[{"data":1,"prerenderedAt":791},["ShallowReactive",2],{"/en-us/blog/go-tools-and-gitlab-how-to-do-continuous-integration-like-a-boss":3,"navigation-en-us":39,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Julien Andrieux":689,"blog-related-posts-en-us-go-tools-and-gitlab-how-to-do-continuous-integration-like-a-boss":703,"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":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/go-tools-and-gitlab-how-to-do-continuous-integration-like-a-boss.yml","Go Tools And Gitlab How To Do Continuous Integration Like A Boss",[7],"julien-andrieux",null,"engineering",{"slug":11,"featured":12,"template":13},"go-tools-and-gitlab-how-to-do-continuous-integration-like-a-boss",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Go tools and GitLab: How to do continuous integration like a boss","How the team at Pantomath makes their lives easier with GitLab CI.",[18],"Julien Andrieux","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667978/Blog/Hero%20Images/go-tools-and-gitlab.jpg","2017-11-27","At [Pantomath](https://pantomath.io/), we use [GitLab](/) for all our development work. The purpose of this paper is not to present GitLab and all [its features](/pricing/feature-comparison/), but to introduce how we use these tools to ease our lives. So what is it all about? To automate everything that is related to your development project, and let you focus on your code.\n\n\u003C!-- more -->\n\nWe’ll cover the [lint](https://en.wikipedia.org/wiki/Lint_(software)), [unit tests](https://en.wikipedia.org/wiki/Unit_testing), [data race](https://en.wikipedia.org/wiki/Race_condition), [memory sanitizer](https://clang.llvm.org/docs/MemorySanitizer.html), [code coverage](https://en.wikipedia.org/wiki/Code_coverage), and build.\n\nAll the source code shown in this post is available at [gitlab.com/pantomath-io/demo-tools](https://gitlab.com/pantomath-io/demo-tools). So feel free to get the repository, and use the tags to navigate in it. The repository should be placed in the `src` folder of your `$GOPATH`:\n\n```bash\n$ go get -v -d gitlab.com/pantomath-io/demo-tools\n$ cd $GOPATH/src/gitlab.com/pantomath-io/demo-tools\n```\n\n### Go tools\n\nLuckily, `Go` — the open source programming language also known as golang — comes with a [lot of useful tools](https://golang.org/cmd/go/), to build, test, and check your code. In fact, it’s all there. We’ll just add extra tools to glue them together. But before we go there, we need to take them one by one, and see what they do.\n\n#### Package list\n\nYour Go project is a collection of packages, as described in the [official doc](https://golang.org/doc/code.html). Most of the following tools will be fed with these packages, and thus the first command we need is a way to list the packages. Hopefully, the `Go` language covers our back with the `list` subcommand ([read the fine manual](https://golang.org/cmd/go/#hdr-List_packages) and this [excellent post from Dave Cheney](https://dave.cheney.net/2014/09/14/go-list-your-swiss-army-knife)):\n\n```bash\n$ go list ./...\n```\n\nNote that we want to avoid applying our tools on external packages or resources, and restrict it to **our** code. So we need to get rid of the [vendor directories](https://golang.org/cmd/go/#hdr-Vendor_Directories):\n\n```bash\n$ go list ./... | grep -v /vendor/\n```\n\n#### Lint\n\nThis is the very first tool we use on the code: the linter. Its role is to make sure that the code respects the code style. This may sounds like an optional tool, or at least a “nice-to-have” but it really helps to keep consistent style over your project.\n\nThis linter is not part of Go *per se*, so you need to grab it and install it by hand (see [official doc](https://github.com/golang/lint)).\n\nThe usage is fairly simple: you just run it on the packages of your code (you can also point the `.go` files):\n\n```bash\n$ golint -set_exit_status $(go list ./... | grep -v /vendor/)\n```\n\nNote the `-set_exit_status` option. By default, `golint` only prints the style issues, and returns (with a 0 return code), so the CI never considers something went wrong. If you specify the `-set_exit_status`, the return code from `golint` will be different from 0 if any style issue is encountered.\n\n#### Unit test\n\nThese are the most common tests you can run on your code. For each `.go` file, we need to have an associated `_test.go` file holding the unit tests. You can run the tests for all the packages with the following command:\n\n```bash\n$ go test -short $(go list ./... | grep -v /vendor/)\n```\n\n#### Data race\n\nThis is usually a hard subject to cover, but the `Go` tool has it by default (but only available on `linux/amd64`, `freebsd/amd64`, `darwin/amd64` and `windows/amd64`). For more information about data race, see [this article](https://golang.org/doc/articles/race_detector.html). Meanwhile, here is how to run it:\n\n```bash\n$ go test -race -short $(go list ./... | grep -v /vendor/)\n```\n\n#### Memory sanitizer\n\nClang has a nice detector for uninitialized reads called [MemorySanitizer](https://clang.llvm.org/docs/MemorySanitizer.html). The `go test` tool is kind enough to interact with this Clang module (as soon as you are on `linux/amd64` host and using a recent version of Clang/LLVM (`>=3.8.0`). This command is how to run it:\n\n```bash\n$ go test -msan -short $(go list ./... | grep -v /vendor/)\n```\n\n#### Code coverage\n\nThis is also a must have to evaluate the health of your code, and see what the part of code is under unit tests and what part is not. [Rob Pike](https://twitter.com/rob_pike) wrote a [full post on that very subject](https://blog.golang.org/cover).\n\nTo calculate the code coverage ratio, we need to run the following script:\n\n```bash\n$ PKG_LIST=$(go list ./... | grep -v /vendor/)\n$ for package in ${PKG_LIST}; do\n    go test -covermode=count -coverprofile \"cover/${package##*/}.cov\" \"$package\" ;\ndone\n$ tail -q -n +2 cover/*.cov >> cover/coverage.cov\n$ go tool cover -func=cover/coverage.cov\n```\n\nIf we want to get the coverage report in HTML format, we need to add the following command:\n\n```bash\n$ go tool cover -html=cover/coverage.cov -o coverage.html\n```\n\n#### Build\n\nLast but not least, once the code has been fully tested, we might want to compile it to make sure we can build a working binary.\n\n```bash\n$ go build -i -v gitlab.com/pantomath-io/demo-tools\n```\n\n### Makefile\n\n*git tag:* [init-makefile](https://gitlab.com/pantomath-io/demo-tools/tags/init-makefile)\n\n![](https://cdn-images-1.medium.com/max/1600/1*Ip_q_6I-kNpUjuPMOutuTA.jpeg)\n*\u003Csmall>Photo by [Matt Artz](https://unsplash.com/photos/qJE5Svhs2ek?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\u003C/small>*\n\nNow we have all the tools that we may use in the context of continuous integration, we can wrap them all in a [Makefile](https://gitlab.com/pantomath-io/demo-tools/blob/init-makefile/Makefile), and have a consistent way to call them.\n\nThe purpose of this doc is not to present `make`, but you can refer to [official documentation](https://www.gnu.org/software/make/manual/make.html) to learn more about it.\n\n    PROJECT_NAME := \"demo-tools\"\n    PKG := \"gitlab.com/pantomath-io/$(PROJECT_NAME)\"\n    PKG_LIST := $(shell go list ${PKG}/... | grep -v /vendor/)\n    GO_FILES := $(shell find . -name '*.go' | grep -v /vendor/ | grep -v _test.go)\n\n    .PHONY: all dep build clean test coverage coverhtml lint\n\n    all: build\n\n    lint: ## Lint the files\n      @golint -set_exit_status ${PKG_LIST}\n\n    test: ## Run unittests\n      @go test -short ${PKG_LIST}\n\n    race: dep ## Run data race detector\n      @go test -race -short ${PKG_LIST}\n\n    msan: dep ## Run memory sanitizer\n      @go test -msan -short ${PKG_LIST}\n\n    coverage: ## Generate global code coverage report\n      ./tools/coverage.sh;\n\n    coverhtml: ## Generate global code coverage report in HTML\n      ./tools/coverage.sh html;\n\n    dep: ## Get the dependencies\n      @go get -v -d ./...\n\n    build: dep ## Build the binary file\n      @go build -i -v $(PKG)\n\n    clean: ## Remove previous build\n      @rm -f $(PROJECT_NAME)\n\n    help: ## Display this help screen\n      @grep -h -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = \":.*?## \"}; {printf \"\\033[36m%-30s\\033[0m %s\\n\", $$1, $$2}'\n\nWhat do we have now? One target for any tool previously presented, and three more targets for:\n\n* installation of dependencies (`dep`);\n* housekeeping of the project (`clean`);\n* some nice and shiny help (`help`).\n\nNote that we also had to create a script for the code coverage work. This is because implementing loops over files in a Makefile is a pain. So the work is done in a `bash` script, and the Makefile only triggers this script.\n\nYou can try the Makefile with the following commands:\n\n    $ make help\n    $ make lint\n    $ make coverage\n\n### Continuous integration\n\n*git tag:* [init-ci](https://gitlab.com/pantomath-io/demo-tools/tags/init-ci)\n\nNow the tools are in place, and we can run various tests on our code, we’d like to automate these, on your repository. Luckily, GitLab offers [CI pipelines](/solutions/continuous-integration/) just for this. And the setup for this is pretty straightforward: all you create is a `.gitlab-ci.yml` file at the root of the repository.\n\nThe [full documentation](https://docs.gitlab.com/ee/ci/yaml/) on this Yaml file presents all the options, but you can start with this `.gitlab-ci.yml`:\n\n```yaml\nimage: golang:1.9\n\ncache:\n  paths:\n    - /apt-cache\n    - /go/src/github.com\n    - /go/src/golang.org\n    - /go/src/google.golang.org\n    - /go/src/gopkg.in\n\nstages:\n  - test\n  - build\n\nbefore_script:\n  - mkdir -p /go/src/gitlab.com/pantomath-io /go/src/_/builds\n  - cp -r $CI_PROJECT_DIR /go/src/gitlab.com/pantomath-io/pantomath\n  - ln -s /go/src/gitlab.com/pantomath-io /go/src/_/builds/pantomath-io\n  - make dep\n\nunit_tests:\n  stage: test\n  script:\n    - make test\n\nrace_detector:\n  stage: test\n  script:\n    - make race\n\nmemory_sanitizer:\n  stage: test\n  script:\n    - make msan\n\ncode_coverage:\n  stage: test\n  script:\n    - make coverage\n\ncode_coverage_report:\n  stage: test\n  script:\n    - make coverhtml\n  only:\n  - master\n\nlint_code:\n  stage: test\n  script:\n    - make lint\n\nbuild:\n  stage: build\n  script:\n    - make\n\n```\n\nIf you break down the file, here are some explanations on its content:\n\n* The first thing is to choose what Docker image will be used to run the CI. Head to the [Docker Hub](https://hub.docker.com/) to choose the right image for your project.\n* Then, you specify some folders of this image [to be cached](https://docs.gitlab.com/ee/ci/yaml/#cache). The goal here is to avoid downloading the same content several times. Once a job is completed, the listed paths will be archived, and next job will use the same archive.\n* You define the different `stages` that will group your jobs. In our case, we have two [stages](https://docs.gitlab.com/ee/ci/yaml/#stages) (to be processed in that order): `test` and `build`. We could have other stages, such as `deploy`.\n* The `before_script` [section](https://docs.gitlab.com/ee/ci/yaml/#before_script) defines the commands to run in the Docker container right before the job is actually done. In our context, the commands just copy or link the repository deployed in the `$GOPATH`, and install dependencies.\n* Then come the actual [jobs](https://docs.gitlab.com/ee/ci/jobs/), using the `Makefile` targets. Note the special case for `code_coverage_report` where execution is restricted to the `master` branch (we don’t want to update the code coverage report from feature branches for instance).\n\nAs we commit/push the `.gitlab-ci.yml` file in the repository, the CI is [automatically triggered](https://gitlab.com/pantomath-io/demo-tools/pipelines/13481935). And the pipeline fails. Howcome?\n\nThe `lint_code` [job](https://gitlab.com/pantomath-io/demo-tools/-/jobs/38690212) fails because it can’t find the `golint` binary:\n\n```bash\n$ make lint\nmake: golint: Command not found\nMakefile:11: recipe for target 'lint' failed\nmake: *** [lint] Error 127\n```\n\nSo, [update](https://gitlab.com/pantomath-io/demo-toolscommit/17a0206eb626504e559f56773e2d81c7b5808dbe) your `Makefile` to install `golint` as part of the `dep` target.\n\nThe `memory_sanitizer` [job](https://gitlab.com/pantomath-io/demo-tools/-/jobs/38690209) fails because `gcc` complains:\n\n```bash\n$ make msan\n# runtime/cgo\ngcc: error: unrecognized argument to -fsanitize= option: 'memory'\nMakefile:20: recipe for target 'msan' failed\nmake: *** [msan] Error 2\n```\n\nBut remember we need to use Clang/LLVM `>=3.8.0` to enjoy the `-msan` option in `go test` command.\n\nWe have two options here:\n\n* either we set up Clang in the job (using `before_script`);\n* or we use a Docker image with Clang installed by default.\n\nThe first option is nice, but that implies to have this setup done **for every single job**. This is going to be so long, we should do it once and for all. So we prefer the second option, which is a good way to play with [GitLab Registry](https://docs.gitlab.com/ee/user/packages/container_registry/index.html).\n\n*git tag:* [use-own-docker](https://gitlab.com/pantomath-io/demo-tools/tags/use-own-docker)\n\nWe need to create a [Dockerfile](https://gitlab.com/pantomath-io/demo-tools/blob/use-own-docker/Dockerfile) for the container (as usual: read the [official documentation](https://docs.docker.com/engine/reference/builder) for more options about it):\n\n    # Base image:\n    FROM golang:1.9\n    MAINTAINER Julien Andrieux \u003Cjulien@pantomath.io>\n\n    # Install golint\n    ENV GOPATH /go\n    ENV PATH ${GOPATH}/bin:$PATH\n    RUN go get -u github.com/golang/lint/golint\n\n    # Add apt key for LLVM repository\n    RUN wget -O -\n     | apt-key add -\n\n    # Add LLVM apt repository\n    RUN echo \"deb\n     llvm-toolchain-stretch-5.0 main\" | tee -a /etc/apt/sources.list\n\n    # Install clang from LLVM repository\n    RUN apt-get update && apt-get install -y --no-install-recommends \\\n        clang-5.0 \\\n        && apt-get clean \\\n        && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*\n\n    # Set Clang as default CC\n    ENV set_clang /etc/profile.d/set-clang-cc.sh\n    RUN echo \"export CC=clang-5.0\" | tee -a ${set_clang} && chmod a+x ${set_clang}\n\nThe container built out of this Dockerfile will be based on [golang:1.9](https://hub.docker.com/_/golang/) image (the one referenced in the `.gitlab-ci.yml` file).\n\nWhile we’re at it, we install `golint` in the container, so we have it available. Then we follow [official way](http://apt.llvm.org/) of installing Clang 5.0 from LLVM repository.\n\nNow we have the Dockerfile in place, we need to build the container image and make it available for GitLab:\n\n```bash\n$ docker login registry.gitlab.com\n$ docker build -t registry.gitlab.com/pantomath-io/demo-tools .\n$ docker push registry.gitlab.com/pantomath-io/demo-tools\n```\n\nThe first command connects you to the GitLab Registry. Then you build the container image described in the Dockerfile. And finally, you push it to the GitLab Registry.\n\nTake a look at the [Registry for your repository](https://gitlab.com/pantomath-io/demo-tools/container_registry), you’ll see your image, ready to be used. And to have the CI using your image, you just need to update the `.gitlab-ci.yml` file:\n\n    image: golang:1.9\n\nbecomes\n\n    image: registry.gitlab.com/pantomath-io/demo-tools:latest\n\nOne last detail: you need to tell the CI to use the proper compiler (i.e. the `CC` environment variable), so we add the variable initialization in the `.gitlab-ci.yml` file:\n\n    export CC=clang-5.0\n\nOnce the modification are done, next commit will trigger the pipeline, which now works:\n\n[gitlab.com/pantomath-io/demo-tools/pipelines/13497136](https://gitlab.com/pantomath-io/demo-tools/pipelines/13497136)\n\n### Badges\n\n*git tag:* [init-badges](https://gitlab.com/pantomath-io/demo-tools/tags/init-badges)\n\n![](https://cdn-images-1.medium.com/max/1600/1*0pY_6oCiHZ_eLh0vfg5rDA.jpeg)\n\n*\u003Csmall>Photo by [Jakob Owens](https://unsplash.com/photos/ZBadHaTUkP0?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\u003C/small>*\n\nNow the tools are in place, every commit will launch a test suite, and you probably want to show it, and that’s legitimate :) The best way to do so is to use badges, and the best place for it is the `README` [file](https://gitlab.com/pantomath-io/demo-tools/blob/init-badges/README.md).\n\nEdit it and add the four following badges:\n\n* Build Status: the status of the last pipeline on the `master` branch:\n\n```text\n[![Build Status](https://gitlab.com/pantomath-io/demo-tools/badges/master/build.svg)](https://gitlab.com/pantomath-io/demo-tools/commits/master)\n```\n\n* Coverage Report: the percentage of source code covered by tests\n\n```text\n[![Coverage Report](https://gitlab.com/pantomath-io/demo-tools/badges/master/coverage.svg)](https://gitlab.com/pantomath-io/demo-tools/commits/master)\n```\n\n* Go Report Card:\n\n```text\n[![Go Report Card](https://goreportcard.com/badge/gitlab.com/pantomath-io/demo-tools)](https://goreportcard.com/report/gitlab.com/pantomath-io/demo-tools)\n```\n\n* License:\n\n```text\n[![License MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://img.shields.io/badge/License-MIT-brightgreen.svg)\n```\n\nThe coverage report needs a special configuration. You need to tell GitLab how to get that information, considering that there is a job in the CI that *displays* it when it runs.\u003Cbr> There is a [configuration](https://gitlab.com/help/user/project/pipelines/settings#test-coverage-parsing) to provide GitLab with a regexp, used in any job’ output. If the regexp matches, GitLab consider the match to be the code coverage result.\n\nSo head to `Settings > CI/CD` in your repository, scroll down to the `Test coverage parsing` setting in the `General pipelines settings` section, and use the following regexp:\n\n    total:\\s+\\(statements\\)\\s+(\\d+.\\d+\\%)\n\nYou’re all set! Head to the [overview of your repository](https://gitlab.com/pantomath-io/demo-tools/tree/init-badges), and look at your `README`:\n\n### Conclusion\n\nWhat’s next? Probably more tests in your CI. You can also look at the CD ([Continuous Deployment](https://docs.gitlab.com/ee/ci/environments/index.html)) to automate the deployment of your builds. The documentation can be done using [GoDoc](https://godoc.org/-/about). Note that you generate a coverage report with the `code_coverage_report`, but don’t use it in the CI. You can make the job copy the HTML file to a web server, using `scp` (see this [documentation](https://docs.gitlab.com/ee/ci/ssh_keys/) on how to use SSH keys).\n\nMany thanks to [Charles Francoise](https://dev.to/loderunner) who co-wrote this paper and [gitlab.com/pantomath-io/demo-tools](https://gitlab.com/pantomath-io/demo-tools).\n\n## About the Guest Author\n\nJulien Andrieux is currently working on Pantomath. Pantomath is a modern, open source monitoring solution, built for performance, that bridges the gaps across all levels of your company. The wellbeing of your infrastructure is everyone’s business. [Keep up with the project](http://goo.gl/tcxtXq).\n\n *[Go tools & GitLab - how to do Continuous Integration like a boss](https://medium.com/pantomath/go-tools-gitlab-how-to-do-continuous-integration-like-a-boss-941a3a9ad0b6) was originally published on Medium.*\n\n*Cover photo by [Todd Quackenbush](https://unsplash.com/photos/IClZBVw5W5A?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)*\n",[23,24,25],"CI/CD","user 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to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[709],"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/)",[25,714,715],"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",[260,611,727],"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",[611,260,714],"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":28,"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":242},"/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",[714,557],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":242},"/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":242},"/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":50,"dataGaLocation":788},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":790},{"href":54,"dataGaName":55,"dataGaLocation":788},1773350817907]