[{"data":1,"prerenderedAt":792},["ShallowReactive",2],{"/en-us/blog/machine-learning-on-the-gitlab-devops-platform":3,"navigation-en-us":40,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-William Arias":689,"blog-related-posts-en-us-machine-learning-on-the-gitlab-devops-platform":703,"assessment-promotions-en-us":743,"next-steps-en-us":782},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/machine-learning-on-the-gitlab-devops-platform.yml","Machine Learning On The Gitlab Devops Platform",[7],"william-arias",null,"engineering",{"slug":11,"featured":12,"template":13},"machine-learning-on-the-gitlab-devops-platform",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How Comet can streamline machine learning on The GitLab DevOps Platform","Here's a step-by-step look at how to bring ML into software development using Comet on GitLab's DevOps Platform.",[18],"William Arias","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749669991/Blog/Hero%20Images/ways-to-encourage-collaboration.jpg","2021-11-08","\n\nBuilding machine learning-powered applications comes with numerous challenges. When we talk about these challenges, there is a tendency to overly focus on problems related to the quality of a model’s predictions—things like data drift, changes in model architectures, or inference latency.\n\nWhile these are all problems worthy of deep consideration, an often overlooked challenge in [ML development](/topics/devops/the-role-of-ai-in-devops/) is the process of integrating a model into an existing software application.\n\nIf you’re tasked with adding an ML feature to a product, you will almost certainly run into an existing codebase that must play nicely with your model. This is, to put it mildly, not an easy task.\n\nML is a highly iterative discipline. Teams often make many changes to their codebase and pipelines in the process of developing a model. Coupling an ML codebase to an application’s dependencies, unit tests, and CI/CD pipelines will significantly reduce the velocity with which ML teams can deliver on a solution, since each change would require running these downstream dependencies before a merge can be approved.\n\nIn this post, we’re going to demonstrate how you can use [Comet](https://www.comet.ml/site/) with [GitLab’s DevOps platform](/solutions/devops-platform/) to streamline the workflow for your ML and software engineering teams, allowing them to collaborate without getting in each other's way.\n\n## The challenge for ML teams working with application teams\n\nLet’s say your team is working on improving a feature engineering pipeline. You will likely have to test many combinations of features with some baseline model for the task to see which combinations make an impact on model performance.\nIt is hard to know beforehand which features might be significant, so having to run multiple experiments is inevitable. If your ML code is a part of your application codebase, this would mean having to run your application’s CI/CD pipeline for every feature combination you might be trying.\n\nThis will certainly frustrate your Engineering and DevOps teams, since you would be unnecessarily tying up system resources, given that software engineering teams do not need to run their pipelines with the same frequency as ML teams do.\n\nThe other issue is that despite having to run numerous experiments, only a single set of outputs from these experiments will make it to your production application. Therefore, the rest of the assets produced through these experiments are not relevant to your application code.\n\nKeeping these two codebases separated will make life a lot easier for everyone – but it also introduces the problem of syncing the latest model between two codebases.\n\n## Use The GitLab DevOps Platform and Comet for your model development process\n\nWith The GitLab DevOps platform and Comet, we can keep the workflows between ML and engineering teams separated, while enabling cross-team collaboration by preserving the visibility and auditability of the entire model development process across teams.\n\nWe will use two separate projects to demonstrate this process. One project will contain our application code for a handwritten digit recognizer, while the other will contain all the code relevant to training and evaluating our model.\n\nWe will adopt a process where discussions, code reviews, and model performance metrics get automatically published and tracked within The GitLab DevOps Platform, increasing the velocity and opportunity for collaboration between data scientists and software engineers for machine learning workflows.\n\n## Project setup\n\nOur project consists of two projects: [comet-model-trainer](https://gitlab.com/tech-marketing/devops-platform/comet-model-trainer) and [ml-ui](https://gitlab.com/tech-marketing/devops-platform/canara-review-apps-testing).\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/cometmodeltrainer.png){: .shadow}\n\nThe **comet-model-trainer** repository contains scripts to train and evaluate a model on the MNIST dataset. We have set up The GitLab DevOps Platform in a way that runs the training and evaluation Pipeline whenever a new merge request is opened with the necessary changes.\n\nThe **ml-ui** repository contains the necessary code to build the frontend of our ML application.\n\nSince the code is integrated with Comet, your ML team can easily track the source code, hyperparameters, metrics, and other details related to the development of the model.\n\nOnce the training and evaluation steps are completed, we can use Comet to fetch summary metrics from the project as well as metrics from the Candidate model and display them within the merge request; This will allow the ML team to easily review the changes to the model.\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/buildmodelgraph.png){: .shadow}\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/summarymetrics.png){: .shadow}\n\nIn our case, the average accuracy of the models in the project is 97%. Our Candidate model achieved an accuracy of 99%, so it looks like it is a good fit to promote to production. The metrics displayed here are completely configurable and can be changed as necessary.\n\nWhen the merge request is approved, the deployment pipeline is triggered and the model is pushed to Comet’s Model Registry. The Model Registry versions each model and links it back to the Comet Experiment that produced it.\n![Alt text for your image](https://about.gitlab.com/images/blogimages/OpenComet_SparkVideo.gif){: .shadow}\n\nOnce the model is pushed to the Model Registry, it is available to the application code. When the application team wishes to deploy this new version of the model to their app, they simply have to trigger their specific deployment pipeline.\n\n## Running the pipeline\n\n### Pipeline outline\n\nWe will run the process outlined below every time a team member creates a merge request to change code in the `build-neural-network`script:\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/modelapprove.png){: .shadow}\n\nNow, let’s take a look at the yaml config used to define our CI/CD pipelines depicted in the previous diagram:\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/workflowsbranch.png){: .shadow}\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/script.png)\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/registermodel.png){: .shadow}\n\nLet's break down the CI/CD pipeline by describing the gitlab-ci.yml file so you can use it and customize it to your needs.\n\nWe start by instructing our GitLab runners to utilize Python:3.8 to run the jobs specified in the pipeline:\n\n`Image: python:3.8`\n\nThen, we define the job where we want to build and train the neural network:\n\n`Build-neural-network`\n\n### Build-neural-network\n\nIn this step, we start by creating a folder where we will store the artifacts generated by this job, install dependencies using the requirements.txt file, and finally  execute the corresponding Python script that will be in charge of training the neural network. The training runs in the GitLab runner using the Python image defined above, along with its dependencies.\n\nOnce the `build-neural-network` job has finalized successfully, we move to the next job: `write-report-mr`\n\nHere, we use another image created by DVC that will allow us to publish a report right in the merge request opened by the contributor who changed code in the neural network script. In this way, we’ve brought software development workflows to the development of ML applications. With the report provided by this job, code and model review can be executed within the merge request view, enabling teams to collaborate not only around the code but also the model performance.\n\nFrom the merge request page, we get access to loss curves and other relevant performance metrics from the model we are training, along with a link to the Comet Experiment UI, where richer details are provided to evaluate the model performance. These details include interactive charts for model metrics, the model hyperparameters, and Confusion Matrices of the test set performance, to name a few.\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/manualDeploy_SparkVideo.gif){: .shadow}\n\nWhen the team is done with the code and model review,  the merge request gets approved, and the script that generated the model is merged into the main codebase, along with its respective commit and the CI pipeline associated to it. This takes us to the next job:\n\n### Register-model\n\nThis job uses an integration between GitLab and Comet to upload the reviewed and accepted version of the model to the Comet Model Registry. If you recall, the Model Registry is where models intended for production can be logged and versioned. In order to run the commands that will register the model, we need to set up these variables:\n\n- COMET_WORKSPACE\n- COMET_PROJECT_NAME\nIn order to do that, follow the steps described [here](https://docs.gitlab.com/ee/ci/variables/#add-a-cicd-variable-to-an-instance).\n\nIt is worth noting that the `register-model` job only runs when the merge request gets reviewed and approved, and this behavior is obtained by setting `only: main` at the end of the job.\n\nFinally, we decide to let a team member have final control of the deployment so therefore we define a manual job:\n`Deploy-ml-ui`\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/deployuiml.png)\n\nWhen triggered, this job will import the model from Comet’s Model Registry and automatically create the necessary containers to build the user interface and deploy to a Kubernetes cluster.\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/downstream.png)\n\nThis job triggers a downstream pipeline, which means that the UI for this MNIST application resides in a different project. This keeps the codebase for the UI and model training separated but integrated and connected at the moment of deploying the model to a production environment.\n\n![Alt text for your image](https://about.gitlab.com/images/blogimages/multipipeline_SparkVideo.gif){: .shadow}\n\n## Key takeaways\n\nIn this post, we addressed some of the challenges faced by ML and software teams when it comes to collaborating on delivering ML-powered applications. Some of these challenges include:\n\n* The discrepancy in the frequency with which each of these teams need to iterate on their codebases and CI/CD pipelines.\n\n* The fact that only a single set of experiment assets from an ML experimentation pipeline is relevant to the application.\n\n* The challenge of syncing a model or other experiment assets across independent codebases.\n\nUsing The GitLab DevOps Platform and Comet, we can start bridging the gap between ML and software engineering teams over the course of a project.\n\nBy having model performance metrics adopted into software development workflows like the one we saw in the issue and merge request, we can keep track of the code changes, discussions, experiments, and models created in the process. All the operations executed by the team are recorded, can be audited, are end-to end-traceable, and (most importantly) reproducible.\n\nWatch a demo of this process:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/W_DsNl5aAVk\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n_About Comet:_\nComet is an MLOps Platform that is designed to help data scientists and teams build better models faster! Comet provides tooling to Track, Explain, Manage, and Monitor your models in a single place!\n\nLearn more about Comet [here](https://www.comet.ml/site/) and get started for free!\n\n\n\n",[23,24,25,26],"DevOps","demo","integrations","AI/ML","yml",{},true,"/en-us/blog/machine-learning-on-the-gitlab-devops-platform",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"https://about.gitlab.com/blog/machine-learning-on-the-gitlab-devops-platform","https://about.gitlab.com","article","en-us/blog/machine-learning-on-the-gitlab-devops-platform",[37,24,25,38],"devops","aiml","LpRc-YBMZnK_UBjtGHsXikpV30j1yboOs4OqsXT3F6k",{"data":41},{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":369,"minimal":400,"duo":419,"pricingDeployment":429},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab logo","header",{"text":48,"config":49},"Get free trial",{"href":50,"dataGaName":51,"dataGaLocation":46},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":53,"config":54},"Talk to sales",{"href":55,"dataGaName":56,"dataGaLocation":46},"/sales/","sales",{"text":58,"config":59},"Sign in",{"href":60,"dataGaName":61,"dataGaLocation":46},"https://gitlab.com/users/sign_in/","sign in",[63,90,185,190,290,350],{"text":64,"config":65,"cards":67},"Platform",{"dataNavLevelOne":66},"platform",[68,74,82],{"title":64,"description":69,"link":70},"The intelligent orchestration platform for DevSecOps",{"text":71,"config":72},"Explore our Platform",{"href":73,"dataGaName":66,"dataGaLocation":46},"/platform/",{"title":75,"description":76,"link":77},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":78,"config":79},"Meet GitLab Duo",{"href":80,"dataGaName":81,"dataGaLocation":46},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":83,"description":84,"link":85},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":86,"config":87},"Learn more",{"href":88,"dataGaName":89,"dataGaLocation":46},"/why-gitlab/","why gitlab",{"text":91,"left":29,"config":92,"link":94,"lists":98,"footer":167},"Product",{"dataNavLevelOne":93},"solutions",{"text":95,"config":96},"View all Solutions",{"href":97,"dataGaName":93,"dataGaLocation":46},"/solutions/",[99,123,146],{"title":100,"description":101,"link":102,"items":107},"Automation","CI/CD and automation to accelerate deployment",{"config":103},{"icon":104,"href":105,"dataGaName":106,"dataGaLocation":46},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[108,112,115,119],{"text":109,"config":110},"CI/CD",{"href":111,"dataGaLocation":46,"dataGaName":109},"/solutions/continuous-integration/",{"text":75,"config":113},{"href":80,"dataGaLocation":46,"dataGaName":114},"gitlab duo agent platform - product menu",{"text":116,"config":117},"Source Code Management",{"href":118,"dataGaLocation":46,"dataGaName":116},"/solutions/source-code-management/",{"text":120,"config":121},"Automated Software Delivery",{"href":105,"dataGaLocation":46,"dataGaName":122},"Automated software delivery",{"title":124,"description":125,"link":126,"items":131},"Security","Deliver code faster without compromising security",{"config":127},{"href":128,"dataGaName":129,"dataGaLocation":46,"icon":130},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[132,136,141],{"text":133,"config":134},"Application Security Testing",{"href":128,"dataGaName":135,"dataGaLocation":46},"Application security testing",{"text":137,"config":138},"Software Supply Chain Security",{"href":139,"dataGaLocation":46,"dataGaName":140},"/solutions/supply-chain/","Software supply chain security",{"text":142,"config":143},"Software Compliance",{"href":144,"dataGaName":145,"dataGaLocation":46},"/solutions/software-compliance/","software compliance",{"title":147,"link":148,"items":153},"Measurement",{"config":149},{"icon":150,"href":151,"dataGaName":152,"dataGaLocation":46},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[154,158,162],{"text":155,"config":156},"Visibility & Measurement",{"href":151,"dataGaLocation":46,"dataGaName":157},"Visibility and Measurement",{"text":159,"config":160},"Value Stream Management",{"href":161,"dataGaLocation":46,"dataGaName":159},"/solutions/value-stream-management/",{"text":163,"config":164},"Analytics & Insights",{"href":165,"dataGaLocation":46,"dataGaName":166},"/solutions/analytics-and-insights/","Analytics and insights",{"title":168,"items":169},"GitLab for",[170,175,180],{"text":171,"config":172},"Enterprise",{"href":173,"dataGaLocation":46,"dataGaName":174},"/enterprise/","enterprise",{"text":176,"config":177},"Small Business",{"href":178,"dataGaLocation":46,"dataGaName":179},"/small-business/","small business",{"text":181,"config":182},"Public Sector",{"href":183,"dataGaLocation":46,"dataGaName":184},"/solutions/public-sector/","public sector",{"text":186,"config":187},"Pricing",{"href":188,"dataGaName":189,"dataGaLocation":46,"dataNavLevelOne":189},"/pricing/","pricing",{"text":191,"config":192,"link":194,"lists":198,"feature":277},"Resources",{"dataNavLevelOne":193},"resources",{"text":195,"config":196},"View all resources",{"href":197,"dataGaName":193,"dataGaLocation":46},"/resources/",[199,231,249],{"title":200,"items":201},"Getting started",[202,207,212,217,222,227],{"text":203,"config":204},"Install",{"href":205,"dataGaName":206,"dataGaLocation":46},"/install/","install",{"text":208,"config":209},"Quick start guides",{"href":210,"dataGaName":211,"dataGaLocation":46},"/get-started/","quick setup checklists",{"text":213,"config":214},"Learn",{"href":215,"dataGaLocation":46,"dataGaName":216},"https://university.gitlab.com/","learn",{"text":218,"config":219},"Product documentation",{"href":220,"dataGaName":221,"dataGaLocation":46},"https://docs.gitlab.com/","product documentation",{"text":223,"config":224},"Best practice videos",{"href":225,"dataGaName":226,"dataGaLocation":46},"/getting-started-videos/","best practice videos",{"text":228,"config":229},"Integrations",{"href":230,"dataGaName":25,"dataGaLocation":46},"/integrations/",{"title":232,"items":233},"Discover",[234,239,244],{"text":235,"config":236},"Customer success stories",{"href":237,"dataGaName":238,"dataGaLocation":46},"/customers/","customer success stories",{"text":240,"config":241},"Blog",{"href":242,"dataGaName":243,"dataGaLocation":46},"/blog/","blog",{"text":245,"config":246},"Remote",{"href":247,"dataGaName":248,"dataGaLocation":46},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":250,"items":251},"Connect",[252,257,262,267,272],{"text":253,"config":254},"GitLab Services",{"href":255,"dataGaName":256,"dataGaLocation":46},"/services/","services",{"text":258,"config":259},"Community",{"href":260,"dataGaName":261,"dataGaLocation":46},"/community/","community",{"text":263,"config":264},"Forum",{"href":265,"dataGaName":266,"dataGaLocation":46},"https://forum.gitlab.com/","forum",{"text":268,"config":269},"Events",{"href":270,"dataGaName":271,"dataGaLocation":46},"/events/","events",{"text":273,"config":274},"Partners",{"href":275,"dataGaName":276,"dataGaLocation":46},"/partners/","partners",{"backgroundColor":278,"textColor":279,"text":280,"image":281,"link":285},"#2f2a6b","#fff","Insights for the future of software development",{"altText":282,"config":283},"the source promo card",{"src":284},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":286,"config":287},"Read the latest",{"href":288,"dataGaName":289,"dataGaLocation":46},"/the-source/","the source",{"text":291,"config":292,"lists":294},"Company",{"dataNavLevelOne":293},"company",[295],{"items":296},[297,302,308,310,315,320,325,330,335,340,345],{"text":298,"config":299},"About",{"href":300,"dataGaName":301,"dataGaLocation":46},"/company/","about",{"text":303,"config":304,"footerGa":307},"Jobs",{"href":305,"dataGaName":306,"dataGaLocation":46},"/jobs/","jobs",{"dataGaName":306},{"text":268,"config":309},{"href":270,"dataGaName":271,"dataGaLocation":46},{"text":311,"config":312},"Leadership",{"href":313,"dataGaName":314,"dataGaLocation":46},"/company/team/e-group/","leadership",{"text":316,"config":317},"Team",{"href":318,"dataGaName":319,"dataGaLocation":46},"/company/team/","team",{"text":321,"config":322},"Handbook",{"href":323,"dataGaName":324,"dataGaLocation":46},"https://handbook.gitlab.com/","handbook",{"text":326,"config":327},"Investor relations",{"href":328,"dataGaName":329,"dataGaLocation":46},"https://ir.gitlab.com/","investor relations",{"text":331,"config":332},"Trust Center",{"href":333,"dataGaName":334,"dataGaLocation":46},"/security/","trust center",{"text":336,"config":337},"AI Transparency Center",{"href":338,"dataGaName":339,"dataGaLocation":46},"/ai-transparency-center/","ai transparency center",{"text":341,"config":342},"Newsletter",{"href":343,"dataGaName":344,"dataGaLocation":46},"/company/contact/#contact-forms","newsletter",{"text":346,"config":347},"Press",{"href":348,"dataGaName":349,"dataGaLocation":46},"/press/","press",{"text":351,"config":352,"lists":353},"Contact us",{"dataNavLevelOne":293},[354],{"items":355},[356,359,364],{"text":53,"config":357},{"href":55,"dataGaName":358,"dataGaLocation":46},"talk to sales",{"text":360,"config":361},"Support portal",{"href":362,"dataGaName":363,"dataGaLocation":46},"https://support.gitlab.com","support portal",{"text":365,"config":366},"Customer portal",{"href":367,"dataGaName":368,"dataGaLocation":46},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":370,"login":371,"suggestions":378},"Close",{"text":372,"link":373},"To search repositories and projects, login to",{"text":374,"config":375},"gitlab.com",{"href":60,"dataGaName":376,"dataGaLocation":377},"search login","search",{"text":379,"default":380},"Suggestions",[381,383,387,389,393,397],{"text":75,"config":382},{"href":80,"dataGaName":75,"dataGaLocation":377},{"text":384,"config":385},"Code Suggestions (AI)",{"href":386,"dataGaName":384,"dataGaLocation":377},"/solutions/code-suggestions/",{"text":109,"config":388},{"href":111,"dataGaName":109,"dataGaLocation":377},{"text":390,"config":391},"GitLab on AWS",{"href":392,"dataGaName":390,"dataGaLocation":377},"/partners/technology-partners/aws/",{"text":394,"config":395},"GitLab on Google Cloud",{"href":396,"dataGaName":394,"dataGaLocation":377},"/partners/technology-partners/google-cloud-platform/",{"text":398,"config":399},"Why GitLab?",{"href":88,"dataGaName":398,"dataGaLocation":377},{"freeTrial":401,"mobileIcon":406,"desktopIcon":411,"secondaryButton":414},{"text":402,"config":403},"Start free trial",{"href":404,"dataGaName":51,"dataGaLocation":405},"https://gitlab.com/-/trials/new/","nav",{"altText":407,"config":408},"Gitlab Icon",{"src":409,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":407,"config":412},{"src":413,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":415,"config":416},"Get Started",{"href":417,"dataGaName":418,"dataGaLocation":405},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/get-started/","get started",{"freeTrial":420,"mobileIcon":425,"desktopIcon":427},{"text":421,"config":422},"Learn more about GitLab Duo",{"href":423,"dataGaName":424,"dataGaLocation":405},"/gitlab-duo/","gitlab duo",{"altText":407,"config":426},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":428},{"src":413,"dataGaName":410,"dataGaLocation":405},{"freeTrial":430,"mobileIcon":435,"desktopIcon":437},{"text":431,"config":432},"Back to pricing",{"href":188,"dataGaName":433,"dataGaLocation":405,"icon":434},"back to pricing","GoBack",{"altText":407,"config":436},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":438},{"src":413,"dataGaName":410,"dataGaLocation":405},{"title":440,"button":441,"config":446},"See how agentic AI transforms software delivery",{"text":442,"config":443},"Watch GitLab Transcend now",{"href":444,"dataGaName":445,"dataGaLocation":46},"/events/transcend/virtual/","transcend event",{"layout":447,"icon":448},"release","AiStar",{"data":450},{"text":451,"source":452,"edit":458,"contribute":463,"config":468,"items":473,"minimal":678},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":453,"config":454},"View page source",{"href":455,"dataGaName":456,"dataGaLocation":457},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":459,"config":460},"Edit this page",{"href":461,"dataGaName":462,"dataGaLocation":457},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":464,"config":465},"Please contribute",{"href":466,"dataGaName":467,"dataGaLocation":457},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":469,"facebook":470,"youtube":471,"linkedin":472},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[474,521,573,617,644],{"title":186,"links":475,"subMenu":490},[476,480,485],{"text":477,"config":478},"View plans",{"href":188,"dataGaName":479,"dataGaLocation":457},"view plans",{"text":481,"config":482},"Why Premium?",{"href":483,"dataGaName":484,"dataGaLocation":457},"/pricing/premium/","why premium",{"text":486,"config":487},"Why Ultimate?",{"href":488,"dataGaName":489,"dataGaLocation":457},"/pricing/ultimate/","why ultimate",[491],{"title":492,"links":493},"Contact Us",[494,497,499,501,506,511,516],{"text":495,"config":496},"Contact sales",{"href":55,"dataGaName":56,"dataGaLocation":457},{"text":360,"config":498},{"href":362,"dataGaName":363,"dataGaLocation":457},{"text":365,"config":500},{"href":367,"dataGaName":368,"dataGaLocation":457},{"text":502,"config":503},"Status",{"href":504,"dataGaName":505,"dataGaLocation":457},"https://status.gitlab.com/","status",{"text":507,"config":508},"Terms of use",{"href":509,"dataGaName":510,"dataGaLocation":457},"/terms/","terms of use",{"text":512,"config":513},"Privacy statement",{"href":514,"dataGaName":515,"dataGaLocation":457},"/privacy/","privacy statement",{"text":517,"config":518},"Cookie preferences",{"dataGaName":519,"dataGaLocation":457,"id":520,"isOneTrustButton":29},"cookie preferences","ot-sdk-btn",{"title":91,"links":522,"subMenu":531},[523,527],{"text":524,"config":525},"DevSecOps platform",{"href":73,"dataGaName":526,"dataGaLocation":457},"devsecops platform",{"text":528,"config":529},"AI-Assisted Development",{"href":423,"dataGaName":530,"dataGaLocation":457},"ai-assisted development",[532],{"title":533,"links":534},"Topics",[535,540,545,548,553,558,563,568],{"text":536,"config":537},"CICD",{"href":538,"dataGaName":539,"dataGaLocation":457},"/topics/ci-cd/","cicd",{"text":541,"config":542},"GitOps",{"href":543,"dataGaName":544,"dataGaLocation":457},"/topics/gitops/","gitops",{"text":23,"config":546},{"href":547,"dataGaName":37,"dataGaLocation":457},"/topics/devops/",{"text":549,"config":550},"Version Control",{"href":551,"dataGaName":552,"dataGaLocation":457},"/topics/version-control/","version control",{"text":554,"config":555},"DevSecOps",{"href":556,"dataGaName":557,"dataGaLocation":457},"/topics/devsecops/","devsecops",{"text":559,"config":560},"Cloud Native",{"href":561,"dataGaName":562,"dataGaLocation":457},"/topics/cloud-native/","cloud native",{"text":564,"config":565},"AI for Coding",{"href":566,"dataGaName":567,"dataGaLocation":457},"/topics/devops/ai-for-coding/","ai for coding",{"text":569,"config":570},"Agentic AI",{"href":571,"dataGaName":572,"dataGaLocation":457},"/topics/agentic-ai/","agentic ai",{"title":574,"links":575},"Solutions",[576,578,580,585,589,592,596,599,601,604,607,612],{"text":133,"config":577},{"href":128,"dataGaName":133,"dataGaLocation":457},{"text":122,"config":579},{"href":105,"dataGaName":106,"dataGaLocation":457},{"text":581,"config":582},"Agile development",{"href":583,"dataGaName":584,"dataGaLocation":457},"/solutions/agile-delivery/","agile delivery",{"text":586,"config":587},"SCM",{"href":118,"dataGaName":588,"dataGaLocation":457},"source code management",{"text":536,"config":590},{"href":111,"dataGaName":591,"dataGaLocation":457},"continuous integration & delivery",{"text":593,"config":594},"Value stream management",{"href":161,"dataGaName":595,"dataGaLocation":457},"value stream management",{"text":541,"config":597},{"href":598,"dataGaName":544,"dataGaLocation":457},"/solutions/gitops/",{"text":171,"config":600},{"href":173,"dataGaName":174,"dataGaLocation":457},{"text":602,"config":603},"Small business",{"href":178,"dataGaName":179,"dataGaLocation":457},{"text":605,"config":606},"Public sector",{"href":183,"dataGaName":184,"dataGaLocation":457},{"text":608,"config":609},"Education",{"href":610,"dataGaName":611,"dataGaLocation":457},"/solutions/education/","education",{"text":613,"config":614},"Financial services",{"href":615,"dataGaName":616,"dataGaLocation":457},"/solutions/finance/","financial services",{"title":191,"links":618},[619,621,623,625,628,630,632,634,636,638,640,642],{"text":203,"config":620},{"href":205,"dataGaName":206,"dataGaLocation":457},{"text":208,"config":622},{"href":210,"dataGaName":211,"dataGaLocation":457},{"text":213,"config":624},{"href":215,"dataGaName":216,"dataGaLocation":457},{"text":218,"config":626},{"href":220,"dataGaName":627,"dataGaLocation":457},"docs",{"text":240,"config":629},{"href":242,"dataGaName":243,"dataGaLocation":457},{"text":235,"config":631},{"href":237,"dataGaName":238,"dataGaLocation":457},{"text":245,"config":633},{"href":247,"dataGaName":248,"dataGaLocation":457},{"text":253,"config":635},{"href":255,"dataGaName":256,"dataGaLocation":457},{"text":258,"config":637},{"href":260,"dataGaName":261,"dataGaLocation":457},{"text":263,"config":639},{"href":265,"dataGaName":266,"dataGaLocation":457},{"text":268,"config":641},{"href":270,"dataGaName":271,"dataGaLocation":457},{"text":273,"config":643},{"href":275,"dataGaName":276,"dataGaLocation":457},{"title":291,"links":645},[646,648,650,652,654,656,658,662,667,669,671,673],{"text":298,"config":647},{"href":300,"dataGaName":293,"dataGaLocation":457},{"text":303,"config":649},{"href":305,"dataGaName":306,"dataGaLocation":457},{"text":311,"config":651},{"href":313,"dataGaName":314,"dataGaLocation":457},{"text":316,"config":653},{"href":318,"dataGaName":319,"dataGaLocation":457},{"text":321,"config":655},{"href":323,"dataGaName":324,"dataGaLocation":457},{"text":326,"config":657},{"href":328,"dataGaName":329,"dataGaLocation":457},{"text":659,"config":660},"Sustainability",{"href":661,"dataGaName":659,"dataGaLocation":457},"/sustainability/",{"text":663,"config":664},"Diversity, inclusion and belonging (DIB)",{"href":665,"dataGaName":666,"dataGaLocation":457},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":331,"config":668},{"href":333,"dataGaName":334,"dataGaLocation":457},{"text":341,"config":670},{"href":343,"dataGaName":344,"dataGaLocation":457},{"text":346,"config":672},{"href":348,"dataGaName":349,"dataGaLocation":457},{"text":674,"config":675},"Modern Slavery Transparency Statement",{"href":676,"dataGaName":677,"dataGaLocation":457},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":679},[680,683,686],{"text":681,"config":682},"Terms",{"href":509,"dataGaName":510,"dataGaLocation":457},{"text":684,"config":685},"Cookies",{"dataGaName":519,"dataGaLocation":457,"id":520,"isOneTrustButton":29},{"text":687,"config":688},"Privacy",{"href":514,"dataGaName":515,"dataGaLocation":457},[690],{"id":691,"title":18,"body":8,"config":692,"content":694,"description":8,"extension":27,"meta":698,"navigation":29,"path":699,"seo":700,"stem":701,"__hash__":702},"blogAuthors/en-us/blog/authors/william-arias.yml",{"template":693},"BlogAuthor",{"name":18,"config":695},{"headshot":696,"ctfId":697},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667549/Blog/Author%20Headshots/warias-headshot.jpg","warias",{},"/en-us/blog/authors/william-arias",{},"en-us/blog/authors/william-arias","1h59SugLZ7hePm0SChSE5WG0Z3uurYxGrujcXoxs4tA",[704,719,732],{"content":705,"config":717},{"title":706,"description":707,"authors":708,"heroImage":710,"date":711,"body":712,"category":9,"tags":713},"How 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/)",[714,715,716],"tutorial","product","features",{"featured":12,"template":13,"slug":718},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":720,"config":730},{"title":721,"description":722,"authors":723,"heroImage":725,"date":726,"category":9,"tags":727,"body":729},"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.",[724],"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",[261,611,728],"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":731,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":733,"config":741},{"title":734,"description":735,"authors":736,"heroImage":737,"date":738,"category":9,"tags":739,"body":740},"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.",[724],"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",[611,261,715],"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":742,"featured":29,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":744},[745,759,770],{"id":746,"categories":747,"header":749,"text":750,"button":751,"image":756},"ai-modernization",[748],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":752,"config":753},"Get your AI maturity score",{"href":754,"dataGaName":755,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":757},{"src":758},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":760,"categories":761,"header":762,"text":750,"button":763,"image":767},"devops-modernization",[715,557],"Are you just managing tools or shipping innovation?",{"text":764,"config":765},"Get your DevOps maturity score",{"href":766,"dataGaName":755,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":771,"categories":772,"header":774,"text":750,"button":775,"image":779},"security-modernization",[773],"security","Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":755,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":783,"blurb":784,"button":785,"secondaryButton":790},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":786,"config":787},"Get your free trial",{"href":788,"dataGaName":51,"dataGaLocation":789},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":791},{"href":55,"dataGaName":56,"dataGaLocation":789},1773350824680]