[{"data":1,"prerenderedAt":795},["ShallowReactive",2],{"/en-us/blog/basics-of-gitlab-ci-updated":3,"navigation-en-us":39,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Itzik Gan Baruch":691,"blog-related-posts-en-us-basics-of-gitlab-ci-updated":705,"assessment-promotions-en-us":746,"next-steps-en-us":785},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":38},"blogPosts/en-us/blog/basics-of-gitlab-ci-updated.yml","Basics Of Gitlab Ci Updated",[7],"itzik-gan-baruch",null,"engineering",{"slug":11,"featured":12,"template":13},"basics-of-gitlab-ci-updated",false,"BlogPost",{"heroImage":15,"body":16,"authors":17,"updatedDate":19,"date":20,"title":21,"tags":22,"description":25,"category":9},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662061/Blog/Hero%20Images/cicdcover.png","Let's assume that you don't know anything about [continuous integration (CI)](/topics/ci-cd/) and [why it's needed](/blog/how-to-keep-up-with-ci-cd-best-practices/) in the software development lifecycle.\n\nImagine that you work on a project, where all the code consists of two text files. Moreover, it is super critical that the concatenation of these two files contains the phrase \"Hello, world.\"\n\nIf it's not there, the whole development team won't get paid that month.\nYeah, it is that serious!\n\nThe most responsible software developer wrote a small script to run every time we are about to send our code to customers.\n\nThe code is pretty sophisticated:\n\n```bash\ncat file1.txt file2.txt | grep -q \"Hello world\"\n```\n\nThe problem is that there are 10 developers on the team, and, you know, human factors can hit hard.\n\nA week ago, a new guy forgot to run the script and three clients got broken builds. So you decided to solve the problem once and for all. Luckily, your code is already on GitLab, and you remember that there is [built-in\nCI](/solutions/continuous-integration/). Moreover, you heard at a conference that people use CI to run tests...\n\n## Let's run our first test inside CI\n\nAfter taking a couple of minutes to find and read the docs, it seems like all we need is these two lines of code in a file called `.gitlab-ci.yml`:\n\n```yaml\ntest:\n  script: cat file1.txt file2.txt | grep -q 'Hello world'\n```\n\nWe commit it, and hooray! Our build is successful:\n\n![build succeeded](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/build_succeeded.png)\n\nLet's change \"world\" to \"Africa\" in the second file and check what happens:\n\n![build failed](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/build_failed.png)\n\nThe build fails as expected!\n\nOK, we now have automated tests here! GitLab CI will run our test script every time we push new code to the source code repository in the DevOps environment.\n\n**Note:** In the above example, we assume that file1.txt and file2.txt exist in the runner host.\n\nTo run this example in GitLab, use the below code that first will create the files and then run the script.\n\n```yaml\ntest:\nbefore_script:\n      - echo \"Hello \" > | tr -d \"\\n\" | > file1.txt\n      - echo \"world\" > file2.txt\nscript: cat file1.txt file2.txt | grep -q 'Hello world'\n```\n\nFor the sake of compactness, we will assume that these files exist in the host, and will not create them in the following examples.\n\n## Make results of builds downloadable\n\nThe next business requirement is to package the code before sending it to our customers. Let's automate that part of the software development process as well!\n\nAll we need to do is define another job for CI. Let's name the job \"package\":\n\n```yaml\ntest:\n  script: cat file1.txt file2.txt | grep -q 'Hello world'\n\npackage:\n  script: cat file1.txt file2.txt | gzip > package.gz\n```\n\nWe have two tabs now:\n\n![Two tabs - generated from two jobs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/two_tabs.png)\n\nHowever, we forgot to specify that the new file is a build _artifact_, so that it could be downloaded. We can fix it by adding an `artifacts` section:\n\n```yaml\ntest:\n  script: cat file1.txt file2.txt | grep -q 'Hello world'\n\npackage:\n  script: cat file1.txt file2.txt | gzip > packaged.gz\n  artifacts:\n    paths:\n    - packaged.gz\n```\n\nChecking... it is there:\n\n![Checking the download button](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/artifacts.png)\n\nPerfect, it is! However, we have a problem to fix: The jobs are running in parallel, but we do not want to package our application if our tests fail.\n\n## Run jobs sequentially\n\nWe only want to run the 'package' job if the tests are successful. Let's define the order by specifying `stages`:\n\n```yaml\nstages:\n  - test\n  - package\n\ntest:\n  stage: test\n  script: cat file1.txt file2.txt | grep -q 'Hello world'\n\npackage:\n  stage: package\n  script: cat file1.txt file2.txt | gzip > packaged.gz\n  artifacts:\n    paths:\n    - packaged.gz\n```\n\nThat should be good!\n\nAlso, we forgot to mention, that compilation (which is represented by concatenation in our case) takes a while, so we don't want to run it twice.\nLet's define a separate step for it:\n\n```yaml\nstages:\n  - compile\n  - test\n  - package\n\ncompile:\n  stage: compile\n  script: cat file1.txt file2.txt > compiled.txt\n  artifacts:\n    paths:\n    - compiled.txt\n\ntest:\n  stage: test\n  script: cat compiled.txt | grep -q 'Hello world'\n\npackage:\n  stage: package\n  script: cat compiled.txt | gzip > packaged.gz\n  artifacts:\n    paths:\n    - packaged.gz\n```\n\nLet's take a look at our artifacts:\n\n![Unnecessary artifact](https://about.gitlab.com/images/blogimages/the-basics-of-gitlab-ci/clean-artifacts.png)\n\nHmm, we do not need that \"compile\" file to be downloadable. Let's make our temporary artifacts expire by setting `expire_in` to '20 minutes':\n\n```yaml\ncompile:\n  stage: compile\n  script: cat file1.txt file2.txt > compiled.txt\n  artifacts:\n    paths:\n    - compiled.txt\n    expire_in: 20 minutes\n```\n\nNow our config looks pretty impressive:\n\n- We have three sequential stages to compile, test, and package our\napplication.\n\n- We pass the compiled app to the next stages so that there's no need to run\ncompilation twice (so it will run faster).\n\n- We store a packaged version of our app in build artifacts for further\nusage.\n\n## Learning which Docker image to use\n\nSo far, so good. However, it appears our builds are still slow. Let's take a look at the logs.\n\n![ruby3.1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/ruby-31.png)\n\nWait, what is this? Ruby 3.1?\n\nWhy do we need Ruby at all? Oh, GitLab.com uses Docker images to [run our builds](/blog/shared-runners/), and [by default](https://docs.gitlab.com/ee/user/gitlab_com/#shared-runners) it uses the [`ruby:3.1`](https://hub.docker.com/_/ruby/) image. For sure, this image contains many packages we don't need. After a minute of Googling, we figure out that there's an image called [`alpine`](https://hub.docker.com/_/alpine/), which is an almost blank Linux image.\n\nOK, let's explicitly specify that we want to use this image by adding `image: alpine` to `.gitlab-ci.yml`.\n\nNow we're talking! We shaved nearly three minutes off:\n\n![Build speed improved](https://about.gitlab.com/images/blogimages/the-basics-of-gitlab-ci/speed.png)\n\nIt looks like there are a lot of public images around:\n\n- [mysql](https://hub.docker.com/_/mysql/)\n\n- [Python](https://hub.docker.com/_/python/)\n\n- [Java](https://hub.docker.com/_/java/)\n\n- [php](https://hub.docker.com/_/php/)\n\nSo we can just grab one for our technology stack. It makes sense to specify an image that contains no extra software because it minimizes download time.\n\n## Dealing with complex scenarios\n\nSo far, so good. However, let's suppose we have a new client who wants us to package our app into `.iso` image instead of `.gz`. Since CI does all the work, we can just add one more job to it. ISO images can be created using the [mkisofs](http://www.w3big.com/linux/linux-comm-mkisofs.html) command.\nHere's how our config should look:\n\n```yaml\nimage: alpine\nstages:\n  - compile\n  - test\n  - package\n\n# ... \"compile\" and \"test\" jobs are skipped here for the sake of compactness\n\npack-gz:\n  stage: package\n  script: cat compiled.txt | gzip > packaged.gz\n  artifacts:\n    paths:\n    - packaged.gz\n\npack-iso:\n  stage: package\n  script:\n  - mkisofs -o ./packaged.iso ./compiled.txt\n  artifacts:\n    paths:\n    - packaged.iso\n```\n\nNote that job names shouldn't necessarily be the same. In fact, if they were the same, it wouldn't be possible to make the jobs run in parallel inside the same stage of the software development process. Hence, think of same names of jobs and stages as coincidence.\n\nAnyhow, the build is failing:\n\n![Failed build because of missing mkisofs](https://about.gitlab.com/images/blogimages/the-basics-of-gitlab-ci/mkisofs.png)\n\nThe problem is that `mkisofs` is not included in the `alpine` image, so we need to install it first.\n\n## Dealing with missing software/packages\n\nAccording to the [Alpine Linux website](https://pkgs.alpinelinux.org/contents?file=mkisofs&path=&name=&branch=edge&repo=&arch=)\n`mkisofs` is a part of the `xorriso` and `cdrkit` packages. These are the magic commands that we need to run to install a package:\n\n```bash\necho \"ipv6\" >> /etc/modules  # enable networking\n\napk update                   # update packages list\n\napk add xorriso              # install package\n```\n\nFor CI, these are just like any other commands. The full list of commands we need to pass to `script` section should look like this:\n\n```yml\nscript:\n- echo \"ipv6\" >> /etc/modules\n- apk update\n- apk add xorriso\n- mkisofs -o ./packaged.iso ./compiled.txt\n```\n\nHowever, to make it semantically correct, let's put commands related to package installation in `before_script`. Note that if you use `before_script` at the top level of a configuration, then the commands will run before all jobs. In our case, we just want it to run before one specific job.\n\n## Directed Acyclic Graphs: Get faster and more flexible pipelines\n\nWe defined stages so that the package jobs will run only if the tests passed. What if we want to break the stage sequencing a bit, and run a few jobs earlier, even if they are defined in a later stage? In some cases, the traditional stage sequencing might slow down the overall pipeline execution time.\n\nImagine that our test stage includes a few more heavy tests that take a lot of time to execute, and that those tests are not necessarily related to the package jobs. In this case, it would be more efficient if the package jobs don't have to wait for those tests to complete before they can start. This is where Directed Acyclic Graphs (DAG) come in: To break the stage order for specific jobs, you can define job dependencies which will skip the regular stage order.\n\nGitLab has a special keyword `needs`, which creates dependencies between jobs, and allows jobs to run earlier, as soon as their dependent jobs complete.\n\nIn the below example, the pack jobs will start running as soon as the test job completes, so if, in future, someone adds more tests in the test stage, the package jobs will start to run before the new test jobs complete:\n\n```yaml\npack-gz:\n  stage: package\n  script: cat compiled.txt | gzip > packaged.gz\n  needs: [\"test\"]\n  artifacts:\n    paths:\n    - packaged.gz\n\npack-iso:\n  stage: package\n  before_script:\n  - echo \"ipv6\" >> /etc/modules\n  - apk update\n  - apk add xorriso\n  script:\n  - mkisofs -o ./packaged.iso ./compiled.txt\n  needs: [\"test\"]\n  artifacts:\n    paths:\n    - packaged.iso\n```\n\nOur final version of `.gitlab-ci.yml`:\n\n```yaml\nimage: alpine\n\n\nstages:\n  - compile\n  - test\n  - package\n\ncompile:\n  stage: compile\n  before_script:\n      - echo \"Hello  \" | tr -d \"\\n\" > file1.txt\n      - echo \"world\" > file2.txt\n  script: cat file1.txt file2.txt > compiled.txt\n  artifacts:\n    paths:\n    - compiled.txt\n    expire_in: 20 minutes\n\ntest:\n  stage: test\n  script: cat compiled.txt | grep -q 'Hello world'\n\npack-gz:\n  stage: package\n  script: cat compiled.txt | gzip > packaged.gz\n  needs: [\"test\"]\n  artifacts:\n    paths:\n    - packaged.gz\n\npack-iso:\n  stage: package\n  before_script:\n  - echo \"ipv6\" >> /etc/modules\n  - apk update\n  - apk add xorriso\n  script:\n  - mkisofs -o ./packaged.iso ./compiled.txt\n  needs: [\"test\"]\n  artifacts:\n    paths:\n    - packaged.iso\n```\n\nWow, it looks like we have just created a pipeline! We have three sequential stages, the jobs `pack-gz` and `pack-iso`, inside the `package` stage, are running in parallel:\n\n![Pipelines illustration](https://about.gitlab.com/images/blogimages/the-basics-of-gitlab-ci/pipeline.png)\n\n## Elevating your pipeline\n\nHere is how to elevate your pipeline.\n\n### Incorporating automated testing into CI pipelines\n\nIn DevOps, a key software development strategy rule is making really great apps with amazing user experience. So, let's add some tests in our CI pipeline to catch bugs early in the entire process. This way, we fix issues before they get big and before we move on to work on a new project.\n\nGitLab makes our lives easier by offering out-of-the-box templates for various [tests](https://docs.gitlab.com/ee/ci/testing/). All we need to do is include these templates in our CI configuration.\n\nIn this example, we will include [accessibility testing](https://docs.gitlab.com/ee/ci/testing/accessibility_testing.html):\n\n```yaml\nstages:\n  - accessibility\n\nvariables:\n  a11y_urls: \"https://about.gitlab.com https://www.example.com\"\n\ninclude:\n  - template: \"Verify/Accessibility.gitlab-ci.yml\"\n```\n\nCustomize the `a11y_urls` variable to list the URLs of the web pages to test with [Pa11y](https://pa11y.org/) and [code quality](https://docs.gitlab.com/ee/ci/testing/code_quality.html).\n\n```yaml\n    include:\n    - template: Jobs/Code-Quality.gitlab-ci.yml\n```\n\nGitLab makes it easy to see the test report right in the merge request widget area. Having the code review, pipeline status, and test results in one spot makes everything smoother and more efficient.\n\n![Accessibility report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/Screenshot_2024-04-02_at_10.56.41.png)\n\n\u003Ccenter>\u003Ci>Accessibility merge request widget\u003C/i>\u003C/center>\u003Cp>\u003C/p>\n\n![Code quality widget in\nMR](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/Screenshot_2024-04-02_at_11.00.25.png)\n\n\u003Ccenter>\u003Ci>Code quality merge request widget\u003C/i>\u003C/center>\n\n### Matrix builds\n\nIn some cases, we will need to test our app in different configurations, OS versions, programming language versions, etc. For those cases, we'll use the [parallel:matrix](https://docs.gitlab.com/ee/ci/yaml/#parallelmatrix) build to test our application across various combinations in parallel using one job configuration. In this blog, we'll test our code with different Python versions using the matrix keyword.\n\n```yaml\npython-req:\n  image: python:$VERSION\n  stage: lint\n  script:\n    - pip install -r requirements_dev.txt\n    - chmod +x ./build_cpp.sh\n    - ./build_cpp.sh\n  parallel:\n    matrix:\n      - VERSION: ['3.8', '3.9', '3.10', '3.11']   # https://hub.docker.com/_/python\n```\n\nDuring pipeline execution, this job will run in parallel four times, each time using different Python image as shown below:\n\n![Matrix job running](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/Screenshot_2024-04-02_at_11.12.48.png)\n\n### Unit testing\n\n#### What are unit tests?\n\nUnit tests are small, targeted tests that check individual components or functions of software to ensure they work as expected. They are essential for catching bugs early in the software development process and verifying that each part of the code performs correctly in isolation.\n\nExample: Imagine you're developing a calculator app. A unit test for the addition function would check if 2 + 2 equals 4. If this test passes, it confirms that the addition function is working correctly.\n\n#### Unit testing best practices\n\nIf the tests fail, the pipeline fails and users get notified. The developer needs to check the job logs, which usually contain thousands of lines, and see where the tests failed so that they can fix them. This check is time-consuming and inefficient.\n\nYou can configure your job to use [unit test reports](https://docs.gitlab.com/ee/ci/testing/unit_test_reports.html).\nGitLab displays reports on the merge request and on the pipelines details page, making it easier and faster to identify the failure without having to check the entire log.\n\n##### JUnit test report\n\nThis is a sample JUnit test report:\n\n![pipelines JUnit test report v13 10](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674097/Blog/Content%20Images/pipelines_junit_test_report_v13_10.png){:\n.shadow.center}\n\n### Integration and end-to-end testing strategies\n\nIn addition to our regular development routine, it's super important to set up a special pipeline just for integration and end-to-end testing. This checks that all the different parts of our code work together smoothly, including those [microservices](https://about.gitlab.com/topics/microservices/), UI testing, and any other components.\n\nWe run these tests [nightly](https://docs.gitlab.com/ee/ci/pipelines/schedules.html). We can set it up so that the [results automatically get sent to a special Slack channel](https://docs.gitlab.com/ee/user/project/integrations/gitlab_slack_application.html#notification-events).\nThis way, when developers come in the next day, they can quickly spot any issues. It's all about catching and fixing problems early on!\n\n### Test environment\n\nFor some of the tests, we may need a test environment to properly test our apps. With GitLab CI/CD, we can automate the deployment of testing environments and save a ton of time. Since this blog mostly focuses on CI, I won't elaborate on this, but you can refer to this section in the [GitLab documentation](https://docs.gitlab.com/ee/topics/release_your_application.html).\n\n## Implementing security scans in CI pipelines\n\nHere are the ways to implement security scans in CI pipelines.\n\n### SAST and DAST integration\n\nWe're all about keeping our code safe. If there are any vulnerabilities in our latest changes, we want to know ASAP. That's why it's a good idea to add security scans to your pipeline. They'll check the code with every commit and give you a heads up about any risks. We've put together a product tour to walk you through adding scans, including static application security testing ([SAST](https://docs.gitlab.com/ee/user/application_security/sast/))\nand dynamic application security testing ([DAST](https://docs.gitlab.com/ee/user/application_security/dast/)), to your CI pipeline.\n\n__Click__ the image below to start the tour.\n\n[![Scans product tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/Screenshot_2024-04-14_at_13.44.42.png)](https://gitlab.navattic.com/gitlab-scans)\n\nPlus, with AI, we can dig even deeper into vulnerabilities and get suggestions on how to fix them. Check out this demo for more info.\n\n__Click__ the image below to start the tour.\n\n[![product tour explain vulnerability\n](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674096/Blog/Content%20Images/Screenshot_2024-04-14_at_13.50.24.png)](https://tech-marketing.gitlab.io/static-demos/pt-explain-vulnerability.html)\n\n## Recap\n\nThere's much more to cover but let's stop here for now. All examples were made intentionally trivial so that you could learn the concepts of GitLab CI without being distracted by an unfamiliar technology stack. Let's wrap up what we have learned:\n\n1. To delegate some work to GitLab CI you should define one or more\n[jobs](https://docs.gitlab.com/ee/ci/jobs/) in `.gitlab-ci.yml`.\n\n2. Jobs should have names and it's your responsibility to come up with good\nones.\n\n3. Every job contains a set of rules and instructions for GitLab CI, defined\nby [special keywords](#keywords).\n\n4. Jobs can run sequentially, in parallel, or out of order using\n[DAG](https://docs.gitlab.com/ee/ci/directed_acyclic_graph/index.html).\n\n5. You can pass files between jobs and store them in build artifacts so that\nthey can be downloaded from the interface.\n\n6. Add [tests and security\nscans](https://docs.gitlab.com/ee/development/integrations/secure.html) to the CI pipeline to ensure the quality and security of your app.\n\nBelow are more formal descriptions of the terms and keywords we used, as well as links to the relevant documentation.\n\n### Keyword descriptions and documentation\n\n| Keyword/term | Description |\n| --- | --- |\n| [.gitlab-ci.yml](https://docs.gitlab.com/ee/ci/yaml/) | File containing all definitions of how your project should be built |\n| [script](https://docs.gitlab.com/ee/ci/yaml/#script) | Defines a shell script to be executed |\n| [before_script](https://docs.gitlab.com/ee/ci/yaml/#before_script) | Used to define the command that should be run before (all) jobs |\n| [image](https://docs.gitlab.com/ee/ci/docker/using_docker_images.html#what-is-image) | Defines what Docker image to use |\n| [stages](https://docs.gitlab.com/ee/ci/yaml/#stages) | Defines a pipeline stage (default: `test`) |\n| [artifacts](https://docs.gitlab.com/ee/ci/yaml/#artifacts) | Defines a list of build artifacts |\n| [artifacts:expire_in](https://docs.gitlab.com/ee/ci/yaml/#artifactsexpire_in) | Used to delete uploaded artifacts after the specified time |\n| [needs](https://docs.gitlab.com/ee/ci/yaml/#needs) | Used to define dependencies between jobs and allows to run jobs out of order |\n| [pipelines](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/) | A pipeline is a group of builds that get executed in stages (batches) |\n\n## More on CI/CD\n\n- [GitLab’s guide to CI/CD for beginners](/blog/beginner-guide-ci-cd/)\n\n- [Get faster and more flexible pipelines with a Directed Acyclic\nGraph](/blog/directed-acyclic-graph/)\n\n- [Decrease build time with custom Docker\nimage](http://beenje.github.io/blog/posts/gitlab-ci-and-conda/)\n\n- [Introducing the GitLab CI/CD Catalog\nBeta](https://about.gitlab.com/blog/introducing-the-gitlab-ci-cd-catalog-beta/)\n\n## FAQ\n\n### How do you choose between running CI jobs sequentially vs. in parallel?\n\nConsiderations for choosing between running CI jobs sequentially or in parallel include job dependencies, resource availability, execution times, potential interference, test suite structure, and cost considerations. For example, if you have a build job that must finish before a deployment job can start, you would run these jobs sequentially to ensure the correct order of execution. On the other hand, tasks such as unit testing and integration testing can typically run in parallel since they are independent and don't rely on each other's completion.\n\n### What are directed Acyclic Graphs in GitLab CI, and how do they improve\npipeline flexibility?\n\nA Directed Acyclic Graph (DAG) in GitLab CI breaks the linear order of pipeline stages. It lets you set dependencies between jobs, so jobs in later stages start as soon as earlier stage jobs finish. This reduces overall pipeline execution time, improves efficiency, and lets some jobs complete earlier than in a regular order.\n\n### What is the importance of choosing the right Docker image for CI jobs in\nGitLab?\n\nGitLab utilizes Docker images to execute jobs. The default image is ruby:3.1. Depending on your job's requirements, it's crucial to choose the appropriate image. Note that jobs first download the specified Docker image, and if the image contains additional packages beyond what's necessary, it will increase download and execution times. Therefore, it's important to ensure that the chosen image contains only the packages essential for your job to avoid unnecessary delays in execution.\n\n## Next steps\n\nAs a next step and to further modernize your software development practice, check out the [GitLab CI/CD\nCatalog](https://docs.gitlab.com/ee/architecture/blueprints/ci_pipeline_components/)\nto learn how to standardize and reuse CI/CD components.",[18],"Itzik Gan Baruch","2024-04-24","2020-12-10","The basics of CI: How to run jobs sequentially, in parallel, or out of order",[23,24],"CI","tutorial","New to continuous integration? 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But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[711],"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",[261,613,715],"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":718,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":720,"config":729},{"title":721,"description":722,"authors":723,"heroImage":724,"date":725,"category":9,"tags":726,"body":728},"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.",[711],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[613,261,727],"product","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":730,"featured":28,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":732,"config":744},{"category":9,"tags":733,"body":735,"date":736,"updatedDate":737,"heroImage":738,"authors":739,"title":742,"description":743},[24,734,108],"git","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n    subgraph Prerequisites\n        direction TB\n        A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n        A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n        B --> I[\"Users enablement and\u003Cbr/>change management\"]\n    end\n    \n    subgraph MigrationPhase[\"Migration phase\"]\n        direction TB\n        C[\"Migrate source code\"]\n        C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n        D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n    end\n    \n    subgraph PostMigration[\"Post-migration steps\"]\n        direction TB\n        F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n        F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n        G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n    end\n    \n    Prerequisites --> MigrationPhase\n    MigrationPhase --> PostMigration\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style I fill:#FC6D26\n    style C fill:#8C929D\n    style D fill:#8C929D\n    style E fill:#8C929D\n    style F fill:#FFA500\n    style G fill:#FFA500\n    style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n    subgraph GitLab\n        direction TB\n        A[\"Top-level Group\"]\n        B[\"Subgroup (optional)\"]\n        C[\"Projects\"]\n        A --> B\n        A --> C\n        B --> C\n    end\n\n    subgraph AzureDevOps[\"Azure DevOps\"]\n        direction TB\n        F[\"Organizations\"]\n        G[\"Projects\"]\n        H[\"Repositories\"]\n        F --> G\n        G --> H\n    end\n\n    style A fill:#FC6D26\n    style B fill:#FC6D26\n    style C fill:#FC6D26\n    style F fill:#8C929D\n    style G fill:#8C929D\n    style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n  - main\n\nvariables:\n  imageName: myapp\n\nstages:\n  - stage: Build\n    jobs:\n      - job: Build\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Build Docker image\n            inputs:\n              command: build\n              repository: $(imageName)\n              Dockerfile: '**/Dockerfile'\n              tags: |\n                $(Build.BuildId)\n\n  - stage: Test\n    jobs:\n      - job: Test\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          # Example: run tests inside the container\n          - script: |\n              docker run --rm $(imageName):$(Build.BuildId) npm test\n            displayName: Run tests\n\n  - stage: Push\n    jobs:\n      - job: Push\n        pool:\n          vmImage: 'ubuntu-latest'\n        steps:\n          - checkout: self\n\n          - task: Docker@2\n            displayName: Login to ACR\n            inputs:\n              command: login\n              containerRegistry: '\u003Cyour-acr-service-connection>'\n\n          - task: Docker@2\n            displayName: Push image to ACR\n            inputs:\n              command: push\n              repository: $(imageName)\n              tags: |\n                $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n  imageName: myapp\n\nstages:\n  - build\n  - test\n  - push\n\nbuild:\n  stage: build\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n  only:\n    - main\n\ntest:\n  stage: test\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n  only:\n    - main\n\npush:\n  stage: push\n  image: docker:latest\n  services:\n    - docker:dind\n  before_script:\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n    - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n  only:\n    - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n  - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n  - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n  - Azure DevOps PAT with required scopes.\n\n  - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n  - Repos only: use GitLab's built-in import (Repo by URL)\n\n  - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n  - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n  - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n  - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n  - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n  - Enforce change freezes in ADO during each wave.\n\n  - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab                                                           | Azure DevOps                                 | Similarities & Key Differences                                                                                                                                          |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group                                                            | Organization                                 | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects.                                                   |\n| Group or Subgroup                                                | Project                                      | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects.                                                |\n| Project (includes a Git repo)                                    | Repository (inside a Project)                | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project.                                                  |\n| Merge Request (MR)                                               | Pull Request (PR)                            | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners.                                                                     |\n| Protected Branches, MR Approval Rules, Status Checks             | Branch Policies                              | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks.                                                                    |\n| GitLab CI/CD                                                     | Azure Pipelines                              | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml.                                                               |\n| .gitlab-ci.yml                                                   | azure-pipelines.yml                          | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers.                                                                   |\n| Runners (shared/specific)                                        | Agents / Agent Pools                         | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs.                                                         |\n| CI/CD Variables (project/group/instance), Protected/Masked       | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags.                                                                          |\n| Integrations, CI/CD Variables, Deploy Keys                       | Service Connections                          | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available.                                                                 |\n| Environments & Deployments (protected envs)                      | Environments (with approvals)                | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab.                                                                                 |\n| Releases (tag + notes)                                           | Releases (classic or pipelines)              | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately.                                                                               |\n| Job Artifacts                                                    | Pipeline Artifacts                           | Persist job outputs. Retention/expiry configured per job or project.                                                                                                  |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.)           | Azure Artifacts (NuGet/npm/Maven, etc.)      | Package hosting. Auth/namespace differ; migrate per package type.                                                                                                     |\n| GitLab Container Registry                                        | Azure Container Registry (ACR) or others     | OCI images. GitLab provides per-project/group registries.                                                                                                             |\n| Issue Boards                                                     | Boards                                       | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group.                                                                         |\n| Issues (types/labels), Epics                                     | Work Items (User Story/Bug/Task)             | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level.                                                                              |\n| Epics, Parent/Child Issues                                       | Epics/Features                               | Hierarchy of work. Schema differs; use epics + issue relationships.                                                                                                   |\n| Milestones and Iterations                                        | Iteration Paths                              | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group.                                                                                       |\n| Labels (scoped labels)                                           | Area Paths                                   | Categorization/ownership. Replace hierarchical areas with scoped labels.                                                                                              |\n| Project/Group Wiki                                               | Project Wiki                                 | Markdown wiki. Backed by repos in both; layout/auth differ slightly.                                                                                                  |\n| Test reports via CI, Requirements/Test Management, integrations  | Test Plans/Cases/Runs                        | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements.                                                                     |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions         | Control read/write/admin. Models differ; leverage group inheritance and protected resources.                                                                          |\n| Webhooks                                                         | Service Hooks                                | Event-driven integrations. Event names/payloads differ; reconfigure endpoints.                                                                                        |\n| Advanced Search                                                  | Code Search                                  | Full-text repo search. Self-managed GitLab may need Elasticsearch/OpenSearch for advanced features.                                                                   |\n","2025-12-03","2026-01-16","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658924/Blog/Hero%20Images/securitylifecycle-light.png",[740,741],"Evgeny Rudinsky","Michael Leopard","Guide: Migrate from Azure DevOps to GitLab","Learn how to carry out the full migration from Azure DevOps to GitLab using GitLab Professional Services migration tools — from planning and execution to post-migration follow-up tasks.",{"featured":28,"template":13,"slug":745},"migration-from-azure-devops-to-gitlab",{"promotions":747},[748,762,773],{"id":749,"categories":750,"header":752,"text":753,"button":754,"image":759},"ai-modernization",[751],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":755,"config":756},"Get your AI maturity score",{"href":757,"dataGaName":758,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":760},{"src":761},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":763,"categories":764,"header":765,"text":753,"button":766,"image":770},"devops-modernization",[727,559],"Are you just managing tools or shipping innovation?",{"text":767,"config":768},"Get your DevOps maturity score",{"href":769,"dataGaName":758,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":774,"categories":775,"header":777,"text":753,"button":778,"image":782},"security-modernization",[776],"security","Are you trading speed for security?",{"text":779,"config":780},"Get your security maturity score",{"href":781,"dataGaName":758,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":783},{"src":784},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":786,"blurb":787,"button":788,"secondaryButton":793},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":789,"config":790},"Get your free trial",{"href":791,"dataGaName":50,"dataGaLocation":792},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":794},{"href":54,"dataGaName":55,"dataGaLocation":792},1772652062473]