[{"data":1,"prerenderedAt":820},["ShallowReactive",2],{"/en-us/blog/set-up-infrastructure-for-cloud-development-environments":3,"navigation-en-us":43,"banner-en-us":453,"footer-en-us":463,"blog-post-authors-en-us-Michael Friedrich":701,"blog-related-posts-en-us-set-up-infrastructure-for-cloud-development-environments":715,"blog-promotions-en-us":757,"next-steps-en-us":810},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":30,"path":31,"publishedDate":20,"seo":32,"stem":37,"tagSlugs":38,"__hash__":42},"blogPosts/en-us/blog/set-up-infrastructure-for-cloud-development-environments.yml","Set Up Infrastructure For Cloud Development Environments",[7],"michael-friedrich",null,"engineering",{"slug":11,"featured":12,"template":13},"set-up-infrastructure-for-cloud-development-environments",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Set up your infrastructure for on-demand, cloud-based development environments in GitLab","Learn how to set up the requirements, manage Kubernetes clusters in different clouds, create the first workspaces and custom images, and get tips and troubleshooting.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659883/Blog/Hero%20Images/post-cover-image.jpg","2023-07-13","Cloud-based development environments enable a better developer onboarding experience and help make teams more efficient. In this tutorial, you'll learn how to ready your infrastructure for on-demand, cloud-based development environments. You'll also learn how to set up the requirements, manage Kubernetes clusters in different clouds, create your first workspaces and custom images, and get tips for troubleshooting.\n\nThe GitLab agent for Kubernetes, an OAuth GitLab app, and a proxy pod deployment make the setup reproducible in different Kubernetes cluster environments and follow cloud-native best practices. Bringing your infrastructure allows platform teams to store the workspace data securely, control resource usage, harden security, and troubleshoot the deployments in known ways.\n\nThis blog post is a long read so feel free to navigate to the sections of interest. However, if you want to follow the tutorial step by step, the sections depend on one another for the parts pertaining to infrastructure setup.\n\n- [Development environments on your infrastructure](#development-environments-on-your-infrastructure)\n- [Requirements](#requirements)\n    - [Workspaces domain](#workspaces-domain)\n    - [TLS certificates](#tls-certificates)\n- [GitLab OAuth application ](#gitlab-oauth-application)\n- [Kubernetes cluster setup](#kubernetes-cluster-setup)\n    - [Set up infrastructure with Google Kubernetes Engine (GKE)](#set-up-infrastructure-with-google-kubernetes-engine=gke)\n    - [Set up infrastructure with Amazon Elastic Kubernetes Service (EKS)](#set-up-infrastructure-with-amazon-elastic-kubernetes-service-eks)\n    - [Set up infrastructure with Azure Managed Kubernetes Service (AKS)](#set-up-infrastructure-with-azure-managed-kubernetes-service-aks)\n    - [Set up infrastructure with Civo Cloud Kubernetes](#set-up-infrastructure-with-civo-cloud-kubernetes)\n    - [Set up infrastructure with self-managed Kubernetes](#set-up-infrastructure-with-self-managed-kubernetes)\n- [Workspaces proxy installation into Kubernetes](#workspaces-proxy-installation-into-kubernetes)\n- [Agent for Kubernetes installation](#agent-for-kubernetes-installation)\n- [Workspaces creation](#workspaces-creation)\n    - [Create the first workspaces](#create-the-first-workspaces)\n    - [Custom workspace container images](#custom-workspace-container-images)\n- [Tips](#tips)\n    - [Certificate management](#certificate-management)\n    - [Troubleshooting](#troubleshooting)\n    - [Contribute](#contribute)\n- [Share your feedback](#share-your-feedback)\n\n## Development environments on your infrastructure\nSecure, on-demand, cloud-based development workspaces are [available in beta for public projects](/blog/introducing-workspaces-beta/) for Premium and Ultimate customers. The first iteration allows you to bring your own infrastructure as a Kubernetes cluster. GitLab already deeply integrates with Kubernetes through the GitLab agent for Kubernetes, setting the foundation for configuration and cluster management.\n\nUsers can define and use a development environment template in a project. Workspaces in GitLab support the [devfile specification](https://docs.gitlab.com/ee/user/workspace/#devfile) as `.devfile.yaml` in the project repository root. The devfile attributes allow configuring of the workspace. For example, the `image` attribute specifies the container image to run and create the workspace in isolated container environments. The containers require a cluster orchestrator, such as Kubernetes, that manages resource usage and ensures data security and safety. Workspaces also need authorization: Project source code may contain sensitive intellectual property or otherwise confidential data in specific environments. The setup requires a GitLab OAuth application as the foundation here.\n\nThe following steps provide an in-depth setup guide for different cloud providers. If you prefer to set up your own environment, please follow the [documentation for workspace prerequisites](https://docs.gitlab.com/ee/user/workspace/#prerequisites). In general, we will practice the following steps:\n0. (Optional) Register a workspaces domain, and create TLS certificates.\n1. Create a Kubernetes cluster and configure access and requirements.\n2. Install an Ingress controller.\n3. Set up the workspaces proxy with the domain, TLS certificates, and OAuth app.\n4. Create a new GitLab group with a GitLab agent project. The agent can be used for all projects in that group.\n5. Install the GitLab agent for Kubernetes using the UI provided Helm chart command.\n6. Create an example project with a devfile configuration for workspaces.\n\nSome commands do not use the terminal indicator (`$` or `#`) to support easier copy-paste of command blocks into terminals.\n\n## Requirements\nThe steps in this blog post require the following CLI tools:\n1. `kubectl` and `helm` for Kubernetes\n2. `certbot` for Let's Encrypt\n3. git, curl, dig, openssl, and sslscan for troubleshooting\n\n### Workspaces domain\nWorkspaces require a domain with DNS entries. Cloud providers, for example, Google Cloud, also provide domain services which integrate more easily. You can also register and manage domains with your preferred provider.\n\nThe required DNS entries will be:\n- Wildcard DNS (`*.remote-dev.dev`) and hostname (`remote-dev.dev`) A/AAAA records pointing to the external Kubernetes external IP: `kubectl get services -A`\n- (Optional, with Let's Encrypt) ACME DNS challenge entries as TXT records\n\nAfter acquiring a domain, wait until the Kubernetes setup is ready and extract the A/AAAA records for the DNS settings. The following example shows how `remote-dev.dev` is configured in the Google Cloud DNS service.\n\n![GitLab remote development workspaces, example DNS configuration for remote-dev.dev](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_google_cloud_dns_remote-dev.dev-entries.png)\n\nExport shell variables that define the workspaces domains, and the email contact. These variables will be used in all setup steps below.\n\n```shell\nexport EMAIL=\"user@company.com\"\nexport GITLAB_WORKSPACES_PROXY_DOMAIN=\"remote-dev.dev\"\nexport GITLAB_WORKSPACES_WILDCARD_DOMAIN=\"*.remote-dev.dev\"\n```\n\n**Note:** This blog post will show the example domain `remote-dev.dev` for better understanding with a working example. The domain `remote-dev.dev` is maintained by the [Developer Evangelism team at GitLab](https://handbook.gitlab.com/handbook/marketing/developer-relations/developer-advocacy/projects/). There are no public demo environments available at the time of writing this blog post.\n\n### TLS certificates\nTLS certificates can be managed with different methods. To get started quickly, it is recommended to follow the [documentation steps](https://docs.gitlab.com/ee/user/workspace/#prerequisites) with Let's Encrypt and later consider production requirements with TLS certificates.\n\n```shell\ncertbot -d \"${GITLAB_WORKSPACES_PROXY_DOMAIN}\" \\\n  -m \"${EMAIL}\" \\\n  --config-dir ~/.certbot/config \\\n  --logs-dir ~/.certbot/logs \\\n  --work-dir ~/.certbot/work \\\n  --manual \\\n  --preferred-challenges dns certonly\n\n  certbot -d \"${GITLAB_WORKSPACES_WILDCARD_DOMAIN}\" \\\n  -m \"${EMAIL}\" \\\n  --config-dir ~/.certbot/config \\\n  --logs-dir ~/.certbot/logs \\\n  --work-dir ~/.certbot/work \\\n  --manual \\\n  --preferred-challenges dns certonly\n```\n\nThe Let's Encrypt CLI prompts you for the ACME DNS challenge. This requires setting TXT records for the challenge session immediately. Add the DNS records and specify a low TTL (time-to-live) of 300 seconds to update the records during the first steps.\n\n```text\n_acme-challenge TXT \u003Cstringfromletsencryptacmechallenge>\n```\n\nYou can verify the DNS records using the `dig` CLI command.\n\n```shell\n$ dig _acme-challenge.remote-dev.dev txt\n...\n;; ANSWER SECTION:\n_acme-challenge.remote-dev.dev.\t246 IN\tTXT\t\"TlGRM9JGdXHGVklPWgytflxWDF82Sv04nF--Wl9JFvg\"\n_acme-challenge.remote-dev.dev.\t246 IN\tTXT\t\"CqG_54w6I0heWF3wLMAmUAitPcUMs9qAU9b8QhBWFj8\"\n```\n\nOnce the Let's Encrypt routine is complete, note the TLS certificate location.\n\n```text\nSuccessfully received certificate.\nCertificate is saved at: /Users/mfriedrich/.certbot/config/live/remote-dev.dev/fullchain.pem\nKey is saved at:         /Users/mfriedrich/.certbot/config/live/remote-dev.dev/privkey.pem\nThis certificate expires on 2023-08-15.\nThese files will be updated when the certificate renews.\n\nSuccessfully received certificate.\nCertificate is saved at: /Users/mfriedrich/.certbot/config/live/remote-dev.dev-0001/fullchain.pem\nKey is saved at:         /Users/mfriedrich/.certbot/config/live/remote-dev.dev-0001/privkey.pem\nThis certificate expires on 2023-08-15.\nThese files will be updated when the certificate renews.\n```\n\nExport the TLS certificate paths into environment variables for the following setup steps.\n\n```shell\nexport WORKSPACES_DOMAIN_CERT=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}/fullchain.pem\"\nexport WORKSPACES_DOMAIN_KEY=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}/privkey.pem\"\n\nexport WILDCARD_DOMAIN_CERT=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}-0001/fullchain.pem\"\nexport WILDCARD_DOMAIN_KEY=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}-0001/privkey.pem\"\n```\n\n**Note**: If you prefer to use your certificates, please copy the files into a safe location, and export the environment variables with the path details.\n\n## GitLab OAuth application\n_After preparing the requirements, continue with the components setup._\n\nCreate a [group-owned OAuth application](https://docs.gitlab.com/ee/integration/oauth_provider.html) for the remote development workspaces group. Creating a centrally managed app with a service account or group with limited access is recommended for production use.\n\nNavigate into the group `Settings > Applications` and specify the following values:\n\n1. Name: `Remote Development workspaces by \u003Cresponsible team> - \u003Cdomain>`. Add the reponsible team that is trusted in your organization. For debugging, add the domain. There might be multiple authorization groups, this helps the identification which workspace domain is used.\n2. Redirect URI: `https://\u003CGITLAB_WORKSPACES_PROXY_DOMAIN>/auth/callback`. Replace `GITLAB_WORKSPACES_PROXY_DOMAIN` with the domain string value.\n3. Set the scopes to `api, read_user, openid, profile` .\n\n![GitLab remote development workspaces, OAuth application in the group settings](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_oauth_app_create.png)\n\nStore the OAuth application details in your password vault, and export them as shell environment variables for the next setup steps.\n\nCreate a configuration secret for the proxy as a signing key (`SIGNING_KEY`), and store it in a safe place (for example, use a secrets vault like 1Password to create and store the key).\n\n```shell\nexport CLIENT_ID=\"XXXXXXXXX\" # Look into password vault and set\nexport CLIENT_SECRET=\"XXXXXXXXXX\" # Look into password vault and set\nexport REDIRECT_URI=\"https://${GITLAB_WORKSPACES_PROXY_DOMAIN}/auth/callback\"\n\nexport GITLAB_URL=\"https://gitlab.com\" # Replace with your self-managed GitLab instance URL if not using GitLab.com SaaS\nexport SIGNING_KEY=\"a_random_key_consisting_of_letters_numbers_and_special_chars\" # Look into password vault and set\n```\n\n## Kubernetes cluster setup\nThe following sections describe how to set up a Kubernetes cluster in different cloud and on-premises environments and install an [ingress controller](https://kubernetes.io/docs/concepts/services-networking/ingress-controllers/) for HTTP access. After completing the Kubernetes setup, you can continue with the workspaces proxy and agent setup steps.\n\n**Choose one method to create a Kubernetes cluster. Note: Use `amd64` as platform architecture [until multi-architecture support is available for running workspaces](https://gitlab.com/groups/gitlab-org/-/epics/10594).** Cloud environments with Arm support will not work yet, for example AWS EKS on Graviton EC2 instances.\n\nYou should have defined the following variables from the previous setup steps:\n\n```sh\nexport EMAIL=\"user@company.com\"\nexport GITLAB_WORKSPACES_PROXY_DOMAIN=\"remote-dev.dev\"\nexport GITLAB_WORKSPACES_WILDCARD_DOMAIN=\"*.remote-dev.dev\"\n\nexport WORKSPACES_DOMAIN_CERT=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}/fullchain.pem\"\nexport WORKSPACES_DOMAIN_KEY=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}/privkey.pem\"\n\nexport WILDCARD_DOMAIN_CERT=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}-0001/fullchain.pem\"\nexport WILDCARD_DOMAIN_KEY=\"${HOME}/.certbot/config/live/${GITLAB_WORKSPACES_PROXY_DOMAIN}-0001/privkey.pem\"\n\nexport CLIENT_ID=\"XXXXXXXXX\" # Look into password vault and set\nexport CLIENT_SECRET=\"XXXXXXXXXX\" # Look into password vault and set\nexport REDIRECT_URI=\"https://${GITLAB_WORKSPACES_PROXY_DOMAIN}/auth/callback\"\n\nexport GITLAB_URL=\"https://gitlab.com\" # Replace with your self-managed GitLab instance URL if not using GitLab.com SaaS\nexport SIGNING_KEY=\"XXXXXXXX\" # Look into password vault and set\n\n```\n\n### Set up infrastructure with Google Kubernetes Engine (GKE)\n\n[Install and configure the Google Cloud SDK and `gcloud` CLI](https://cloud.google.com/sdk/docs/install?hl=en), and install the `gke-gcloud-auth-plugin` plugin to authenticate against Google Cloud.\n\n```shell\nbrew install --cask google-cloud-sdk\n\ngcloud components install gke-gcloud-auth-plugin\n\ngcloud auth login\n```\n\nCreate a new GKE cluster using the `gcloud` command, or follow the steps in the Google Cloud Console.\n\n```shell\n\nexport GCLOUD_PROJECT=group-community\nexport GCLOUD_CLUSTER=de-remote-development-1\n\ngcloud config set project $GCLOUD_PROJECT\n\n# Create cluster (modify for your needs)\ngcloud container clusters create $GCLOUD_CLUSTER \\\n    --release-channel stable \\\n    --zone us-central1-c \\\n    --project $GCLOUD_PROJECT\n\n# Verify cluster\ngcloud container clusters list\n\nNAME                     LOCATION         MASTER_VERSION   MASTER_IP       MACHINE_TYPE  NODE_VERSION       NUM_NODES  STATUS\nde-remote-development-1  us-central1-c    1.26.3-gke.1000  34.136.33.199   e2-medium     1.26.3-gke.1000    3          RUNNING\n\ngcloud container clusters get-credentials $GCLOUD_CLUSTER --zone us-central1-c --project $GCLOUD_PROJECT\nFetching cluster endpoint and auth data.\nkubeconfig entry generated for de-remote-development-1.\n```\n\n1. The setup requires the [`Kubernetes Engine Admin` role in Google IAM](https://cloud.google.com/kubernetes-engine/docs/concepts/access-control?hl=en#recommendations) to create ClusterRoleBindings.\n2. Create a new Kubernetes cluster (do not use Autopilot).\n3. Ensure that [cluster autoscaling](https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler?hl=en) is enabled in the GKE cluster.\n4. Verify that a [default Storage Class](https://cloud.google.com/kubernetes-engine/docs/concepts/persistent-volumes?hl=en#storageclasses) has been defined.\n5. Install an Ingress controller, for example [ingress-nginx](https://kubernetes.github.io/ingress-nginx/deploy/#gce-gke). Follow the documentation and run the following commands to install `ingress-nginx` into the Kubernetes cluster.\n\n```shell\nkubectl create clusterrolebinding cluster-admin-binding \\\n  --clusterrole cluster-admin \\\n  --user $(gcloud config get-value account)\n\nkubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/controller-v1.7.1/deploy/static/provider/cloud/deploy.yaml\n```\n\nPrint the external IP for the DNS records, and update wildcard DNS (`*.remote-dev.dev`) and hostname (`remote-dev.dev`).\n\n```shell\ngcloud container clusters list\n\nkubectl get services -A\n```\n\n### Set up infrastructure with Amazon Elastic Kubernetes Service (EKS)\nCreating an Amazon EKS cluster requires [cluster IAM roles](https://docs.aws.amazon.com/eks/latest/userguide/create-cluster.html). You can the [`eksctl` CLI for Amazon EKS](https://eksctl.io/), which automatically creates the roles. `eksctl` [requires the AWS IAM Authenticator for Kubernetes](https://github.com/weaveworks/eksctl/blob/main/README.md#prerequisite), which will get pulled with Homebrew automatically on macOS.\n\n```shell\nbrew install eksctl awscli aws-iam-authenticator\naws configure\n\neksctl create cluster --name remote-dev \\\n    --region us-west-2 \\\n    --node-type m5.xlarge \\\n    --nodes 3 \\\n    --nodes-min=1 \\\n    --nodes-max=4 \\\n    --version=1.26 \\\n    --asg-access\n```\n\nThe eksctl command uses the [`--asg-access`, `--nodes-min/max` parameters for auto-scaling](https://eksctl.io/usage/autoscaling/). The autoscaler requires [additional configuration steps](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/cloudprovider/aws/README.md), alternatively [Karpenter is supported in Amazon EKS](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/). Review the [autoscaling documentation](https://docs.aws.amazon.com/eks/latest/userguide/autoscaling.html), and [default Storage Class `gp2`](https://docs.aws.amazon.com/eks/latest/userguide/storage-classes.html) fulfilling the requirements. The Kubernetes configuration is automatically updated locally.\n\nInstall the [Nginx Ingress controller for EKS](https://kubernetes.github.io/ingress-nginx/deploy/#aws). Follow the documentation and run the following command to install `ingress-nginx` into the Kubernetes cluster.\n\n```shell\nkubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/controller-v1.8.0/deploy/static/provider/aws/deploy.yaml\n```\n\nPrint the external IP for the DNS records, and update wildcard DNS (`*.remote-dev.dev`) and hostname (`remote-dev.dev`).\n\n```shell\neksctl get cluster --region us-west-2 --name remote-dev\n\nkubectl get services -A\n```\n\n### Set up infrastructure with Azure Managed Kubernetes Service (AKS)\nInstall [Azure CLI](https://learn.microsoft.com/en-us/azure/aks/learn/quick-kubernetes-deploy-cli).\n\n```shell\nbrew install azure-cli\n\naz login\n```\n\nReview the documentation for the [cluster autoscaler in AKS](https://learn.microsoft.com/en-us/azure/aks/cluster-autoscaler) and the [default Storage Class being `managed-csi`](https://learn.microsoft.com/en-us/azure/aks/concepts-storage#storage-classes), create a new resource group, and create a new Kubernetes cluster. Download the Kubernetes configuration to continue with the `kubectl` commands.\n\n```shell\naz group create --name remote-dev-rg --location eastus\n\naz aks create \\\n--resource-group remote-dev-rg \\\n--name remote-dev \\\n--node-count 1 \\\n--vm-set-type VirtualMachineScaleSets \\\n--load-balancer-sku standard \\\n--enable-cluster-autoscaler \\\n--min-count 1 \\\n--max-count 3\n\naz aks get-credentials --resource-group remote-dev-rg --name remote-dev\n```\n\nInstall the [Nginx ingress controller in AKS](https://learn.microsoft.com/en-us/azure/aks/ingress-basic?tabs=azure-cli#basic-configuration). Follow the documentation and run the following commands to install `ingress-nginx` into the Kubernetes cluster.\n\n```shell\nNAMESPACE=ingress-basic\n\nhelm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx\nhelm repo update\n\nhelm install ingress-nginx ingress-nginx/ingress-nginx \\\n  --create-namespace \\\n  --namespace $NAMESPACE \\\n  --set controller.service.annotations.\"service\\.beta\\.kubernetes\\.io/azure-load-balancer-health-probe-request-path\"=/healthz\n```\n\nPrint the external IP for the DNS records, and update wildcard DNS (`*.remote-dev.dev`) and hostname (`remote-dev.dev`).\n\n```shell\nkubectl get services --namespace ingress-basic -o wide -w ingress-nginx-controller\n\nkubectl get services -A\n```\n\n### Set up infrastructure with Civo Cloud Kubernetes\nInstall and configure the [Civo CLI](https://www.civo.com/docs/kubernetes/create-a-cluster#creating-a-cluster-using-civo-cli), and create a Kubernetes cluster using 2 nodes, 4 CPUs, 8 GB RAM.\n\n```shell\ncivo kubernetes create remote-dev -n 2 -s g4s.kube.large\n\ncivo kubernetes config remote-dev --save\nkubectl config use-context remote-dev\n```\n\nYou have full permissions on the cluster to create ClusterRoleBindings. The [default Storage Class](https://www.civo.com/docs/kubernetes/kubernetes-volumes#creating-a-persistent-volume-claim-pvc) is set to 'civo-volume'.\n\nInstall the [Nginx Ingress controller using Helm](https://kubernetes.github.io/ingress-nginx/deploy/#quick-start). Follow the documentation and run the following command to install `ingress-nginx` into the Kubernetes cluster.\n\n```shell\nhelm upgrade --install ingress-nginx ingress-nginx \\\n  --repo https://kubernetes.github.io/ingress-nginx \\\n  --namespace ingress-nginx --create-namespace\n\n```\n\nPrint the external IP for the DNS records, and update wildcard DNS (`*.remote-dev.dev`) and hostname (`remote-dev.dev`).\n\n```shell\ncivo kubernetes show remote-dev\n\nkubectl get services -A\n```\n\n### Set up infrastructure with self-managed Kubernetes\nThe process follows similar steps, requiring a user with permission to create `ClusterRoleBinding` resources. The [Nginx Ingress controller](https://kubernetes.github.io/ingress-nginx/deploy/#quick-start) is the fastest path forward. Once the cluster is ready, print the load balancer IP for the DNS records, and create/update A/AAAA record for wildcard DNS (`*.remote-dev.dev`) and hostname (`remote-dev.dev`) pointing to the load balancer IP.\n\n## Workspaces proxy installation into Kubernetes\n_After completing the Kubernetes cluster setup with one of your preferred providers, please continue with the next steps._\n\nAdd the Helm repository for the workspaces proxy (it is using the [Helm charts feature in the GitLab package registry](https://docs.gitlab.com/ee/user/packages/helm_repository/)).\n\n```shell\nhelm repo add gitlab-workspaces-proxy \\\nhttps://gitlab.com/api/v4/projects/gitlab-org%2fremote-development%2fgitlab-workspaces-proxy/packages/helm/devel\n```\n\nInstall the gitlab-workspaces-proxy, and optionally [specify the most current chart version](https://gitlab.com/gitlab-org/remote-development/gitlab-workspaces-proxy/-/blob/main/helm/Chart.yaml). If you are using a different ingress controller than Nginx, you need to change the `ingress.className` key. Re-run the command when new TLS certificates need to be installed.\n\n```shell\nhelm repo update\n\nhelm upgrade --install gitlab-workspaces-proxy \\\n  gitlab-workspaces-proxy/gitlab-workspaces-proxy \\\n  --version 0.1.6 \\\n  --namespace=gitlab-workspaces \\\n  --create-namespace \\\n  --set=\"auth.client_id=${CLIENT_ID}\" \\\n  --set=\"auth.client_secret=${CLIENT_SECRET}\" \\\n  --set=\"auth.host=${GITLAB_URL}\" \\\n  --set=\"auth.redirect_uri=${REDIRECT_URI}\" \\\n  --set=\"auth.signing_key=${SIGNING_KEY}\" \\\n  --set=\"ingress.host.workspaceDomain=${GITLAB_WORKSPACES_PROXY_DOMAIN}\" \\\n--set=\"ingress.host.wildcardDomain=${GITLAB_WORKSPACES_WILDCARD_DOMAIN}\" \\\n  --set=\"ingress.tls.workspaceDomainCert=$(cat ${WORKSPACES_DOMAIN_CERT})\" \\\n  --set=\"ingress.tls.workspaceDomainKey=$(cat ${WORKSPACES_DOMAIN_KEY})\" \\\n  --set=\"ingress.tls.wildcardDomainCert=$(cat ${WILDCARD_DOMAIN_CERT})\" \\\n  --set=\"ingress.tls.wildcardDomainKey=$(cat ${WILDCARD_DOMAIN_KEY})\" \\\n  --set=\"ingress.className=nginx\"\n```\n\nThe chart installs and configures the ingress automatically. You can verify the setup by getting the `Ingress` resource type:\n\n```shell\nkubectl get ingress -n gitlab-workspaces\n\nNAME                      CLASS   HOSTS                             ADDRESS   PORTS     AGE\ngitlab-workspaces-proxy   nginx   remote-dev.dev,*.remote-dev.dev             80, 443   9s\n```\n\n### Agent for Kubernetes installation\nCreate the agent configuration file in `.gitlab/agents/\u003Cagentname>/config.yaml`, add to git, and push it into the repository. The `remote_development` key specifies the `dns_zone`, which must be set to the workspaces domain. Additionally, the integration needs to be enabled. The `observability` key intentionally configures [debug logging](https://docs.gitlab.com/ee/user/clusters/agent/work_with_agent.html#debug-the-agent) for the first setup to troubleshoot faster. You can adjust the `logging` levels for production usage.\n\n```shell\nexport GL_AGENT_K8S=remote-dev-dev\n\n$ mkdir agent-kubernetes && cd agent-kubernetes\n$ mkdir -p .gitlab/agents/${GL_AGENT_K8S}/\n\n$ cat \u003C\u003CEOF >.gitlab/agents/${GL_AGENT_K8S}/config.yaml\nremote_development:\n    enabled: true\n    dns_zone: \"${GITLAB_WORKSPACES_PROXY_DOMAIN}\"\n\nobservability:\n  logging:\n    level: debug\n    grpc_level: warn\nEOF\n\n$ git add .gitlab/agents/${GL_AGENT_K8S}/config.yaml\n$ git commit -avm \"Add agent for Kubernetes configuration\"\n# adjust the URL to your GitLab server URL and project path\n$ git remote add origin https://gitlab.example.com/remote-dev-workspaces/agent-kubernetes.git\n# will create a private project when https/PAT is used\n$ git push\n```\n\nOpen the GitLab project in your browser, navigate into `Operate > Kubernetes Clusters`, and click the `Connect a new cluster (agent)` button. Select the agent from the configuration dropdown, and click `Register`. The form generates a ready-to-use Helm chart CLI command. Similar to the command below, replace `XXXXXXXXXXREPLACEME` with the actual token value.\n\n```shell\nhelm repo add gitlab https://charts.gitlab.io\nhelm repo update\nhelm upgrade --install remote-dev-dev gitlab/gitlab-agent \\\n    --namespace gitlab-agent-remote-dev-dev \\\n    --create-namespace \\\n    --set image.tag=v16.0.1 \\\n    --set config.token=XXXXXXXXXXREPLACEME \\\n    --set config.kasAddress=wss://kas.gitlab.com # Replace with your self-managed GitLab KAS instance URL if not using GitLab.com SaaS\n```\n\nRun the commands, and verify that the agent is connected in the `Operate > Kubernetes Clusters` overview. You can access the pod logs using the following command:\n\n```shell\n$ kubectl get ns\nNAME                          STATUS   AGE\ngitlab-agent-remote-dev-dev   Active   9d\ngitlab-workspaces             Active   22d\n...\n\n$ kubectl logs -f -l app.kubernetes.io/name=gitlab-agent -n gitlab-agent-$GL_AGENT_K8S\n```\n\n_Congrats! Your infrastructure setup for on-demand, cloud-based development environments is complete._\n\n## Workspaces creation\nAfter completing the infrastructure setup, you must verify that all components work together and users can create workspaces. You can fork or import the [`example-python-http-simple` project](https://gitlab.com/gitlab-da/use-cases/remote-development/example-python-http-simple) into your GitLab group with access to the GitLab agent for Kubernetes to try it immediately. The project provides a simple Python web app with Flask that provides different HTTP routes. Alternatively, start with a new project and create a `.devfile.yaml` with the [example configuration](https://docs.gitlab.com/ee/user/workspace/#example-configurations).\n\nOptional: Inspect the [`.devfile.yaml`](https://docs.gitlab.com/ee/user/workspace/#devfile) file to learn about the configuration format. We will look into the `image` key later.\n\n```yaml\nschemaVersion: 2.2.0\ncomponents:\n  - name: py\n    attributes:\n      gl/inject-editor: true\n    container:\n      # Use a custom image that supports arbitrary user IDs.\n      # NOTE: THIS IMAGE IS NOT ACTIVELY MAINTAINED. DEMO USE CASES ONLY, DO NOT USE IN PRODUCTION.\n      # Source: https://gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id\n      image: registry.gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id:latest\n      memoryRequest: 1024M\n      memoryLimit: 2048M\n      cpuRequest: 500m\n      cpuLimit: 1000m\n      endpoints:\n        - name: http-python\n          targetPort: 8080\n```\n\n### Create the first workspaces\nNavigate to the `Your Work > Workspaces` menu and create a new workspace. Search for the project name, select the agent for Kubernetes, and create the workspace.\n\n![GitLab remote development workspaces, Python example](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_python.png)\n\nOpen two terminals to follow the workspaces proxy and agent logs in the Kubernetes cluster.\n\n```shell\n$ kubectl logs -f -l app.kubernetes.io/name=gitlab-workspaces-proxy -n gitlab-workspaces\n\n{\"level\":\"info\",\"ts\":1686331102.886607,\"caller\":\"server/server.go:74\",\"msg\":\"Starting proxy server...\"}\n{\"level\":\"info\",\"ts\":1686331133.146862,\"caller\":\"upstream/tracker.go:47\",\"msg\":\"New upstream added\",\"host\":\"8080-workspace-62029-5534214-2vxdxq.remote-dev.dev\",\"backend\":\"workspace-62029-5534214-2vxdxq.gl-rd-ns-62029-5534214-2vxdxq\",\"backend_port\":8080}\n2023/06/09 17:21:10 getHostnameFromState state=https://60001-workspace-62029-5534214-2vxdxq.remote-dev.dev/folder=/projects/demo-python-http-simple\n```\n\n```shell\n$ kubectl logs -f -l app.kubernetes.io/name=gitlab-agent -n gitlab-agent-$GL_AGENT_K8S\n\n{\"level\":\"debug\",\"time\":\"2023-06-09T18:36:19.839Z\",\"msg\":\"Applied event\",\"mod_name\":\"remote_development\",\"apply_event\":\"WaitEvent{ GroupName: \\\"wait-0\\\", Status: \\\"Pending\\\", Identifier: \\\"gl-rd-ns-62029-5534214-k66cjy_workspace-62029-5534214-k66cjy-gl-workspace-data__PersistentVolumeClaim\\\" }\",\"agent_id\":62029}\n{\"level\":\"debug\",\"time\":\"2023-06-09T18:36:19.866Z\",\"msg\":\"Received update event\",\"mod_name\":\"remote_development\",\"workspace_namespace\":\"gl-rd-ns-62029-5534214-k66cjy\",\"workspace_name\":\"workspace-62029-5534214-k66cjy\",\"agent_id\":62029}\n{\"level\":\"debug\",\"time\":\"2023-06-09T18:36:43.627Z\",\"msg\":\"Applied event\",\"mod_name\":\"remote_development\",\"apply_event\":\"WaitEvent{ GroupName: \\\"wait-0\\\", Status: \\\"Successful\\\", Identifier: \\\"gl-rd-ns-62029-5534214-k66cjy_workspace-62029-5534214-k66cjy_apps_Deployment\\\" }\",\"agent_id\":62029}\n```\n\nWait until the workspace is provisioned successfully, and click to open the HTTP URL, example format `https://60001-workspace-62029-5534214-2vxdxq.remote-dev.dev/?folder=%2Fprojects%2Fexample-python-http-simple`. The GitLab OAuth application will ask you for authorization.\n\n![GitLab OAuth provider app, example with the Developer Evangelism demo environment](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_oauth_app.png)\n\nYou can select the Web IDE menu, open a new terminal (`cmd shift p` and search for `terminal create`). More shortcuts and Web IDE usage are documented [here](https://docs.gitlab.com/ee/user/project/web_ide/).\n\n![GitLab remote development workspaces, Python example, create terminal](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_python_web_ide_create_terminal.png)\n\nUsing the Python example project, try to run the `hello.py` file with the Python interpreter after changing the terminal to `bash` to access auto-completion and shell history. Type `pyth`, press tab, type `hel`, press tab, enter.\n\n```shell\n$ bash\n\n$ python hello.py\n```\n\nThe command will fail because the Python requirements still need to be installed. Let us fix that by running the following command:\n\n```shell\n$ pip install -r requirements.txt\n```\n\n![GitLab remote development workspaces, Python example, install requirements in the terminal](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_python_web_ide_terminal_install_pip.png)\n\n**Note**: This example is intentionally kept simple, and does not use best practices with `pyenv` for managing Python environments. We will explore development environment templates in future blog posts.\n\nRun the Python application `hello.py` again to start the web server on port 8080.\n\n```shell\n$ python hello.py\n```\n\nYou can access the exposed port by modifying the URL from the default port at the beginning of the URL to the exposed port `8080`. The `?folder` URL parameter can also be removed.\n\n```diff\n-https://60001-workspace-62029-5534214-kbtcmq.remote-dev.dev/?folder=/projects/example-python-http-simple\n+https://8080-workspace-62029-5534214-kbtcmq.remote-dev.dev/\n```\n\nThe URL is not publicly available and requires access through the GitLab OAuth session.\n\n![GitLab remote development workspaces, Python example, run webserver, access HTTP](https://about.gitlab.com/images/blogimages/infrastructure-cloud-development-environments/gitlab_remote_dev_workspaces_python_web_ide_terminal_run_webserver_access_http.png)\n\nModifying the workspace requires custom container images supporting to run with [arbitrary user IDs](https://docs.gitlab.com/ee/user/workspace/#arbitrary-user-ids). The example project uses a custom image which allows to install Python dependencies and create build artifacts. It also allows to use the bash terminal shown above. Learn more about custom image creation in the next section.\n\n### Custom workspace container images\nCustom container images require support for [arbitrary user IDs](https://docs.gitlab.com/ee/user/workspace/#arbitrary-user-ids). You can build custom container images with [GitLab CI/CD](/solutions/continuous-integration/) and use the [GitLab container registry](https://docs.gitlab.com/ee/user/packages/container_registry/) to distribute the container images on the DevSecOps platform.\n\nWorkspaces run with arbitrary user IDs in the Kubernetes cluster containers and manage resource access with Linux group permissions. Existing container images may need to be changed, and imported as base image for new container images. The [following example](https://gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id/-/blob/main/Dockerfile) uses the `python:3.11-slim-bullseye` image from Docker Hub as a base container image in the `FROM` key. The next steps create and set a home directory in `/home/gitlab-workspaces`, and manage user and group access to specified directories. Additionally, you can install more convenience tools and configurations into the image, for example the `git` package.\n\n[`Dockerfile`](https://gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id/-/blob/main/Dockerfile)\n```text\n# Example demo for a Python-based container image.\n# NOTE: THIS IMAGE IS NOT ACTIVELY MAINTAINED. DEMO USE CASES ONLY, DO NOT USE IN PRODUCTION.\n\nFROM python:3.11-slim-bullseye\n\n# User id for build time. Runtime will be an arbitrary random ID.\nRUN useradd -l -u 33333 -G sudo -md /home/gitlab-workspaces -s /bin/bash -p gitlab-workspaces gitlab-workspaces\n\nENV HOME=/home/gitlab-workspaces\n\nWORKDIR $HOME\n\nRUN mkdir -p /home/gitlab-workspaces && chgrp -R 0 /home && chmod -R g=u /etc/passwd /etc/group /home\n\n# TODO: Add more convenience tools into the user home directory, i.e. enable color prompt for the terminal, install pyenv to manage Python environments, etc\nRUN apt update && \\\n    apt -y --no-install-recommends install git procps findutils htop vim curl wget && \\\n    rm -rf /var/lib/apt/lists/*\n\nUSER gitlab-workspaces\n```\n\n **As an exercise**, [fork the project](https://gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id) and modify the package installation step in the `Dockerfile` file to install the `dnsutils` package on the Debian based image to get access to the `dig` command.\n\n[`Dockerfile`](https://gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id/-/blob/main/Dockerfile)\n```diff\n-RUN apt update && \\\n-    apt -y --no-install-recommends install git procps findutils htop vim curl wget && \\\n-    rm -rf /var/lib/apt/lists/*\n+RUN apt update && \\\n+    apt -y --no-install-recommends install git procps findutils htop vim curl wget dnsutils && \\\n+    rm -rf /var/lib/apt/lists/*\n```\n\n[Build the container image](https://docs.gitlab.com/ee/ci/docker/using_docker_build.html) with your preferred CI/CD workflow. On GitLab.com SaaS, you can include the `Docker.gitlab-ci.yml` template which takes care of building the image.\n\n```yaml\ninclude:\n    - template: Docker.gitlab-ci.yml\n```\n\nWhen building the container images manually, use Linux and `amd64` as platform architecture [until multi-architecture support is available for running workspaces](https://gitlab.com/groups/gitlab-org/-/epics/10594). Also, review the [optimizing images guide in the documentation](https://docs.gitlab.com/ee/ci/pipelines/pipeline_efficiency.html#optimize-docker-images) when creating custom container images to optimize size and build times.\n\nNavigate into `Deploy > Container Registry` in the GitLab UI and copy the image URL from the tagged image. Open the `.devfile.yaml` file in the forked GitLab project `example-python-http-simple`, and change the `image` path to the newly built image URL.\n\n[`.devfile.yaml`](https://gitlab.com/gitlab-da/use-cases/remote-development/example-python-http-simple/-/blob/main/.devfile.yaml)\n```diff\n-      image: registry.gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id:latest\n+      image: registry.gitlab.example.com/remote-dev-workspaces/python-remote-dev-workspaces-user-id:latest\n```\n\nNavigate into `Your Work > Workspaces` and create a new workspace for the project, and try to execute the `dig` command to query the IPv6 address of GitLab.com (or any other internal domain).\n\n```shell\n$ dig +short gitlab.com AAAA\n```\n\nThe custom container image project is located [here](https://gitlab.com/gitlab-da/use-cases/remote-development/container-images/python-remote-dev-workspaces-user-id/).\n\n## Tips\nThis blog post's setup steps with environment variables are easy to follow. For production usage, use automation to manage your environment with Terraform, Ansible, etc.\n\n- Terraform: [Provision a GKE Cluster (Google Cloud)](https://developer.hashicorp.com/terraform/tutorials/kubernetes/gke), [Provision an EKS Cluster (AWS)](https://developer.hashicorp.com/terraform/tutorials/kubernetes/eks), [Provision an AKS Cluster (Azure)](https://developer.hashicorp.com/terraform/tutorials/kubernetes/aks), [Deploy Applications with the Helm Provider](https://developer.hashicorp.com/terraform/tutorials/kubernetes/helm-provider)\n- Ansible: [google.cloud.gcp_container_cluster module](https://docs.ansible.com/ansible/latest/collections/google/cloud/gcp_container_cluster_module.html), [community.aws.eks_cluster module](https://docs.ansible.com/ansible/latest/collections/community/aws/eks_cluster_module.html), [azure.azcollection.azure_rm_aks module](https://docs.ansible.com/ansible/latest/collections/azure/azcollection/azure_rm_aks_module.html), [kubernetes.core collection](https://docs.ansible.com/ansible/latest/collections/kubernetes/core/index.html#plugin-index)\n\n### Certificate management\nThe workspaces domain requires a valid TLS certificate. The examples above used certbot with Let's Encrypt, requiring a certificate renewal after three months. Depending on your corporate requirements, you may need to create TLS certificates signed by the corporate CA identity and manage the certificates. Alternatively, you can look into solutions like [cert-manager for Kubernetes](https://cert-manager.io/docs/getting-started/) that will help renew certificates automatically.\n\nDo not forget to add TLS certificate validity monitoring to avoid unforeseen errors. The [blackbox exporter for Prometheus](https://github.com/prometheus/blackbox_exporter) can help with monitoring TLS certificate expiry and send alerts.\n\n### Troubleshooting\nHere are a few tips for troubleshooting connections and inspecting the cluster resources.\n\n#### Verify the connections\nTry to connect to the workspaces domain to see whether the Kubernetes Ingress controller responds to HTTP requests.\n\n```shell\n$ curl -vL ${GITLAB_WORKSPACES_PROXY_DOMAIN}\n```\n\nInspect the logs of the proxy deployment to follow connection requests. Since the proxy requires an authorization token sent via the OAuth app, an HTTP 400 error is expected for unauthenticated curl requests.\n\n```shell\n$ kubectl logs -f -l app.kubernetes.io/name=gitlab-workspaces-proxy -n gitlab-workspaces\n```\n\nCheck if the TLS certificate is valid. You can also use `sslcan` and other tools.\n\n```shell\n$ openssl s_client -connect ${GITLAB_WORKSPACES_PROXY_DOMAIN}:443\n\n$ sslcan ${GITLAB_WORKSPACES_PROXY_DOMAIN}\n```\n\n[Debug the agent for Kubernetes](https://docs.gitlab.com/ee/user/clusters/agent/work_with_agent.html#debug-the-agent) and inspect the pod logs.\n\n```shell\n$ kubectl get ns\n\n$ kubectl logs -f -l app.kubernetes.io/name=gitlab-agent -n gitlab-agent-\u003CNAMESPACENAME>\n```\n\n#### Workspaces cannot be created even if the agent is connected\nWhen the workspaces deployment is spinning and nothing happens, try restarting the workspaces proxy and agent for Kubernetes. This is a known problem and tracked [in this issue](https://gitlab.com/gitlab-org/gitlab/-/issues/414399#note_1426652421).\n\n```shell\n$ kubectl rollout restart deployment -n gitlab-workspaces\n\n$ kubectl rollout restart deployment -n gitlab-agent-$GL_AGENT_K8S\n```\n\nIf the agent for Kubernetes remains unresponsive, consider a complete reinstall. First, navigate into the GitLab UI into `Operate > Kubernetes Clusters` and [delete the agent](https://docs.gitlab.com/ee/user/clusters/agent/work_with_agent.html#remove-an-agent-through-the-gitlab-ui). Next, use the following commands to delete the Helm release from the cluster, and run the installation command generated from the UI again.\n\n```shell\nkubectl get ns\nhelm list -A\n\nexport RELEASENAME=xxx\nexport NAMESPACENAME=xxx\nexport TOKEN=XXXXXXXXXXREPLACEME\nhelm uninstall ${RELEASENAME} -n gitlab-agent-${NAMESPACENAME}\n\nhelm repo add gitlab https://charts.gitlab.io\nhelm repo update\n\nhelm upgrade --install ${RELEASENAME} gitlab/gitlab-agent \\\n    --namespace gitlab-agent-${NAMESPACENAME} \\\n    --create-namespace \\\n    --set image.tag=v16.1.2 \\\n    --set config.token=${TOKEN} \\\n    --set config.kasAddress=wss://kas.gitlab.com # Replace with your self-managed GitLab KAS instance URL if not using GitLab.com SaaS\n```\n\nExample: `helm uninstall remote-dev-dev -n gitlab-agent-remote-dev-dev`\n\n#### Cannot modify workspace using custom images\nIf you cannot modify the workspace, open a new terminal and check the user id and their groups.\n\n```shell\n$ id\n```\n\nInspect the `.devfile.yaml` file in the project and extract the `image` attribute to test the used container image. You can use container CLI, for example `docker` that runs the container with a different user ID. Note: You can use any user ID to test the behavior.\n\nTip: Use grep and cut commands to extract the image attribute URL from the `.devfile.yaml`.\n\n```shell\n$ cat .devfile.yaml | grep image: | cut -f2 -d ':')\n```\n\nRun the following command to execute the `id` command in the container, and print the user information.\n\n```shell\n$ docker run -u 1234 -ti registry.gitlab.com/path/to/project/image:tagname id\n```\n\nTry to modify the workspace by running the command `echo 'Hi' >> ~/example.md`. This can fail with a permission error.\n\n```shell\n$ docker run -u 1234 -ti registry.gitlab.com/path/to/project/image:tagname echo 'Hi' >> ~/example.md\n```\n\nIf the above command failed, the Linux user group does not have enough permissions to modify the file. You can view the permissions using the `ls` command.\n\n```shell\n$ docker run -u 1234 -ti registry.gitlab.com/path/to/project/image:tagname ls -lart ~/\n```\n\n### Contribute\nThe [remote development developer documentation](https://gitlab.com/gitlab-org/remote-development/gitlab-remote-development-docs) provides insights into the [architecture blueprint](https://docs.gitlab.com/ee/architecture/blueprints/remote_development/) and how to set up a local development environment to [start contributing](/community/contribute/). In the future, we will be able to use remote development workspaces to develop remote development workspaces.\n\n## Share your feedback\nIn this blog post, you have learned how to manage the infrastructure for remote development workspaces, create your first workspace, and more tips on custom workspace images and troubleshooting. Using the same development environment across organizations and communities, developers can focus on writing code and get fast preview feedback (i.e., by running a web server that can be accessed externally in the remote workspace). Providing the same reproducible environment also helps opensource contributors to reproduce bugs and provide feedback most efficiently. They can use the same best practices as upstream maintainers.\n\nDevelopers and DevOps engineers will be using the Web IDE in workspaces. Later, being able to [connect their desktop client to workspaces](https://gitlab.com/groups/gitlab-org/-/epics/10478), they can take advantage of even more efficiency with the [most comprehensive AI-powered DevSecOps platform](/gitlab-duo-agent-platform/): Code suggestions and more AI-powered workflows are just one fingertip away.\n\nWhat will your teams build with remote development workspaces? 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CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[722],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[112,726,727,728],"DevOps platform","tutorial","features",{"featured":30,"template":13,"slug":730},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":732,"config":742},{"title":733,"description":734,"authors":735,"heroImage":737,"date":738,"body":739,"category":9,"tags":740},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[736],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[727,741,728],"product",{"featured":12,"template":13,"slug":743},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":745,"config":755},{"title":746,"description":747,"authors":748,"heroImage":750,"date":751,"category":9,"tags":752,"body":754},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[749],"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",[265,623,753],"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-agent-platform/) 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":756,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":758},[759,773,784,796],{"id":760,"categories":761,"header":763,"text":764,"button":765,"image":770},"ai-modernization",[762],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":766,"config":767},"Get your AI maturity score",{"href":768,"dataGaName":769,"dataGaLocation":247},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":771},{"src":772},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":774,"categories":775,"header":776,"text":764,"button":777,"image":781},"devops-modernization",[741,570],"Are you just managing tools or shipping innovation?",{"text":778,"config":779},"Get your DevOps maturity score",{"href":780,"dataGaName":769,"dataGaLocation":247},"/assessments/devops-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":785,"categories":786,"header":788,"text":764,"button":789,"image":793},"security-modernization",[787],"security","Are you trading speed for security?",{"text":790,"config":791},"Get your security maturity score",{"href":792,"dataGaName":769,"dataGaLocation":247},"/assessments/security-modernization-assessment/",{"config":794},{"src":795},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":797,"paths":798,"header":801,"text":802,"button":803,"image":808},"github-azure-migration",[799,800],"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":804,"config":805},"See how GitLab compares to GitHub",{"href":806,"dataGaName":807,"dataGaLocation":247},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":809},{"src":783},{"header":811,"blurb":812,"button":813,"secondaryButton":818},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":814,"config":815},"Get your free trial",{"href":816,"dataGaName":54,"dataGaLocation":817},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":509,"config":819},{"href":58,"dataGaName":59,"dataGaLocation":817},1776442989559]