tags":759,"body":760},"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.",[744],"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",[617,267,735],"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":762,"featured":31,"template":14},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"header":764,"blurb":765,"button":766,"secondaryButton":771},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":767,"config":768},"Get your free trial",{"href":769,"dataGaName":56,"dataGaLocation":770},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":501,"config":772},{"href":60,"dataGaName":61,"dataGaLocation":770},{"promotions":774},[775,789,800],{"id":776,"categories":777,"header":779,"text":780,"button":781,"image":786},"ai-modernization",[778],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":782,"config":783},"Get your AI maturity score",{"href":784,"dataGaName":785,"dataGaLocation":249},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":787},{"src":788},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":790,"categories":791,"header":792,"text":780,"button":793,"image":797},"devops-modernization",[735,563],"Are you just managing tools or shipping innovation?",{"text":794,"config":795},"Get your DevOps maturity score",{"href":796,"dataGaName":785,"dataGaLocation":249},"/assessments/devops-modernization-assessment/",{"config":798},{"src":799},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":801,"categories":802,"header":804,"text":780,"button":805,"image":809},"security-modernization",[803],"security","Are you trading speed for security?",{"text":806,"config":807},"Get your security maturity score",{"href":808,"dataGaName":785,"dataGaLocation":249},"/assessments/security-modernization-assessment/",{"config":810},{"src":811},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",1773350808192]