[{"data":1,"prerenderedAt":796},["ShallowReactive",2],{"/en-us/blog/environment-friction-cycle":3,"navigation-en-us":41,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Darwin Sanoy":691,"blog-related-posts-en-us-environment-friction-cycle":707,"assessment-promotions-en-us":747,"next-steps-en-us":786},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":40},"blogPosts/en-us/blog/environment-friction-cycle.yml","Environment Friction Cycle",[7],"darwin-sanoy",null,"engineering",{"slug":11,"featured":12,"template":13},"environment-friction-cycle",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How GitLab can eliminate the massive value stream friction of developer environment provisioning and cleanup","It is important to have the complete picture of scaled effects in view when designing automation.",[18],"Darwin Sanoy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682507/Blog/Hero%20Images/sandeep-singh-3KbACriapqQ-unsplash.jpg","2022-11-17","A strong DevOps value stream drives developer empowerment as far left as possible. In GitLab, this is embodied in per-feature branch merge requests that are rich with automated code quality and defect information - including not only findings - but automated remediation capabilities and collaboration. Some defects and code quality issues can only be found by analyzing a running copy of the application, including DAST, IAST, fuzzing and many others. GitLab has built a fully automated, seamless developer environment lifecycle management approach right into the developer experience. In fact, it’s so seamlessly built-in, it can be easy to overlook how critical developer environment lifecycle management is. This article will highlight why and how GitLab adds value using developer environment automation. In addition, while GitLab provides out of the box developer environment lifecycle management for Kubernetes, this article demonstrates an approach and a working example of how to extend that capability to other common cloud-based application framework PaaS offerings.\n\n## Provisioning of development environments is generally a negative feedback loop\n\nIn a prior job, I worked on a DevOps transformation team that supported multiple massive shared development environments in AWS. They were accessible to more than 4,000 developers working to build more than 100 SaaS applications and utility stacks. In the journey to the AWS Cloud, each development team took ownership of the automation required to deploy their applications. Since developers were able to self-service, over time this solved the problem of development friction generated by waiting for environments to be provisioned for testing, feature experiments, integration experiments, etc. \n\nHowever, the other half of the problem then ballooned - environment sprawl - with an untold number of environments idling without management and without knowledge of when they could be torn down. Over time the development environment cost became a significant multiple of production costs. The cloud has solved problems with environment provisioning bottlenecks due to hardware acquisition and provisioning, but this can also inadvertently fuel the high costs of unmanaged sprawl. This problem understandably causes organizations to raise administrative barriers to new development environments.\n\nIn many organizations this becomes a vicious cycle - most especially if developer environments are operated by a different team, or worse, on an independent budget. Environment justification friction usually comes quickly after discovering the true cost of the current running environments. Developers then have to justify the need for new environment requests and they have to make the gravest of promises to disband the environment as soon as they are done. Another friction arises when a separate group is tasked with cost controls and environment provisioning and cleanup. This introduces friction in the form of administrative and work queueing delays. Coordination friction also crops up because an accurate understanding of exactly what is needed for an environment can be challenging to convey. When mistakes are made or key information is missing, developers must go back and forth on support requests to get the configuration completely correct.\n\n## Partial automation can worsen the problem\n\nThat’s the first half of the environment lifecycle, but as I mentioned, even if that is fully automated and under the control of developers, the other half of the feedback loop comes into play. When a given development environment has fulfilled its initial justification reason, the team does not want to destroy it because environments are so hard to justify and create. Then the sprawl starts and, of course, the barriers to new environments are raised even higher. This is a classic negative feedback loop.\n\nSystems theory shows us that sometimes there are just a few key factors in stopping or even reversing a negative feedback loop. Lets take this specific problem apart and talk about how GitLab solves for it.\n\n## Treat developer environments as a complete lifecycle\n\nIn the prior example it is evident that by leaving out the last stage of the environment lifecycle - retirement or tear down - we still end up with a negative feedback loop. Removing provisioning friction actually makes the problem worse if retirement friction is not also addressed at the same time. Solutions to this problem need to address the entire lifecycle to avoid impacting value stream velocity. Neglecting or avoiding the retirement stage of a lifecycle is a common problem across all types of systems. In contrast, by addressing the entire lifecycle we can transform it from being a negative feedback loop to a managed lifecycle.\n\n## The problems of who and when\n\nBuried inside the insidious friction loop are a couple key coordination problems we’ll call “Who and When.” Basically, \"Who\" should create environments and \"When\" should they be created to ensure reasonable cost optimization? Then again, _Who_ should cleanup environments and _When_ do you know that the environment is no longer needed with certainty? Even with highly collaborative teams working hard together for maximum business value, these questions present a difficulty that frequently results in environments running for a long time before they are used and after they are no longer needed. The knowledge of appropriate timing plays a critical role in gaining control over this source of friction.\n\n## The problem of non-immutable development environments\n\nFriction in environment lifecycle management creates a substantial knock-on problem associated with long-lived environments. Long-lived environments that are updated multiple times for various independent projects start to accumulate configuration rot; they become snowflakes with small changes that are left over from non-implemented experiments, software or configuration removals, and other irrelevant bits and pieces. Immutability is the practice of not doing “in place” updates to a computing element, but rather destroying it and replacing it with a fresh, built-from-scratch, element. Docker has made this concept very accepted and effective in production workloads, but development environments frequently do not have this attribute due to automating without the design constraint of immutability, so they are updated in-place for reuse by various initiatives. If the environment lifecycle is not fully automated, it impossible to make them workable on a per-feature branch basis.\n\n## The problem of non-isolated development environments \n\nWhen environments are manually provisioned or when there is a lot of cost or administrative friction to setting them up, environment sharing becomes more common place. This creates sharing contention at many levels. Waiting to schedule into use an environment, pressure to complete work quickly so others can use the environment, and restrictions on the types of changes that can be made to shared environments are just some of the common sharing contention elements that arise. If environments can be isolated, then sharing contention friction evaporates. Pushing this to the extreme of a per-feature branch granularity brings many benefits, but is also difficult.\n\n## Effect on the development value stream\n\nThe effect that a friction-filled environment lifecycle has on the value stream can be immense - how many stories have you heard of projects waylaid for weeks or months while waiting on environment provisioning? What about defects shipped to production because a shared environment had left over configuration during testing? Frequently this friction is tolerated in the value stream because no one will argue that unlimited environment sprawl is an unwise use of company resources. We all turn off the lights in our home when we are no longer using a room and it is good business sense and good stewardship not to leave idle resources running at work.\n\nThe concept of good stewardship of planetary resources is actually becoming an architectural level priority in the technology sector. This is in evidenced in AWS’ [introduction of the “Sustainability” pillar to the AWS Well Architected principals in 2021](https://aws.amazon.com/blogs/aws/sustainability-pillar-well-architected-framework/) and many other green initiatives in the technology sector.\n\nIt’s imperative that efforts to improve the development value stream consider whether developer environment management friction is hampering the breadth, depth and velocity of product management and software development.\n\n## Seamless and fully automated review environment lifecycle management\n\nWhat if this negative feedback loop could be stopped? What if new environments were seamless and automatically created right at the moment they were needed? What if developers were completely happy to immediately tear down an environment when they were done because it takes no justification nor effort on their part to create new one at will?\n\nEnter GitLab Review Environments!\n\nGitLab review apps are created by the developer action of creating a new branch. No humans are involved as the environment is deployed while the developer is musing their first code changes on their branch.\n\nAs the developer pushes code updates the review apps are automatically updated with the changes and all quality checks and security scanning are run to ensure the developer understands that they introduced a vulnerability or quality defect. This is done within the shortest possible amount of time after the defect was introduced.\n\nWhen the developer merges their code, the review app is automatically torn down.\n\nThis seamless approach to developer environment provisioning and cleanup addresses enough of the critical factors in the negative feedback loop that it is effectively nullified.\n\nConsider:\n\n- Developer environment provisioning and cleanup are fully automated, transparent, developer-initiated activities. They do not consume people nor human process resources, which are always legions slower and more expensive than technology solutions.\n- Provisioning and cleanup timing are exactly synchronized with the developer’s need, preventing inefficiencies in idle time before or after environment usage.\n- They are immutable on a new branch basis - a new branch always creates a new environment from fresh copy of the latest code.\n- They are isolated - no sharing contention and no mixing of varying configuration.\n- They treat developer environments as a lifecycle.\n\nIt is so transparent that some developers may not even realize that their feature branch has an isolated environment associated with it.\n\n## Hard dollar costs are important and opportunity costs are paramount\n\nGitLab environments positively contribute to the value stream in two critical ways. First, the actual waste of idle machines is dramatically reduced. However, more importantly, all the human processes that end up being applied to managing that waste also disappear. Machines running in the cloud are only lost money. Inefficient use of people’s time carries a high dollar cost but it also carries a higher opportunity cost. There are so many value-generating activities people can do when their time is unencumbered by cost-control administration.\n\n## Multiplying the value stream contributions of developer review environments\n\nDeveloper environment friction is an industry-wide challenge and GitLab nearly eliminates the core problems of this feedback cycle. However, GitLab has also gone way beyond simply addressing this problem by creating a lot of additional value through seamless per-feature branch developer environments.\n\nHere is a visualization of where dynamic review environments plug into the overall GitLab developer workflow.\n\n![](https://about.gitlab.com/images/blogimages/environment-friction-lifecycle/gitlabenvironmentlifecycle.png)\n\n**Figure 1: Review environments with AWS Cloud Services**\n\nFigure 1 is showing GitLab’s full development cycle support with a little art of the possible thrown in around interfacing with AWS deployment services. The green dashed arrow indicates that GitLab deploys a review environment when the branch is first created. Since the green arrow is part of the developer's iteration loop, the green arrow is also depicting that review app updates are done on each code push. \n\nThe light purple box is showing that the iterative development and CI checks are all within the context of a merge request (MR), which provides a Single Pane of Glass (SPOG) for all quality checks, vulnerabilities and collaboration. Finally, when the merge is done, the review environment is cleaned up. The feature branch merge request is the furthest left that visibility and remediation can be shifted. GitLab’s shifting of this into the developer feature branch is what gives developers a semi-private opportunity to fix any quality or security findings with the specific code they have added or updated.\n\nOne other thing to note here is that when GitLab CD code is engineered to handle review environments, it is reused for all other preproduction and production environments. The set of AWS icons after the “Release” icon would be using the same deployment code. However, if the GitLab CD code is engineered only around deploying to a set of static environments, it is not automatically capable of review environments. Review environment support is a superset of static environment support.\n\n## Review environments enable a profound shift left of visibility and remediation\n\nAt GitLab “shift left” is not just about “problem visibility” but also about “full developer enablement to resolve problems” while in-context. GitLab merge requests provide critical elements that encourage developers to get into a habit of defect remediation:\n\n- **Context** - Defect and vulnerability reporting is only for code the developer changed in their branch and is tracked by the merge request (MR) for that branch.\n- **Responsibility** - Since MRs and branches are associated to an individual, it is evident to the developer (and the whole team) what defects were introduced or discovered by which developers.\n- **Timing** - Developers become aware of defects nearly as soon as they are introduced, not weeks or months after having integrated with other code. If they were working on a physical product, we can envision that all the parts are still on the assembly bench.\n- **Visibility - Appropriately Local, Then Appropriately Global** - Visibility of defects is context specific. While a developer has an open MR that is still a work in progress, they can be left alone to remedy accidentally-introduced defects with little concern from others because the visibility is local to the MR. However, once they seek approvals to merge their code, then the approval process for the MR will cause the visibility of any unresolved defects and vulnerabilities to come to the attention of everyone involved in the approval process. This ensures that oversight happens with just the right timing - not too early and not forgotten. This makes a large-scale contribution to human efficiency in the development value stream.\n- **Advisement** - As much as possible GitLab integrates tools and advice right into the feature branch MR context where the defects are visible. Developers are given full vulnerability details and can take just-in-time training on specific vulnerabilities. \n- **Automated Remediation** - Developers can choose to apply auto-remediations when they are available.\n- **Collaboration** - They can use MR comments and new issues to collaborate with team mates throughout the organization on resolving defects of all types.\n\nHaving seamless, effortless review environments at a per-feature branch granularity is a critical ingredient in GitLab’s ability to maximize the shift left of the above developer capabilities. This is most critical in the developer checks that require a running copy of application, which is provided by the review environments. These checks include things such as DAST, IAST, API fuzzing and accessibility testing. The industry is also continuing to multiply the types of defect scanners that require an actively running copy of the application.\n\n## Extending GitLab review environments to other cloud application framework PaaS\n\nSo you may be thinking, “I love GitLab review environments, but not all of our applications are targeting Kubernetes.” It is true that the out- of-the-box showcasing of GitLab review environments depends on Kubernetes. One of the key reasons for this is that Kubernetes provides an integrated declarative deployment capability known as deployment manifests. The environment isolation capability, known as namespaces, also provides a critical capability. GitLab wires these Kubernetes capabilities up to a few key pieces of GitLab CD to accomplish the magic of isolated, per-feature branch review environments.\n\nAs far as I know there is no formal or defacto industry term for what I’ll call “Cloud Application Framework PaaS.” Cloud-provided PaaS can be targeted at various “levels” of the problem of building applications. For instance, primitive components such as AWS ELB address the problem of application load balancing by providing a variety of virtual, cloud-scaling and secured appliances that you can use as a component of building an application. Another example is [AWS Cognito](https://aws.amazon.com/cognito/) to help with providing user login and profile services to an application build.\n\nHowever, there are also cloud PaaS offerings that seek to solve the entire problem of rapid application building and maintenance. These are services like AWS Amplify and AWS AppRunner. These services frequently knit together primitive PaaS components (such as described above) into a composite that attempts to accelerate the entire process of building applications. Frequently these PaaS also include special CLIs or other developer tools that attempt to abstract the creation, maintenance and deployment of an Infrastructure as Code layer. They also tend to be [GitOps](/topics/gitops/)-oriented by storing this IaC in the same repository as the application code, which enables full control over deployments via Git controls such as branches and merge requests.\n\nThis approach relieves developers of early stage applications from having to learn IaC or hire IaC operations professionals too early. Basically it allows avoidance of overly early optimization of onboarding IaC skills. If the application is indeed successful it is quite common to outgrow the integrated IaC support provided by these specialized PaaS, however, the evolution is very natural because the managed IaC can simply start to be developed by specialists.\n\nThe distinction of cloud application framework PaaS is important when understanding where GitLab can create compound value with Dynamic Review Environments. I will refer to this kind of PaaS as “Cloud Application Infrastructure PaaS” that tries to solve the entire “Building Applications Problem.”\n\nSo we have a bunch of GitLab interfaces and conventions for implementing seamless developer review environments and we have non-Kubernetes cloud application infrastructures that provide declarative deployment interfaces and we can indeed make them work together! Interesting it is all done in GitLab CI YAML, which means that once you see the art of the possible, you can start implementing dynamic review environment lifecycle management for many custom environment types with the existing GitLab features. \n\n## A working, non-Kubernetes example of dynamic review environments in action\n\n![](https://about.gitlab.com/images/blogimages/environment-friction-lifecycle/CloudFormationDeployAnimatedGif.gif)\n\n**Figure 2: Working CD example of review environments for AWS CloudFormation**\n\nFigure 2 shows the details of an actual non-Kubernetes working example called CloudFormation AutoDeploy With Dynamic Review Environments. This project enables any AWS CloudFormation template to be deployed. It specifically supports an isolated stack deployment whenever a review branch is created and then also destroys that environment when the branch is merged. \n\nHere are some of the key design constraints and best practices that allow it to support automated review environments.:\n\n- **The code is implemented as an include.** Notice that the main [.gitlab-ci.yml](https://gitlab.com/guided-explorations/aws/cloudformation-deploy/-/blob/main/.gitlab-ci.yml) files have only variables applicable to this project and then the inclusion of Deploy-AWSCloudFormation.gitlab-ci.yml. This allows you to treat the CloudFormation integration as a managed process, shared include to be improved and updated. If the stress of backward compatibility of managing a shared dependency is too much, you can encourage developers to make a copy of this file to essentially version peg it with their project.\n\n- **Avoids Conflict with Auto DevOps CI Stage Names** - The [standard stages of Auto Devops are here](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/gitlab/ci/templates/Auto-DevOps.gitlab-ci.yml#L70). This constraint allows the auto deploy template to be leveraged. \n\n- **Creates and Sequences Custom Stages as Necessary** - For instance, you can see we’ve added `create-changeset` stage and jobs.\n\n- The `deploy-review` job and it’s `environment:` section must have a very specific construction, let’s look at the important details:\n\n  ```text\n    rules:\n      - if: '$CI_COMMIT_BRANCH == \"main\"'\n        when: never\n      - if: '$REVIEW_DISABLED'\n        when: never\n      - if: '($CI_COMMIT_TAG || $CI_COMMIT_BRANCH) && $REQUIRE_CHANGESET_APPROVALS == \"true\"'\n        when: manual\n      - if: '($CI_COMMIT_TAG || $CI_COMMIT_BRANCH) && $REQUIRE_CHANGESET_APPROVALS != \"true\"'\n    artifacts:\n      reports:\n        dotenv: envurl.env\n    environment:\n      name: review/$CI_COMMIT_REF_SLUG\n      url: $DYNAMIC_ENVIRONMENT_URL\n      on_stop: stop_review\n  ```\n\n  \n\n  - `rules:` are used to ensure this job only runs when we are not on the main branch. The main branch implements long lived stage and prod environments.\n  - `artifacts:reports:dotenv` allows variables populated during a CI job to become pipeline level variables. The most critical role this does in this job is to allow the URL retrieved from CloudFormation Outputs to be populated into the variable DYNAMIC_ENVIRONMENT_URL. The file `enviurl.env` would have at least the line `DYNAMIC_ENVIRONMENT_URL={url-from-cloudformation}` in it. You can see this in the job code as `echo \"DYNAMIC_ENVIRONMENT_URL=${STACK_ENV_URL}\" >> envurl.env`\n  - `environment:name:` is using the Auto Deploy convention of placing review apps under the review environments top level called `review` The reference $CI_COMMIT_REF_SLUG ensures that the branch (or tag name) is used, but with all illegal characters removed. By your development convention, the Environment Name should become a part of the IaC constructs that ensure both uniqueness as well as identifiability by this pipeline. In GitLab's standard auto deploy for Kubernetes this is done by constructing a namespace that contains the name in this provided parameter. In CloudFormation we make it part of the Stack Name. The value here is exposed in the job as the variable ${ENVRONMENT}.\n  - `environment:url:` it is not self-evident here that the variable DYNAMIC_ENVIRONMENT_URL was populated by the deployment job and added to the file `enviro.env` so that it would contain the right value at this time. This causes the GitLab “Environment” page to have a clickable link to visit the environment. It also is used by DAST and other live application scan engines to find and scan the isolated environment.\n  - `environment:on_stop:` in the deploy-review job is what maps to the `stop_review` named job. This is the magic sauce behind automatic environment deletion when a feature branch is merged. `stop_review` must be written with the correct commands to accomplish the teardown.\n\n## A reusable engineering pattern\n\nThis CloudFormation pattern serves as a higher-level pattern of how GitLab review environments can be adopted to any other cloud “Application Level PaaS.” This is a term I use to indicate a cloud PaaS that is abstracted highly enough that developers think of it as “a place to deploy applications.” Perhaps a good way to contrast it with PaaS that does not claim to serve as an entire application platform. Cloud-based load balancers are a good example of a PaaS that performs a utility function for applications but is not a place to build an entire cloud application. \n\n## Application PaaS for abstracting IaC concerns for developers\n\nGitLab auto deploy combines well with the cloud application framework PaaS that has a disposition toward developer productivity by reducing or eliminating IaC management required by developers. AWS Amplify has such productivity support in the form of a developer specific CLI which allows impacting to be authored and updated in the same Git repository where the application code is stored. 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Architect",{"headshot":699,"linkedin":700,"ctfId":701},"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",[708,723,736],{"content":709,"config":721},{"title":710,"description":711,"authors":712,"heroImage":714,"date":715,"body":716,"category":9,"tags":717},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[713],"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/)",[718,719,720],"tutorial","product","features",{"featured":12,"template":13,"slug":722},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":724,"config":734},{"title":725,"description":726,"authors":727,"heroImage":729,"date":730,"category":9,"tags":731,"body":733},"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.",[728],"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",[263,613,732],"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":735,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":737,"config":745},{"title":738,"description":739,"authors":740,"heroImage":741,"date":742,"category":9,"tags":743,"body":744},"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.",[728],"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",[613,263,719],"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":746,"featured":28,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":748},[749,763,774],{"id":750,"categories":751,"header":753,"text":754,"button":755,"image":760},"ai-modernization",[752],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":756,"config":757},"Get your AI maturity score",{"href":758,"dataGaName":759,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":761},{"src":762},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":764,"categories":765,"header":766,"text":754,"button":767,"image":771},"devops-modernization",[719,559],"Are you just managing tools or shipping innovation?",{"text":768,"config":769},"Get your DevOps maturity score",{"href":770,"dataGaName":759,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":772},{"src":773},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":775,"categories":776,"header":778,"text":754,"button":779,"image":783},"security-modernization",[777],"security","Are you trading speed for security?",{"text":780,"config":781},"Get your security maturity score",{"href":782,"dataGaName":759,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":787,"blurb":788,"button":789,"secondaryButton":794},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":790,"config":791},"Get your free trial",{"href":792,"dataGaName":52,"dataGaLocation":793},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":795},{"href":56,"dataGaName":57,"dataGaLocation":793},1773350812447]