[{"data":1,"prerenderedAt":791},["ShallowReactive",2],{"/en-us/blog/measuring-engineering-productivity-at-gitlab":3,"navigation-en-us":36,"banner-en-us":436,"footer-en-us":446,"blog-post-authors-en-us-Clement Ho":688,"blog-related-posts-en-us-measuring-engineering-productivity-at-gitlab":702,"assessment-promotions-en-us":742,"next-steps-en-us":781},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":12,"meta":25,"navigation":26,"path":27,"publishedDate":20,"seo":28,"stem":32,"tagSlugs":33,"__hash__":35},"blogPosts/en-us/blog/measuring-engineering-productivity-at-gitlab.yml","Measuring Engineering Productivity At Gitlab",[7],"clement-ho",null,"engineering",{"slug":11,"featured":12,"template":13},"measuring-engineering-productivity-at-gitlab",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How we measure engineering productivity at GitLab","Learn about how we measure and iterate through this metric",[18],"Clement Ho","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749681533/Blog/Hero%20Images/background.jpg","2020-08-27","\n\nThis blog post was originally published on the GitLab Unfiltered blog. It was reviewed and republished on 2020-09-02.\n\n\nOne of the challenges in a rapidly growing engineering organization is determining how your organization's productivity scales over time. Companies that grow quickly often face a slow down in output because of inefficiencies and communication challenges. For example, a task that you used to be able to ask another coworker to do may now need a comprehensive approval flow.\n\nAt GitLab, we went from 100 to 280 engineers in 1.5 years. As a startup, it was critical that we continued our momentum of:\n\n![Shipping monthly releases => Provide more value to users => Increasing revenue](https://about.gitlab.com/images/blogimages/measuring-engineering-productivity/momentum.png){: .shadow.center}\n\nAs a result, we created several [Key Performance Indicators](/company/kpis/#what-are-kpis) (KPIs) and Performance Indicators (PIs) around this:\n\n- [Throughput](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#throughput)\n- [Product MRs Review to Merge time (RTMT)](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#review-to-merge-time-rtmt)\n- [Development Department Member MR Rate](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#development-department-member-mr-rate)\n- [Say Do Ratio](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#say-do-ratios)\n- [Product MRs by Type](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#product-mrs-by-type)\n\nThe primary one that is often discussed in engineering leadership at GitLab is [Merge Request (MR)](/solutions/continuous-integration/) Rate.\n\nIn this blog post, I'll take a deep dive into how we measure engineering productivity at GitLab using MR Rate, the challenges we've encountered, and what we do to increase this metric. I hope that through this, you'll have a deeper understanding of how we operate at GitLab and inspire you to reflect on how your organization measures engineering productivity.\n\n## What is MR Rate?\n\n![MR Rate = (Total MRs for a team in a given month)/(number of team members employed during that month)](https://about.gitlab.com/images/blogimages/measuring-engineering-productivity/mr-rate-formula.jpeg){: .shadow.center}\n\n**Note:** We include management roles in the team count because we want this metric to be a team metric and want managers to be accountable for their team's metric.\n\nWe use this metric because:\n\n1. We want to incentivize everyone to [iterate](https://handbook.gitlab.com/handbook/values/#iteration) and break down work into smaller MRs because smaller MRs have a faster review time and get merged faster (better developer and maintainer review experience)\n1. The quicker we can deliver features to users, the faster we can iterate upon them\n1. Every MR into the codebase improves the codebase, and every improvement has the downstream effect of making the product better\n\nWhen viewed at an organization level, this metric helps us understand how productivity in the organization changes over time. Although this metric seems simple, it actually requires a lot of detailed analysis as there are many situations to examine:\n\n- New team vs. established team\n- Team performance issues (blocking work or incorrect iteration work breakdown)\n- Individual growth (and performance management)\n- [Community contributions](https://handbook.gitlab.com/handbook/marketing/developer-relations/contributor-success/) vs. independent team contributions\n- Operational productivity constraints\n\nAt first, we measured MRs based on labels associated with the product domain (which generally maps to an existing engineering team). As an open core company, this allowed us to easily aggregate community contributions into the metric. We wanted to account for them because we want to continue encouraging team members to support community contribution MRs and recognize that these MRs continue to help provide the product with more value to users.\n\nUnfortunately, as our organization grew over time, this metric became confusing. Although we had a bot that would label MRs, we occasionally had bad data and mislabeled MRs. In addition, certain teams with product areas that were more mature had more community contributions than others. The combination of these issues evolved the metric into multiple types.\n\n- MR Rate measured through labeling\n- Team MR Rate measured through MR authorship (also known as Narrow MR Rate)\n\nIt's likely that over time this may continue to evolve but for now, these new types of MR Rates have brought more clarity within our organization.\n\n## What are the challenges with MR Rate?\n\nThere are many challenges, but we'll highlight a few notable ones.\n\nFirst of all, one metric never tells the full story. One of the challenges we faced as we hyper focused on this metric was being biased to the number given by the metric rather than truly understanding the story surrounding the metric. For example, a team with a high MR Rate could be shipping quantity over quality. By the MR Rate measurement alone, the organization could unintentionally exemplify teams with unstable features.\n\nIn order to avoid these types of situations, we first ensure that we clearly define our [Definition of Done](https://docs.gitlab.com/ee/development/contributing/merge_request_workflow.html#definition-of-done) and our [maintainer](https://handbook.gitlab.com/handbook/engineering/workflow/code-review/#maintainer) [review process](https://docs.gitlab.com/ee/development/code_review.html). This allows us to set a baseline for quality so that we can set clear expectations in the organization and create clear guidance when MRs are below our standards for quality.\n\nIn addition, we also use other metrics to get a fuller understanding of the story and we regularly introspect about our numbers. We intentionally accompany MR Rate with a few other metrics such as [Product MRs by Type](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#product-mrs-by-type) to better understand the distribution of MRs and [Say Do Ratio](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#say-do-ratios) (this is our latest addition, we're still iterating on it) to better understand how the teams are performing relative to what they committed with product management during the development milestone. We generally use MR Rate to observe trends and regularly ask ourselves, “why is this trending down?” as well as “why is this trending up so much? Is there something that this team is doing that other teams can learn from?”. These are some techniques we use to keep ourselves accountable for understanding the broader picture of the metric.\n\nAnother challenge we faced with MR Rate is balancing it between a team vs. individual metric. As an organization, we want MR Rate to trend upwards over time, and we want to hold engineering leaders accountable for their teams. Engineering directors are responsible for their (organization) sub-department's metrics, and engineering managers are responsible for their team's metrics respectively.\n\nWe intentionally chose not to make MR Rate an individual metric because we do not want to encourage siloed, non-collaborative behavior. For example, we do not want a team member to feel disincentivized to review other team members' MRs or unblock others. This is especially important because [collaboration](https://handbook.gitlab.com/handbook/values/#collaboration) is a company value. Although actions such as making an MR Rate leaderboard could potentially increase the metric for the organization, we have intentionally chosen not to do that because we want to encourage collaboration. We also chose not to use MR Rate as a metric for a team member's underperformance.\n\nThis conscious decision is tricky (especially for smaller teams) because it can be rather difficult for engineering managers to increase their team's MR Rate trends without discussing individual metrics. When teams have less team members, each team member's total MRs in a month would be more impactful to the team's overall MR Rate compared to a larger team. Different teams have attempted to address this in different ways which we will explain in the next section.\n\n## How do we increase MR Rate?\n\nWe use four primary strategies to increase MR Rate.\n\n1. Improving iteration\n1. Setting KPIs\n1. Setting goals (OKRs) to increase KPI\n1. Empowering teams to improve efficiencies\n\nImproving iteration is our primary strategy because team members who are better at iterating are able to create smaller MRs, which results in a higher MR Rate. In our experience, iteration is easy to conceptualize but difficult to apply. Our organization put together some resources (including a [training template](https://gitlab.com/gitlab-com/Product/-/blob/master/.gitlab/issue_templates/iteration-training.md)), and our CEO has set up Iteration Office Hours as an opportunity to coach (most of which are also available publicly on [YouTube](https://www.youtube.com/c/GitLabUnfiltered/search?query=iteration+office+hours+with)).\n\nFrom an organizational perspective, we use KPIs to monitor our MR Rate. Our organization tracks our [Development Department Narrow MR Rate](https://handbook.gitlab.com/handbook/product/groups/product-analysis/engineering/metrics/#development-department-narrow-mr-rate) as our primary KPI with a description, a chart with current and historical data, and a predefined target. As of writing this article, our target is 10, and we are trending toward that target over time.\n\n![Development Department Narrow MR Rate](https://about.gitlab.com/images/blogimages/measuring-engineering-productivity/dept-mr-rate.png){: .shadow.center}\n\n_KPI chart as of August 24, 2020_\n\nEach sub-department under the development department also has their dashboards available publicly (though these dashboards are not as organized and easy to find as the KPI). For example, the Ops sub-department tracks this on their specific [handbook page](https://handbook.gitlab.com/handbook/engineering/development/ops/#ops-sub-department-performance-indicators). We are currently working on consolidating these charts. These KPI dashboards make it easy to understand how the organization is performing and allow us to keep it top of mind.\n\nIn addition to KPIs, each fiscal quarter, engineering management uses these indicators to determine how to set OKRs. In previous quarters, OKRs were set to raise MR Rate to higher targets. This quarter's goal, in light of COVID's long lasting implications, is to maintain the target, because we understand that the current situation is affecting everyone differently. OKRs help align the organization toward the same goals so that everyone understands and can contribute to these goals.\n\nFrom a team perspective, we also empower our engineering managers to experiment with processes to improve efficiency but stay mindful of maintaining healthy work life balance. Some engineering managers choose to use individual MR Rate values as a means of coaching and understanding more about each team member's merge requests. For example, a team member may have a lower MR Rate because he/she is a maintainer, and because of the number of MR reviews received, is unable to have completed as many MRs as he/she could do. Some teams also look through their team's MR Rate on a weekly basis and provide commentary to their directors as a means of understanding more about the metric in order to improve it over time.\n\n## Recap\n\nThe MR Rate is how we've chosen to measure and increase engineering productivity at GitLab. It's not perfect, but we're constantly iterating to make it better. We have yet to determine what our ceiling is or whether we've already reached it but we will definitely share with the wider community when we get to that point. What metrics do you use to measure your organization's engineering productivity? Do you have suggestions or comments about MR Rate? Leave a comment below, and we'll read through them and do our best to respond.\n\n# Special thanks\n\nThanks to the following engineering leaders at GitLab who opened up their calendars to share their insights on this topic:\n\n- [Eric Johnson](/company/team/#edjdev), executive vice president of Engineering\n- [Christopher Lefelhocz](/company/team/#clefelhocz1), vice president of Development\n- [Wayne Haber](/company/team/#whaber), director of Engineering, Threat Management\n- [Sam Goldstein](/company/team/#sgoldstein), director of Engineering, Op\n- [Tim Zallmann](/company/team/#timzallmann), director of Engineering, Dev\n- [Chun Du](/company/team/#cdu1), director of Engineering, Enablement\n- [Bartek Marnane](/company/team/#bmarnane), director of Engineering, Growth\n- [Todd Stadelhofer](/company/team/#tstadelhofer), director of Engineering, Secure\n- [Darby Frey](/company/team/#darbyfrey), senior manager, Engineering, Verify\n- [Daniel Croft](/company/team/#dcroft), senior manager, 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statement",{"items":678},[679,682,685],{"text":680,"config":681},"Terms",{"href":506,"dataGaName":507,"dataGaLocation":454},{"text":683,"config":684},"Cookies",{"dataGaName":516,"dataGaLocation":454,"id":517,"isOneTrustButton":26},{"text":686,"config":687},"Privacy",{"href":511,"dataGaName":512,"dataGaLocation":454},[689],{"id":690,"title":18,"body":8,"config":691,"content":693,"description":8,"extension":24,"meta":697,"navigation":26,"path":698,"seo":699,"stem":700,"__hash__":701},"blogAuthors/en-us/blog/authors/clement-ho.yml",{"template":692},"BlogAuthor",{"name":18,"config":694},{"headshot":695,"ctfId":696},"","ClemMakesApps",{},"/en-us/blog/authors/clement-ho",{},"en-us/blog/authors/clement-ho","6ylR84HlSG3erbjSU_mZmmTKuXk5kNBHOl6FPisA1TE",[703,718,731],{"content":704,"config":716},{"title":705,"description":706,"authors":707,"heroImage":709,"date":710,"body":711,"category":9,"tags":712},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[708],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficulat to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[713,714,715],"tutorial","product","features",{"featured":12,"template":13,"slug":717},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":719,"config":729},{"title":720,"description":721,"authors":722,"heroImage":724,"date":725,"category":9,"tags":726,"body":728},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[723],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[258,610,727],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":730,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":732,"config":740},{"title":733,"description":734,"authors":735,"heroImage":736,"date":737,"category":9,"tags":738,"body":739},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[723],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[610,258,714],"Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":741,"featured":26,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":743},[744,758,769],{"id":745,"categories":746,"header":748,"text":749,"button":750,"image":755},"ai-modernization",[747],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":751,"config":752},"Get your AI maturity score",{"href":753,"dataGaName":754,"dataGaLocation":240},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":756},{"src":757},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":759,"categories":760,"header":761,"text":749,"button":762,"image":766},"devops-modernization",[714,556],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":240},"/assessments/devops-modernization-assessment/",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":770,"categories":771,"header":773,"text":749,"button":774,"image":778},"security-modernization",[772],"security","Are you trading speed for security?",{"text":775,"config":776},"Get your security maturity score",{"href":777,"dataGaName":754,"dataGaLocation":240},"/assessments/security-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":782,"blurb":783,"button":784,"secondaryButton":789},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":785,"config":786},"Get your free trial",{"href":787,"dataGaName":47,"dataGaLocation":788},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":492,"config":790},{"href":51,"dataGaName":52,"dataGaLocation":788},1773350822372]