[{"data":1,"prerenderedAt":821},["ShallowReactive",2],{"/en-us/blog/custom-actions-rasa-gitlab-devops":3,"navigation-en-us":43,"banner-en-us":454,"footer-en-us":464,"blog-post-authors-en-us-William Arias":702,"blog-related-posts-en-us-custom-actions-rasa-gitlab-devops":716,"blog-promotions-en-us":759,"next-steps-en-us":811},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":17,"extension":25,"externalUrl":26,"featured":14,"heroImage":19,"isFeatured":14,"meta":27,"navigation":28,"path":29,"publishedDate":20,"rawbody":30,"seo":31,"slug":13,"stem":36,"tagSlugs":37,"tags":41,"template":15,"updatedDate":26,"__hash__":42},"blogPosts/en-us/blog/custom-actions-rasa-gitlab-devops.yml","Create and Deploy Custom Actions Containers to Rasa X using Gitlab DevOps Platform",[7],"william-arias",[9],"William Arias","**This blog post was a collaboration between William Arias, from Gitlab, and Vincent D. Warmerdam, from Rasa. You can find the same blog post on [Rasa's blog](https://blog.rasa.com/create-and-deploy-custom-actions-containers-to-rasa-x-using-gitlab-devops-platform/)**.\n\n## Create and Deploy Custom Actions Containers to Rasa X using Gitlab DevOps Platform\nVirtual assistants do more than just carry on conversations. They can send emails, make updates to a calendar, or call an API endpoint. Essentially, they can do actions that add significant value and convenience to the user experience.\nIn assistants built with Rasa*, this type of functionality is executed by custom code called custom actions. As with any code you run in production, you’ll need to think about how you want to deploy updates to custom actions. In this blog post, we’ll show you how to set up GitLab to deploy custom action Docker containers to your Kubernetes cluster. If we follow [good DevOps practices](/stages-devops-lifecycle/) we can greatly speed up the development and quality of our  virtual assistants.\n* Rasa Open Source is a machine learning framework for building text and voice-based virtual assistants. It provides infrastructure for understanding messages, holding conversations, and connecting to many messaging channels and APIs. Rasa X is a toolset that runs on top of Rasa Open Source, extending its capabilities. Rasa X includes key features for sharing the assistant with test users, reviewing and annotating conversation data, and deploying the assistant. [Learn more about Rasa.](https://rasa.com/docs/)\n\n## Deployment high-level overview\nThe typical workflow for deploying a new version of custom actions is outlined below.\n![actions-process](https://about.gitlab.com/images/blogimages/actions-process.png)\n\nEvery change to your custom actions code will require a new container image to be built and pulled by Rasa X. Gitlab CI/CD can save you from doing a lot of manual work and automate steps like the ones described in the workflow above. Let's see how to do it.\n\n## Using Rasa with Gitlab DevOps Platform\nLet's create a pipeline that will automate manual steps.\n\n---\n**NOTE**\nThis article assumes you have your [Gitlab Project](https://gitlab.com/warias/gl-commit-2020) with your customs Actions Code created along with a [Google Kubernetes Cluster](https://cloud.google.com/kubernetes-engine/docs/quickstart).\n\n---\n\nIf you are a Gitlab user you are probably familiar with .gitlab-ci.yml file and its CI/CD capabilities. Every time you commit a change to your customs actions code you want Gitlab to run a script that will build and update your docker containers.\n![actions-process-2](https://about.gitlab.com/images/blogimages/process2.png)\n\nLet's breakdown the CI/CD pipeline by describing the gitlab-ci.yml file so you can use it and customize it to your needs\n## Variables\nWe make use of environment variables created in Gitlab at the moment of running the Jobs to define our actions Docker image\n\n```yaml\nvariables:\n    ACTIONS_CONTAINER_IMAGE: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_SLUG\n    TAG: $CI_COMMIT_SHA\n    K8S_SECRET: secret-gitlab-registry\n\n```\n\nThe snippet above does the following:\n- It defines the name of the Docker Image for custom actions using environment variables ```$CI_REGISTRY_IMAGE/$CI_COMMIT_REF_SLUG.``` This will make the name of the Docker image different for every commit\n- It creates a secret used to pull the Rasa Action Image from the Gitlab Private Registry to the Google Kubernetes Cluster.\n\n## Stages\nWe have two main stages in our pipeline, build and deploy:\n```yaml\nstages:\n  - build\n  - deploy\n\n```\nEvery time there is a new commit with changes to our custom actions code, or when we decide to run the CI/CD Pipeline it will:\n- Build: Here, we automate the building of the Docker image using the variables defined above, and the Dockerfile. We also tag the image and push it to the GitLab container registry.\n- Deploy: Here we log-in to Kubernetes Engine on Google Cloud and deploy the newly created Actions image to Rasa X.\nLet's see it in more detail:\n\n**Build**:\n```yaml\nbuild-actions-image:\n image: docker:19.03.1\n services:\n   - docker:dind\n stage: build\n script:\n   - docker login -u ```$CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY```\n   - docker build -t $ACTIONS_CONTAINER_IMAGE:$TAG -f Dockerfile .\n   - docker push $ACTIONS_CONTAINER_IMAGE:$TAG\n\n```\nThe job build-actions-image executed on the build stage takes advantage of the CI/CD variables that are part of the environment where the pipelines run. It automates the usage of Docker commands to build the Actions image by reading its corresponding Dockerfile. The output of this stage is a new Custom Actions image per every commit with code changes.\n\n**Deploy**:\n```text\ndeploy-custom-action-x:\n  stage: deploy\n  image: crileroro/gcloud-kubectl-helm\n  variables:\n    GCP_PROJECT: gke-project-302411\n    GCP_REGION: europe-west1\n    CLUSTER_NAME: gke-python-demo\n    NAMESPACE_RASA: rasa-environment\n  before_script:\n    - gcloud auth activate-service-account --key-file $SERVICE_ACCOUNT_GCP\n    - gcloud config set project $GCP_PROJECT\n    - gcloud config set compute/region $GCP_REGION\n    - gcloud container clusters get-credentials $CLUSTER_NAME\n  script:\n    - kubectl create ns $NAMESPACE_RASA --dry-run=client -o yaml | kubectl apply -f -\n    - kubectl create secret docker-registry $K8S_SECRET\n              --docker-server=$CI_REGISTRY\n              --docker-username=$CI_DEPLOY_USER\n              --docker-password=$CI_DEPLOY_PASSWORD\n              --namespace $NAMESPACE_RASA\n              -o yaml --dry-run=client | kubectl apply -f -\n    - helm repo add rasa-x https://rasahq.github.io/rasa-x-helm\n    - helm upgrade -i --reuse-values\n                      --namespace $NAMESPACE_RASA\n                      --set app.name=$ACTIONS_CONTAINER_IMAGE\n                      --set app.tag=$TAG\n                      --set images.imagePullSecrets[0].name=$K8S_SECRET rasa-x rasa-x/rasa-x\n\n```\n\nNotice the variables in ```before_script```, these ones are needed to authenticate to GCP where we have our Kubernetes cluster. This step is optional and could be skipped in cases where you have [Gitlab pre-integrated](https://docs.gitlab.com/user/project/clusters/add_remove_clusters/) with your Kubernetes cluster running on Google Cloud.\n\nThe main and most interesting part of the script is:\n```text\nscript:\n    - kubectl create ns $NAMESPACE_RASA --dry-run=client -o yaml | kubectl apply -f -\n    - kubectl create secret docker-registry $K8S_SECRET\n              --docker-server=$CI_REGISTRY\n              --docker-username=$CI_DEPLOY_USER\n              --docker-password=$CI_DEPLOY_PASSWORD\n              --namespace $NAMESPACE_RASA\n              -o yaml --dry-run=client | kubectl apply -f -\n    - helm repo add rasa-x https://rasahq.github.io/rasa-x-helm\n    - helm upgrade -i --reuse-values\n                      --namespace $NAMESPACE_RASA\n                      --set app.name=$ACTIONS_CONTAINER_IMAGE\n                      --set app.tag=$TAG\n                      --set images.imagePullSecrets[0].name=$K8S_SECRET rasa-x rasa-x/rasa-x\n\n```\n\nWe start by creating the *namespace* for our custom actions code, and if it already exists, then we proceed to apply Kubernetes commands using kubectl and helm.\n```shell\nhelm repo add rasa-x https://rasahq.github.io/rasa-x-helm\n    - helm upgrade -i --reuse-values\n                      --namespace $NAMESPACE_RASA\n                      --set app.name=$ACTIONS_CONTAINER_IMAGE\n                      --set app.tag=$TAG\n                      --set images.imagePullSecrets[0].name=$K8S_SECRET rasa-x rasa-x/rasa-x\n\n```\nThe snippet above adds a rasa-x Helm chart and upgrades or changes the values corresponding to the new **Custom Action Image** by assigning to it the ```$ACTIONS_CONTAINER_IMAGE``` created in the build stage.\nNote that the pipeline described above focuses only on creating and deploying the ACTIONS_CONTAINER_IMAGE. It could be extended by adding more stages, for example, code quality, security testing, and unit testing among others.\n\n## Summary\nUsing the GitLab DevOps Platform together with Rasa X can make it easier for stakeholders to deliver a virtual assistant by automating potentially time-consuming, error-prone steps. In this case, we’ve shown how you can build Rasa custom action servers and deploy them to Kubernetes.\nPushing new custom action containers to Kubernetes only scratches the surface of what you can automate with GitLab. You could also add steps for code quality, security audits and unit tests. The main goal is to automate the manual parts of deployment so that you can focus on what is important. In the case of Rasa X, that means that more time can be spent learning from your users and making a better assistant in the process.\n\nDo you want to learn more? Watch this video of Gitlab DevOps Platform and Rasa [Deploy your Rasa Chatbots like a boss with DevOps](https://youtu.be/ko9-zPDuhQo)\n\nHappy hacking!\n\nCover image by [Eric Krull](https://unsplash.com/@ekrull?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com)\n","devsecops",{"slug":13,"featured":14,"template":15},"custom-actions-rasa-gitlab-devops",false,"BlogPost",{"title":5,"description":17,"authors":18,"heroImage":19,"date":20,"body":10,"category":11,"tags":21},"Using the GitLab DevOps Platform together with Rasa X can make it easier for stakeholders to deliver a virtual assistant by automating potentially time-consuming, error-prone steps. In this case, we’ve shown how you can build Rasa custom action servers and deploy them to Kubernetes.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668410/Blog/Hero%20Images/vablog.jpg","2021-04-06",[22,23,24],"CI","cloud native","DevOps","yml",null,{},true,"/en-us/blog/custom-actions-rasa-gitlab-devops","seo:\n  title: >-\n    Creating custom action containers for Rasa X with GitLab\n  description: >-\n    Using the GitLab DevOps Platform together with Rasa X can make it easier for\n    stakeholders to deliver a virtual assistant by automating potentially\n    time-consuming, error-prone steps. In this case, we’ve shown how you can\n    build Rasa custom action servers and deploy them to Kubernetes.\n  ogTitle: >-\n    Creating custom action containers for Rasa X with GitLab\n  ogDescription: >-\n    Using the GitLab DevOps Platform together with Rasa X can make it easier for\n    stakeholders to deliver a virtual assistant by automating potentially\n    time-consuming, error-prone steps. In this case, we’ve shown how you can\n    build Rasa custom action servers and deploy them to Kubernetes.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668410/Blog/Hero%20Images/vablog.jpg\n  ogUrl: https://about.gitlab.com/blog/custom-actions-rasa-gitlab-devops\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: https://about.gitlab.com/blog/custom-actions-rasa-gitlab-devops\ncontent:\n  title: >-\n    Create and Deploy Custom Actions Containers to Rasa X using Gitlab DevOps\n    Platform\n  description: >-\n    Using the GitLab DevOps Platform together with Rasa X can make it easier for\n    stakeholders to deliver a virtual assistant by automating potentially\n    time-consuming, error-prone steps. In this case, we’ve shown how you can\n    build Rasa custom action servers and deploy them to Kubernetes.\n  authors:\n    - William Arias\n  heroImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668410/Blog/Hero%20Images/vablog.jpg\n  date: '2021-04-06'\n  body: >\n    **This blog post was a collaboration between William Arias, from Gitlab, and\n    Vincent D. Warmerdam, from Rasa. You can find the same blog post on [Rasa's\n    blog](https://blog.rasa.com/create-and-deploy-custom-actions-containers-to-rasa-x-using-gitlab-devops-platform/)**.\n\n\n    ## Create and Deploy Custom Actions Containers to Rasa X using Gitlab DevOps\n    Platform\n\n    Virtual assistants do more than just carry on conversations. They can send\n    emails, make updates to a calendar, or call an API endpoint. Essentially,\n    they can do actions that add significant value and convenience to the user\n    experience.\n\n    In assistants built with Rasa*, this type of functionality is executed by\n    custom code called custom actions. As with any code you run in production,\n    you’ll need to think about how you want to deploy updates to custom actions.\n    In this blog post, we’ll show you how to set up GitLab to deploy custom\n    action Docker containers to your Kubernetes cluster. If we follow [good\n    DevOps practices](/stages-devops-lifecycle/) we can greatly speed up the\n    development and quality of our  virtual assistants.\n\n    * Rasa Open Source is a machine learning framework for building text and\n    voice-based virtual assistants. It provides infrastructure for understanding\n    messages, holding conversations, and connecting to many messaging channels\n    and APIs. Rasa X is a toolset that runs on top of Rasa Open Source,\n    extending its capabilities. Rasa X includes key features for sharing the\n    assistant with test users, reviewing and annotating conversation data, and\n    deploying the assistant. [Learn more about Rasa.](https://rasa.com/docs/)\n\n\n    ## Deployment high-level overview\n\n    The typical workflow for deploying a new version of custom actions is\n    outlined below.\n\n    ![actions-process](https://about.gitlab.com/images/blogimages/actions-process.png)\n\n\n    Every change to your custom actions code will require a new container image\n    to be built and pulled by Rasa X. Gitlab CI/CD can save you from doing a lot\n    of manual work and automate steps like the ones described in the workflow\n    above. Let's see how to do it.\n\n\n    ## Using Rasa with Gitlab DevOps Platform\n\n    Let's create a pipeline that will automate manual steps.\n\n\n    ---\n\n    **NOTE**\n\n    This article assumes you have your [Gitlab\n    Project](https://gitlab.com/warias/gl-commit-2020) with your customs Actions\n    Code created along with a [Google Kubernetes\n    Cluster](https://cloud.google.com/kubernetes-engine/docs/quickstart).\n\n\n    ---\n\n\n    If you are a Gitlab user you are probably familiar with .gitlab-ci.yml file\n    and its CI/CD capabilities. Every time you commit a change to your customs\n    actions code you want Gitlab to run a script that will build and update your\n    docker containers.\n\n    ![actions-process-2](https://about.gitlab.com/images/blogimages/process2.png)\n\n\n    Let's breakdown the CI/CD pipeline by describing the gitlab-ci.yml file so\n    you can use it and customize it to your needs\n\n    ## Variables\n\n    We make use of environment variables created in Gitlab at the moment of\n    running the Jobs to define our actions Docker image\n\n\n    ```yaml\n\n    variables:\n        ACTIONS_CONTAINER_IMAGE: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_SLUG\n        TAG: $CI_COMMIT_SHA\n        K8S_SECRET: secret-gitlab-registry\n\n    ```\n\n\n    The snippet above does the following:\n\n    - It defines the name of the Docker Image for custom actions using\n    environment variables ```$CI_REGISTRY_IMAGE/$CI_COMMIT_REF_SLUG.``` This\n    will make the name of the Docker image different for every commit\n\n    - It creates a secret used to pull the Rasa Action Image from the Gitlab\n    Private Registry to the Google Kubernetes Cluster.\n\n\n    ## Stages\n\n    We have two main stages in our pipeline, build and deploy:\n\n    ```yaml\n\n    stages:\n      - build\n      - deploy\n\n    ```\n\n    Every time there is a new commit with changes to our custom actions code, or\n    when we decide to run the CI/CD Pipeline it will:\n\n    - Build: Here, we automate the building of the Docker image using the\n    variables defined above, and the Dockerfile. We also tag the image and push\n    it to the GitLab container registry.\n\n    - Deploy: Here we log-in to Kubernetes Engine on Google Cloud and deploy the\n    newly created Actions image to Rasa X.\n\n    Let's see it in more detail:\n\n\n    **Build**:\n\n    ```yaml\n\n    build-actions-image:\n     image: docker:19.03.1\n     services:\n       - docker:dind\n     stage: build\n     script:\n       - docker login -u ```$CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY```\n       - docker build -t $ACTIONS_CONTAINER_IMAGE:$TAG -f Dockerfile .\n       - docker push $ACTIONS_CONTAINER_IMAGE:$TAG\n\n    ```\n\n    The job build-actions-image executed on the build stage takes advantage of\n    the CI/CD variables that are part of the environment where the pipelines\n    run. It automates the usage of Docker commands to build the Actions image by\n    reading its corresponding Dockerfile. The output of this stage is a new\n    Custom Actions image per every commit with code changes.\n\n\n    **Deploy**:\n\n    ```text\n\n    deploy-custom-action-x:\n      stage: deploy\n      image: crileroro/gcloud-kubectl-helm\n      variables:\n        GCP_PROJECT: gke-project-302411\n        GCP_REGION: europe-west1\n        CLUSTER_NAME: gke-python-demo\n        NAMESPACE_RASA: rasa-environment\n      before_script:\n        - gcloud auth activate-service-account --key-file $SERVICE_ACCOUNT_GCP\n        - gcloud config set project $GCP_PROJECT\n        - gcloud config set compute/region $GCP_REGION\n        - gcloud container clusters get-credentials $CLUSTER_NAME\n      script:\n        - kubectl create ns $NAMESPACE_RASA --dry-run=client -o yaml | kubectl apply -f -\n        - kubectl create secret docker-registry $K8S_SECRET\n                  --docker-server=$CI_REGISTRY\n                  --docker-username=$CI_DEPLOY_USER\n                  --docker-password=$CI_DEPLOY_PASSWORD\n                  --namespace $NAMESPACE_RASA\n                  -o yaml --dry-run=client | kubectl apply -f -\n        - helm repo add rasa-x https://rasahq.github.io/rasa-x-helm\n        - helm upgrade -i --reuse-values\n                          --namespace $NAMESPACE_RASA\n                          --set app.name=$ACTIONS_CONTAINER_IMAGE\n                          --set app.tag=$TAG\n                          --set images.imagePullSecrets[0].name=$K8S_SECRET rasa-x rasa-x/rasa-x\n\n    ```\n\n\n    Notice the variables in ```before_script```, these ones are needed to\n    authenticate to GCP where we have our Kubernetes cluster. This step is\n    optional and could be skipped in cases where you have [Gitlab\n    pre-integrated](https://docs.gitlab.com/user/project/clusters/add_remove_clusters/)\n    with your Kubernetes cluster running on Google Cloud.\n\n\n    The main and most interesting part of the script is:\n\n    ```text\n\n    script:\n        - kubectl create ns $NAMESPACE_RASA --dry-run=client -o yaml | kubectl apply -f -\n        - kubectl create secret docker-registry $K8S_SECRET\n                  --docker-server=$CI_REGISTRY\n                  --docker-username=$CI_DEPLOY_USER\n                  --docker-password=$CI_DEPLOY_PASSWORD\n                  --namespace $NAMESPACE_RASA\n                  -o yaml --dry-run=client | kubectl apply -f -\n        - helm repo add rasa-x https://rasahq.github.io/rasa-x-helm\n        - helm upgrade -i --reuse-values\n                          --namespace $NAMESPACE_RASA\n                          --set app.name=$ACTIONS_CONTAINER_IMAGE\n                          --set app.tag=$TAG\n                          --set images.imagePullSecrets[0].name=$K8S_SECRET rasa-x rasa-x/rasa-x\n\n    ```\n\n\n    We start by creating the *namespace* for our custom actions code, and if it\n    already exists, then we proceed to apply Kubernetes commands using kubectl\n    and helm.\n\n    ```shell\n\n    helm repo add rasa-x https://rasahq.github.io/rasa-x-helm\n        - helm upgrade -i --reuse-values\n                          --namespace $NAMESPACE_RASA\n                          --set app.name=$ACTIONS_CONTAINER_IMAGE\n                          --set app.tag=$TAG\n                          --set images.imagePullSecrets[0].name=$K8S_SECRET rasa-x rasa-x/rasa-x\n\n    ```\n\n    The snippet above adds a rasa-x Helm chart and upgrades or changes the\n    values corresponding to the new **Custom Action Image** by assigning to it\n    the ```$ACTIONS_CONTAINER_IMAGE``` created in the build stage.\n\n    Note that the pipeline described above focuses only on creating and\n    deploying the ACTIONS_CONTAINER_IMAGE. It could be extended by adding more\n    stages, for example, code quality, security testing, and unit testing among\n    others.\n\n\n    ## Summary\n\n    Using the GitLab DevOps Platform together with Rasa X can make it easier for\n    stakeholders to deliver a virtual assistant by automating potentially\n    time-consuming, error-prone steps. In this case, we’ve shown how you can\n    build Rasa custom action servers and deploy them to Kubernetes.\n\n    Pushing new custom action containers to Kubernetes only scratches the\n    surface of what you can automate with GitLab. You could also add steps for\n    code quality, security audits and unit tests. The main goal is to automate\n    the manual parts of deployment so that you can focus on what is important.\n    In the case of Rasa X, that means that more time can be spent learning from\n    your users and making a better assistant in the process.\n\n\n    Do you want to learn more? Watch this video of Gitlab DevOps Platform and\n    Rasa [Deploy your Rasa Chatbots like a boss with\n    DevOps](https://youtu.be/ko9-zPDuhQo)\n\n\n    Happy hacking!\n\n\n    Cover image by [Eric\n    Krull](https://unsplash.com/@ekrull?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\n    on [Unsplash](https://unsplash.com)\n\n  category: devsecops\n  tags:\n    - CI\n    - cloud native\n    - DevOps\nconfig:\n  slug: custom-actions-rasa-gitlab-devops\n  featured: false\n  template: BlogPost\n",{"title":32,"description":17,"ogTitle":32,"ogDescription":17,"noIndex":14,"ogImage":19,"ogUrl":33,"ogSiteName":34,"ogType":35,"canonicalUrls":33},"Creating custom action containers for Rasa X with 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software development the easy way using GitLab","Learn how University of Washington lecturer Stephen G. Dame uses GitLab for Education to manage student assignments, distribute course materials, and provide inline code feedback at scale.\n",[722],"Rod Burns","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659537/Blog/Hero%20Images/display-article-image-0679-1800x945-fy26.png","2026-04-29","For instructors teaching software development, one of the biggest logistical challenges is assignment distribution and feedback at scale. How do you give large groups of students access to course materials, keep solution code private, and still deliver meaningful, contextual feedback without lots of administrative overhead?\n\nThe **[GitLab for Education program](https://about.gitlab.com/solutions/education/)** provides qualifying institutions with free access to **GitLab Ultimate**, enabling instructors to build professional-grade workflows that mirror real-world software development environments. In this article, you'll learn how Stephen G. Dame, a lecturer in the Computing and Software Systems department at the University of Washington, Bothell, uses simple workflows in GitLab to manage everything from course materials to student feedback across multiple classes.\n\n## From aerospace to academia: Bringing GitLab to the classroom\n\nDame came to academia with years of experience as a chief software engineer at Boeing Commercial Airplanes, where GitLab was used for aerospace projects. As an adjunct professor, he became an early advocate for GitLab within the university, joining the GitLab for Education program to access the full feature set needed to run structured, scalable course workflows.\n\n> **\"GitLab provides the greatest way to organize multiple classes, student assignments, lectures, and code samples through the use of Groups and Subgroups, which I found to be unique to GitLab compared to other repository platforms.\"**\n>\n> - Stephen G. Dame, University of Washington, Bothell\n\n## Set up groups: Build the right structure before writing a line of code\n\nThe foundation of an effective GitLab-based course is a well-planned group hierarchy. GitLab's **[Groups and Subgroups](https://docs.gitlab.com/tutorials/manage_user/#create-the-organization-parent-group-and-subgroups)** allow instructors to model the natural structure of a university department institution, course, and role with precise, inheritable permissions at every level.\n\nDame's structure places the university at the root (`UWTeaching`), with each course occupying its own subgroup (e.g. `css430`). Within each course sit repositories for `lecture-materials` and `code`, alongside dedicated Subgroups for `students` and `graders`. Instructor materials remain private, while student and grader subgroups are configured with controlled permissions so that assignment briefs and solutions are visible only to the right people.\n\n![Screenshot of GitLab group hierarchy — institution, course subgroup, and per-student subgroups](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777463673/dpxfnitv76pdmvcqtgag.png)\n\nPermissions cascade downward through the hierarchy via **Manage > Members**, allowing Dame to add students to a course's `students` subgroup with `Reporter` access and an expiration date tied to the end of the academic quarter. Students can clone and pull from assignment repositories but cannot push — keeping solution code firmly under instructor control.\n\nStudents are guided to set up SSH keys across all their working environments (local machines, cloud shells, virtual machines) so they can clone repositories and receive weekly updates via `git pull`. They copy relevant code into their own private repositories to manage their own version history.\n\n**Tip for large classes:** For larger cohorts, adding students by hand is impractical. GitLab's REST API lets you automate subgroup creation and membership from a list of usernames. Below is a sample Python script that handles this:\n\n```python\n    import gitlab\n    from datetime import datetime\n\n    # Connect to your GitLab instance\n    gl = gitlab.Gitlab('https://gitlab.com', private_token='YOUR_PRIVATE_TOKEN')\n\n    # Target parent group ID (e.g., the ID for \"css430 > students\")\n    parent_group_id = 12345678\n\n    # Set expiration: typically the beginning of the next month after quarter end\n    expiry_date = '2025-01-01'\n\n    # List of collected student usernames\n    student_list = ['alice_css430', 'bob_css430', 'carol_css430', 'dave_css430', 'eve_css430']\n\n    for username in student_list:\n        try:\n            # 1. Create a personal subgroup for the student\n            subgroup = gl.groups.create({\n                'name': username,\n                'path': username,\n                'parent_id': parent_group_id,\n                'visibility': 'private'\n            })\n\n            # 2. Add student to the new subgroup with Expiration\n            user = gl.users.list(username=username)[0]\n            subgroup.members.create({\n                'user_id': user.id,\n                'access_level': gitlab.const.REPORTER_ACCESS,\n                'expires_at': expiry_date\n            })\n            print(f\"Success: Subgroup created and student added for {username}\")\n        except Exception as e:\n            print(f\"Error processing {username}: {e}\")\n```\nThere is also an [open source project that automates class management](https://gitlab.com/edu-docs/class-management-automation) published by GitLab that provides additional tooling for this workflow.\n## Give feedback where the work actually lives\n\nOnce the structure is in place, the feedback workflow is where GitLab's value becomes most apparent to students. Dame asks students to submit assignments by opening a **[merge request](https://docs.gitlab.com/user/project/merge_requests/)** in their repository. This gives instructors an immediate, clean diff of everything the student has written.\n![A GitLab merge request showing inline code comment function for an instructor](https://res.cloudinary.com/about-gitlab-com/image/upload/v1777467468/icclzyglbkwlvfysggbi.png)\nInstructors can click any line of code and leave an **inline comment** — not just flagging what is wrong, but explaining why, and pointing to what to look at next. Students receive this feedback in direct context with their code, which is far more actionable than a comment at the bottom of a submitted document.\n\n## Join GitLab for Education\n\nSetting up your first GitLab assignment takes some initial effort, but once the structure is in place it largely runs itself. The real payoff goes beyond organization: Students graduate having worked daily in an environment that mirrors professional software development, building habits around [version control](https://about.gitlab.com/topics/version-control/) and [code review](https://docs.gitlab.com/development/code_review/) rather than learning them as abstract concepts.\n\nIf you are just getting started, keep it simple. Begin with a single course group, one assignment template, and a basic pipeline. The structure will grow naturally alongside your confidence with the platform.\n\nMake sure to **[sign up for GitLab for Education](https://about.gitlab.com/solutions/education/join/)** so that you and your students can access all top-tier features, including unlimited reviewers on merge requests, additional compute minutes, and expanded storage.\n\n> [Apply to the GitLab for Education program today](https://about.gitlab.com/solutions/education/join/).",[624,727],"open source",{"featured":14,"template":15,"slug":729},"teaching-software-development-the-easy-way-using-gitlab",{"content":731,"config":743},{"description":732,"authors":733,"heroImage":735,"date":736,"title":737,"body":738,"category":11,"tags":739},"AI-generated code is 34% of development work. Discover how to balance productivity gains with quality, reliability, and security.",[734],"Manav Khurana","https://res.cloudinary.com/about-gitlab-com/image/upload/v1767982271/e9ogyosmuummq7j65zqg.png","2026-01-08","AI is reshaping DevSecOps: Attend GitLab Transcend to see what’s next","AI promises a step change in innovation velocity, but most software teams are hitting a wall. According to our latest [Global DevSecOps Report](https://about.gitlab.com/developer-survey/), AI-generated code now accounts for 34% of all development work. Yet 70% of DevSecOps professionals report that AI is making compliance management more difficult, and 76% say agentic AI will create unprecedented security challenges.\n\nThis is the AI paradox: AI accelerates coding, but software delivery slows down as teams struggle to test, secure, and deploy all that code.\n\n## Productivity gains meet workflow bottlenecks\nThe problem isn't AI itself. It's how software gets built today. The traditional DevSecOps lifecycle contains hundreds of small tasks that developers must navigate manually: updating tickets, running tests, requesting reviews, waiting for approvals, fixing merge conflicts, addressing security findings. These tasks drain an average of seven hours per week from every team member, according to our research.\n\nDevelopment teams are producing code faster than ever, but that code still crawls through fragmented toolchains, manual handoffs, and disconnected processes. In fact, 60% of DevSecOps teams use more than five tools for software development overall, and 49% use more than five AI tools. This fragmentation creates collaboration barriers, with 94% of DevSecOps professionals experiencing factors that limit collaboration in the software development lifecycle.\n\nThe answer isn't more tools. It's intelligent orchestration that brings software teams and their AI agents together across projects and release cycles, with enterprise-grade security, governance, and compliance built in.\n\n## Seeking deeper human-AI partnerships\nDevSecOps professionals don't want AI to take over — they want reliable partnerships. The vast majority (82%) say using agentic AI would increase their job satisfaction, and 43% envision an ideal future with a 50/50 split between human and AI contributions. They're ready to trust AI with 37% of their daily tasks without human review, particularly for documentation, test writing, and code reviews.\n\nWhat we heard resoundingly from DevSecOps professionals is that AI won't replace them; rather, it will fundamentally reshape their roles. 83% of DevSecOps professionals believe AI will significantly change their work within five years, and notably, 76% think this will create more engineering jobs, not fewer. As coding becomes easier with AI, engineers who can architect systems, ensure quality, and apply business context will be in high demand.\n\nCritically, 88% agree there are essential human qualities that AI will never fully replace, including creativity, innovation, collaboration, and strategic vision.\n\nSo how can organizations bridge the gap between AI’s promise and the reality of fragmented workflows?\n\n## Join us at GitLab Transcend: Explore how to drive real value with agentic AI\nOn February 10, 2026, GitLab will be hosting Transcend, where we'll reveal how intelligent orchestration transforms AI-powered software development. You'll get a first look at GitLab's upcoming product roadmap and learn how teams are solving real-world challenges by modernizing development workflows with AI.\n\nOrganizations winning in this new era balance AI adoption with security, compliance, and platform consolidation. AI offers genuine productivity gains when implemented thoughtfully — not by replacing human developers, but by freeing DevSecOps professionals to focus on strategic thinking and creative innovation.\n\n[Register for Transcend today](https://about.gitlab.com/events/transcend/virtual/) to secure your spot and discover how intelligent orchestration can help your software teams stay in flow.",[740,741,742],"AI/ML","DevOps platform","security",{"featured":28,"template":15,"slug":744},"ai-is-reshaping-devsecops-attend-gitlab-transcend-to-see-whats-next",{"content":746,"config":757},{"title":747,"description":748,"authors":749,"heroImage":751,"date":752,"body":753,"category":11,"tags":754},"Atlassian ending Data Center as GitLab maintains deployment choice","As Atlassian transitions Data Center customers to cloud-only, GitLab presents a menu of deployment choices that map to business needs.",[750],"Emilio Salvador","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098354/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_5XrohmuWBNuqL89BxVUzWm_1750098354056.png","2025-10-07","Change is never easy, especially when it's not your choice. Atlassian's announcement that [all Data Center products will reach end-of-life by March 28, 2029](https://www.atlassian.com/blog/announcements/atlassian-ascend), means thousands of organizations must now reconsider their DevSecOps deployment and infrastructure. But you don't have to settle for deployment options that don't fit your needs. GitLab maintains your freedom to choose — whether you need self-managed for compliance, cloud for convenience, or hybrid for flexibility — all within a single AI-powered DevSecOps platform that respects your requirements.\n\nWhile other vendors force migrations to cloud-only architectures, GitLab remains committed to supporting the deployment choices that match your business needs. Whether you're managing sensitive government data, operating in air-gapped environments, or simply prefer the control of self-managed deployments, we understand that one size doesn't fit all.\n\n## The cloud isn't the answer for everyone\n\nFor the many companies that invested millions of dollars in Data Center deployments, including those that migrated to Data Center [after its Server products were discontinued](https://about.gitlab.com/blog/atlassian-server-ending-move-to-a-single-devsecops-platform/), this announcement represents more than a product sunset. It signals a fundamental shift away from customer-centric architecture choices, forcing enterprises into difficult positions: accept a deployment model that doesn't fit their needs, or find a vendor that respects their requirements.\n\nMany of the organizations requiring self-managed deployments represent some of the world's most important organizations: healthcare systems protecting patient data, financial institutions managing trillions in assets, government agencies safeguarding national security, and defense contractors operating in air-gapped environments.\n\nThese organizations don't choose self-managed deployments for convenience; they choose them for compliance, security, and sovereignty requirements that cloud-only architectures simply cannot meet. Organizations operating in closed environments with restricted or no internet access aren't exceptions — they represent a significant portion of enterprise customers across various industries.\n\n![GitLab vs. Atlassian comparison table](https://res.cloudinary.com/about-gitlab-com/image/upload/v1759928476/ynl7wwmkh5xyqhszv46m.jpg)\n\n## The real cost of forced cloud migration goes beyond dollars\n\nWhile cloud-only vendors frame mandatory migrations as \"upgrades,\" organizations face substantial challenges beyond simple financial costs:\n\n* **Lost integration capabilities:** Years of custom integrations with legacy systems, carefully crafted workflows, and enterprise-specific automations become obsolete. Organizations with deep integrations to legacy systems often find cloud migration technically infeasible.\n\n* **Regulatory constraints:** For organizations in regulated industries, cloud migration isn't just complex — it's often not permitted. Data residency requirements, air-gapped environments, and strict regulatory frameworks don't bend to vendor preferences. The absence of single-tenant solutions in many cloud-only approaches creates insurmountable compliance barriers.\n\n* **Productivity impacts:** Cloud-only architectures often require juggling multiple products: separate tools for planning, code management, CI/CD, and documentation. Each tool means another context switch, another integration to maintain, another potential point of failure. GitLab research shows [30% of developers spend at least 50% of their job maintaining and/or integrating their DevSecOps toolchain](https://about.gitlab.com/developer-survey/). Fragmented architectures exacerbate this challenge rather than solving it.\n\n## GitLab offers choice, commitment, and consolidation\n\nEnterprise customers deserve a trustworthy technology partner. That's why we've committed to supporting a range of deployment options — whether you need on-premises for compliance, hybrid for flexibility, or cloud for convenience, the choice remains yours. That commitment continues with [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), our AI solution that supports developers at every stage of their workflow.\n\nBut we offer more than just deployment flexibility. While other vendors might force you to cobble together their products into a fragmented toolchain, GitLab provides everything in a **comprehensive AI-native DevSecOps platform**. Source code management, CI/CD, security scanning, Agile planning, and documentation are all managed within a single application and a single vendor relationship.\n\nThis isn't theoretical. When Airbus and [Iron Mountain](https://about.gitlab.com/customers/iron-mountain/) evaluated their existing fragmented toolchains, they consistently identified challenges: poor user experience, missing functionalities like built-in security scanning and review apps, and management complexity from plugin troubleshooting. **These aren't minor challenges; they're major blockers for modern software delivery.**\n\n## Your migration path: Simpler than you think\n\nWe've helped thousands of organizations migrate from other vendors, and we've built the tools and expertise to make your transition smooth:\n\n* **Automated migration tools:** Our [Bitbucket Server importer](https://docs.gitlab.com/user/import/bitbucket_server/) brings over repositories, pull requests, comments, and even Large File Storage (LFS) objects. For Jira, our [built-in importer](https://docs.gitlab.com/user/project/import/jira/) handles issues, descriptions, and labels, with professional services available for complex migrations.\n\n* **Proven at scale:** A 500 GiB repository with 13,000 pull requests, 10,000 branches, and 7,000 tags is likely to [take just 8 hours to migrate](https://docs.gitlab.com/user/import/bitbucket_server/) from Bitbucket to GitLab using parallel processing.\n\n* **Immediate ROI:** A [Forrester Consulting Total Economic Impact™ study commissioned by GitLab](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/) found that investing in GitLab Ultimate confirms these benefits translate to real bottom-line impact, with a three-year 483% ROI, 5x time saved in security related activities, and 25% savings in software toolchain costs.\n\n## Start your journey to a unified DevSecOps platform\n\nForward-thinking organizations aren't waiting for vendor-mandated deadlines. They're evaluating alternatives now, while they have time to migrate thoughtfully to platforms that protect their investments and deliver on promises.\n\nOrganizations invest in self-managed deployments because they need control, compliance, and customization. When vendors deprecate these capabilities, they remove not just features but the fundamental ability to choose environments matching business requirements.\n\nModern DevSecOps platforms should offer complete functionality that respects deployment needs, consolidates toolchains, and accelerates software delivery, without forcing compromises on security or data sovereignty.\n\n[Talk to our sales team](https://about.gitlab.com/sales/) today about your migration options, or explore our [comprehensive migration resources](https://about.gitlab.com/move-to-gitlab-from-atlassian/) to see how thousands of organizations have already made the switch.\n\nYou also can [try GitLab Ultimate with GitLab Duo Enterprise](https://about.gitlab.com/free-trial/devsecops/) for free for 30 days to see what a unified DevSecOps platform can do for your organization.",[23,569,755,756],"product","features",{"featured":28,"template":15,"slug":758},"atlassian-ending-data-center-as-gitlab-maintains-deployment-choice",{"promotions":760},[761,775,786,797],{"id":762,"categories":763,"header":765,"text":766,"button":767,"image":772},"ai-modernization",[764],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":768,"config":769},"Get your AI maturity score",{"href":770,"dataGaName":771,"dataGaLocation":247},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":776,"categories":777,"header":778,"text":766,"button":779,"image":783},"devops-modernization",[755,11],"Are you just managing tools or shipping innovation?",{"text":780,"config":781},"Get your DevOps maturity score",{"href":782,"dataGaName":771,"dataGaLocation":247},"/assessments/devops-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":787,"categories":788,"header":789,"text":766,"button":790,"image":794},"security-modernization",[742],"Are you trading speed for security?",{"text":791,"config":792},"Get your security maturity score",{"href":793,"dataGaName":771,"dataGaLocation":247},"/assessments/security-modernization-assessment/",{"config":795},{"src":796},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":798,"paths":799,"header":802,"text":803,"button":804,"image":809},"github-azure-migration",[800,801],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":805,"config":806},"See how GitLab compares to GitHub",{"href":807,"dataGaName":808,"dataGaLocation":247},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":810},{"src":785},{"header":812,"blurb":813,"button":814,"secondaryButton":819},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":815,"config":816},"Get your free trial",{"href":817,"dataGaName":54,"dataGaLocation":818},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":510,"config":820},{"href":58,"dataGaName":59,"dataGaLocation":818},1777493586852]