[{"data":1,"prerenderedAt":789},["ShallowReactive",2],{"/en-us/blog/supercharge-your-git-workflows":3,"navigation-en-us":32,"banner-en-us":432,"footer-en-us":442,"blog-post-authors-en-us-Darwin Sanoy":684,"blog-related-posts-en-us-supercharge-your-git-workflows":700,"assessment-promotions-en-us":740,"next-steps-en-us":779},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":12,"path":27,"publishedDate":20,"seo":28,"stem":29,"tagSlugs":30,"__hash__":31},"blogPosts/en-us/blog/supercharge-your-git-workflows.yml","Supercharge Your Git Workflows",[7],"darwin-sanoy",null,"engineering",{"slug":11,"featured":12,"template":13},"supercharge-your-git-workflows",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"category":9,"tags":21,"body":24},"Supercharge your Git workflows","Optimize git clone operations in any environment — up to 93% reduction in clone times and 98% reduction in disk space usage — with the Git Much Faster script.",[18],"Darwin Sanoy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098264/Blog/Hero%20Images/Blog/Hero%20Images/AdobeStock_519147119_2RafH61mqosMZv8HGAlsUj_1750098264407.jpg","2025-09-10",[22,23],"tutorial","git","Picture this: You're working on the Chromium project and you need to clone the repository. You run `git clone`, grab a coffee, check your email, maybe take a lunch break, and 95 minutes later, you finally have your working directory. This is the reality for developers working with large repositories containing 50GB+ of data.\nThe productivity impact is staggering. CI/CD pipelines grind to a halt waiting for repository clones. Infrastructure costs skyrocket as compute resources sit idle. Developer frustration mounts as context-switching becomes the norm.\nBut what if that 95-minute wait could be reduced to just 6 minutes? What if you could achieve a 93% reduction in clone times using proven techniques?\nEnter [Git Much Faster](https://gitlab.com/gitlab-accelerates-embedded/misc/git-much-faster) — a comprehensive benchmarking and optimization script that transforms how you work with large Git repositories. Built from real-world experience optimizing embedded development workflows, this script provides practical strategies delivering measurable performance improvements across standard git clones, optimized configurations, and Git's built-in Scalar tool.\nYou'll discover how to dramatically reduce git clone times using optimization strategies, explore real-world performance benchmarks from major repositories like the Linux kernel and Chromium, and understand how to implement these optimizations safely in both development and CI/CD environments.\n## Project overview: What is Git Much Faster?\nGit Much Faster is a script I wrote as an enablement tool to allow you to benchmark multiple clone optimization approaches on the same client — whether that is a traditional developer workstation, CI, cloud-hosted development environments or specialized clones for GitOps. It also contains the curated configuration settings for the fastest clone optimization. You can use these settings as a starting point and adapt or remove configurations that create too lean of a clone for your client's intended use of the repository clone.\nGit Much Faster addresses a fundamental challenge: Git's default clone behavior prioritizes safety over speed. While this works for small repositories, it becomes a significant bottleneck with large codebases, extensive binary assets, or complex monorepo structures.\nThe problem manifests across increasingly common scenarios. Embedded development teams inherit repositories filled with legacy firmware binaries, bootloaders, and vendor SDKs stored directly in version control. Web applications accumulate years of marketing assets and design files. Game development projects contain massive 3D models and audio files growing repository sizes into tens of gigabytes.\nEnterprise CI/CD pipelines suffer particularly acute pain. Each job requires a fresh repository clone, and when operations take 20 to 90 minutes, entire development workflows grind to a halt. Infrastructure costs multiply as compute resources remain idle during lengthy clone operations.\nGit Much Faster solves this through comprehensive benchmarking comparing four distinct strategies: standard git clone (baseline with full history), optimized git clone (custom configurations with compression disabled and sparse checkout), Git's Scalar clone (integrated partial cloning), and current directory assessment (analyzing existing repositories without re-cloning).\nThe tool provides measurable, repeatable benchmarking in controlled AWS environments, eliminating variables that make performance testing unreliable. The real power of Git Much Faster is to run all the benchmarks in whatever your target environment looks like — so if slow network connections are a reality for some developers, you can decipher the best clone optimization for their situation.\n## Technical deep dive: The optimization strategies\nUnderstanding Git Much Faster's effectiveness requires examining specific configurations that address Git's performance bottlenecks through a layered approach tackling network transfer efficiency, CPU utilization, and storage patterns.\nThe most significant gains come from two key optimizations. The first, `core.compression=0`, eliminates CPU-intensive compression during network operations. CPU cycles spent compressing often exceed bandwidth savings on modern high-speed networks. This optimization alone reduces clone times by 40% to 60%.\nThe second major optimization, `http.postBuffer=1024M`, addresses Git's conservative HTTP buffer sizing. Large repositories benefit tremendously from increased buffer sizes, allowing Git to handle larger operations without breaking them into multiple requests, reducing protocol overhead.\nGit Much Faster leverages shallow clones using `--depth=1` (fetching only the latest commit) and partial clones with `--filter=blob:none` (deferring file content downloads until checkout). Shallow clones reduce data by 70%-90% for mature repositories, while partial clones prove particularly effective for repositories with large binary assets.\nSparse checkout provides surgical precision in controlling checked-out files. Git Much Faster implements comprehensive exclusion covering 30+ binary file types — images, documents, archives, media files, and executables — reducing working directory size by up to 78% while maintaining full source code access.\nGit's Scalar tool, integrated into Git since Version 2.38, combines partial clone, sparse checkout, and background maintenance. However, benchmarking reveals Scalar doesn't implement the aggressive compression and buffer optimizations providing the most significant performance gains. Testing shows the custom optimized approach typically outperforms Scalar by 48%-67% while achieving similar disk space savings.\n## End-to-end load reduction\nAn interesting thing about optimizing the clone operation is that it also reduces complete system loading because you are reducing the size of your request. GitLab has a specialized, horizontal scaling layer known as [Gitaly Cluster](https://docs.gitlab.com/administration/gitaly/praefect/). When full history clones and large monorepos are the norm, the sizing of Gitaly Cluster is driven higher. This is because all git clone requests are serviced by a server-side binary to create “pack files” to be sent over the wire. Since these server-side git operations involve running compression utilities, it drives all three of memory, CPU, and I/O requirements at once.\nWhen git clone operations are optimized to reduce the size of the total content ask, it reduces load on the end-to-end stack: Client, Network, Gitaly Service and Storage. All layers speed up and become cheaper at the same time.\n## Real-world performance results\nGit Much Faster's effectiveness is demonstrated through rigorous benchmarking across diverse, real-world repositories using consistent AWS infrastructure with Arm instances and controlled network conditions.\n**Linux kernel repository (7.5GB total):** Standard clone took 6 minutes 29 seconds. Optimized clone achieved 46.28 seconds — an 88.1% improvement, reducing the .git directory from 5.9GB to 284MB. Scalar took 2 minutes 21 seconds (63.7% improvement), completing 67.3% slower than the optimized approach.\n**Chromium repository (60.9GB total):** Standard clone required 95 minutes 12 seconds. Optimized clone achieved 6 minutes 41 seconds — a dramatic 93% improvement, compressing the .git directory from 55.7GB to 850MB. Scalar took 13 minutes 3 seconds (86.3% improvement) but remained 48.8% slower than the optimized approach.\n**GitLab website repository (8.9GB total):** Standard clone took 6 minutes 23 seconds. Optimized clone achieved 6.49 seconds — a remarkable 98.3% improvement, reducing the .git directory to 37MB. Scalar took 33.60 seconds (91.2% improvement) while remaining 80.7% slower.\nThe benchmarking reveals clear patterns: Larger repositories show more dramatic improvements, binary-heavy repositories benefit most from sparse checkout filtering, and the custom optimization approach consistently outperforms both standard Git and Scalar across all repository types.\n## Practical implementation guide\nImplementation requires understanding when to apply each technique based on use case and risk tolerance. For development requiring full repository access, use standard Git cloning. For read-heavy workflows needing rapid access to current code, deploy optimized cloning. For CI/CD pipelines where speed is paramount, optimized cloning provides maximum benefit.\nGetting started requires only simple download and execution:\n```bash\ncurl -L https://gitlab.com/gitlab-accelerates-embedded/misc/git-much-faster/-/raw/master/git-much-faster.sh -o ./git-much-faster.sh\n# For benchmarking bash ./git-much-faster.sh --methods=optimized,standard --repo=https://github.com/your-org/your-repo.git\n```\nFor production-grade testing, Git Much Faster project includes complete Terraform infrastructure for AWS deployment, eliminating variables that skew local testing results.\nOptimized clones require careful consideration of limitations. Shallow clones prevent access to historical commits, limiting operations like `git log` across file history. For teams adopting optimizations it is best to create specific optimizations for high volume usage. For instance, developers can perform an optimized clone, and if and when needed, convert to full clones when needed via `git fetch --unshallow`. If a given CI job accesses commit history (e.g. using GitVersion), then you may need the full history, but not a checkout.\n## Use cases and industry applications\nEmbedded development presents unique challenges where projects historically stored compiled firmware and hardware design files directly in version control. These repositories often contain FPGA bitstreams, PCB layouts, and vendor SDK distributions ballooning sizes into tens of gigabytes. Build processes frequently require cloning dozens of external repositories, multiplying performance impact.\nEnterprise monorepos encounter Git performance challenges as repositories grow encompassing multiple projects and accumulated historical data. Media and asset-heavy projects compound challenges, as mentioned above — web applications accumulate marketing assets over years, while game development faces severe challenges with 3D models and audio files pushing repositories beyond 100GB. [More use cases](https://gitlab.com/gitlab-accelerates-embedded/misc/git-much-faster/-/tree/master?ref_type=heads#common-use-cases) can be found in the project.\nCI/CD pipelines represent the most impactful application. Each container-based CI job requires a fresh repository clone, and when operations consume 20 to 90 minutes, entire development workflows become unviable.\nGeographically spread out development teams may have team members whose network performance to their primary development workstation is extremely limited or varies dramatically. Optimizing the Git clone can help by reducing over the wire sizes dramatically.\n## Next steps\nGit clone optimization represents a transformative opportunity delivering measurable improvements — up to 93% reduction in clone times and 98% reduction in disk space usage — that fundamentally change how teams interact with codebases.\nThe key insight is that Git's default conservative approach leaves substantial performance opportunities untapped. By understanding specific bottlenecks — network transfer inefficiency, CPU-intensive compression, unnecessary data downloads — teams can implement targeted optimizations delivering transformative results.\n\n**Ready to revolutionize your Git workflows?**\n[Read the docs in the Git Much Faster repository](https://gitlab.com/gitlab-accelerates-embedded/misc/git-much-faster) and get started running benchmarks against your largest repositories. Begin with read-only optimization in CI/CD pipelines where benefits are immediate and risks minimal. As your team gains confidence, gradually expand optimization to development workflows based on measured results.\nThe future of Git performance optimization continues evolving, but fundamental principles — eliminating unnecessary work, optimizing for actual bottlenecks, measuring results rigorously — remain valuable regardless of future tooling evolution. Teams mastering these concepts today position themselves to leverage whatever improvements tomorrow's Git ecosystem provides.\n","yml",{},"/en-us/blog/supercharge-your-git-workflows",{"title":15,"description":16},"en-us/blog/supercharge-your-git-workflows",[22,23],"qo3-eSaevSwQX9zKV6BAaKUrjTcFAVPr5nJ1zkbGS1Q",{"data":33},{"logo":34,"freeTrial":39,"sales":44,"login":49,"items":54,"search":362,"minimal":393,"duo":412,"pricingDeployment":422},{"config":35},{"href":36,"dataGaName":37,"dataGaLocation":38},"/","gitlab logo","header",{"text":40,"config":41},"Get free trial",{"href":42,"dataGaName":43,"dataGaLocation":38},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":45,"config":46},"Talk to sales",{"href":47,"dataGaName":48,"dataGaLocation":38},"/sales/","sales",{"text":50,"config":51},"Sign in",{"href":52,"dataGaName":53,"dataGaLocation":38},"https://gitlab.com/users/sign_in/","sign 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inclusion and belonging (DIB)",{"href":660,"dataGaName":661,"dataGaLocation":450},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":324,"config":663},{"href":326,"dataGaName":327,"dataGaLocation":450},{"text":334,"config":665},{"href":336,"dataGaName":337,"dataGaLocation":450},{"text":339,"config":667},{"href":341,"dataGaName":342,"dataGaLocation":450},{"text":669,"config":670},"Modern Slavery Transparency Statement",{"href":671,"dataGaName":672,"dataGaLocation":450},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":674},[675,678,681],{"text":676,"config":677},"Terms",{"href":502,"dataGaName":503,"dataGaLocation":450},{"text":679,"config":680},"Cookies",{"dataGaName":512,"dataGaLocation":450,"id":513,"isOneTrustButton":12},{"text":682,"config":683},"Privacy",{"href":507,"dataGaName":508,"dataGaLocation":450},[685],{"id":686,"title":18,"body":8,"config":687,"content":689,"description":8,"extension":25,"meta":695,"navigation":12,"path":696,"seo":697,"stem":698,"__hash__":699},"blogAuthors/en-us/blog/authors/darwin-sanoy.yml",{"template":688},"BlogAuthor",{"role":690,"name":18,"config":691},"Field Chief Cloud Architect",{"headshot":692,"linkedin":693,"ctfId":694},"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",[701,716,729],{"content":702,"config":713},{"title":703,"description":704,"authors":705,"heroImage":707,"date":708,"body":709,"category":9,"tags":710},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[706],"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/)",[22,711,712],"product","features",{"featured":714,"template":13,"slug":715},false,"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":717,"config":727},{"title":718,"description":719,"authors":720,"heroImage":722,"date":723,"category":9,"tags":724,"body":726},"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.",[721],"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",[254,606,725],"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":728,"featured":714,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":730,"config":738},{"title":731,"description":732,"authors":733,"heroImage":734,"date":735,"category":9,"tags":736,"body":737},"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.",[721],"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",[606,254,711],"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":739,"featured":12,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":741},[742,756,767],{"id":743,"categories":744,"header":746,"text":747,"button":748,"image":753},"ai-modernization",[745],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":749,"config":750},"Get your AI maturity score",{"href":751,"dataGaName":752,"dataGaLocation":236},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":754},{"src":755},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":757,"categories":758,"header":759,"text":747,"button":760,"image":764},"devops-modernization",[711,552],"Are you just managing tools or shipping innovation?",{"text":761,"config":762},"Get your DevOps maturity score",{"href":763,"dataGaName":752,"dataGaLocation":236},"/assessments/devops-modernization-assessment/",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":768,"categories":769,"header":771,"text":747,"button":772,"image":776},"security-modernization",[770],"security","Are you trading speed for security?",{"text":773,"config":774},"Get your security maturity score",{"href":775,"dataGaName":752,"dataGaLocation":236},"/assessments/security-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":780,"blurb":781,"button":782,"secondaryButton":787},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":783,"config":784},"Get your free trial",{"href":785,"dataGaName":43,"dataGaLocation":786},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":488,"config":788},{"href":47,"dataGaName":48,"dataGaLocation":786},1773350835181]