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Bereitstellen von Runner-Skalierungsgruppen mit Actions Runner Controller

Hier erfährst du, wie du Runner-Skalierungsgruppen mit Actions Runner Controller bereitstellst und Actions Runner Controller mithilfe von erweiterten Konfigurationsoptionen an deine Anforderungen anpasst.

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About runner scale sets

Runner scale sets is a group of homogeneous runners that can be assigned jobs from GitHub Actions. The number of active runners owned by a runner scale set can be controlled by auto-scaling runner solutions such as Actions Runner Controller (ARC).

You can use runner groups to manage runner scale sets. Similar to self-hosted runners, you can add runner scale sets to existing runner groups. However, runner scale sets can belong to only one runner group at a time and cannot have labels assigned to them. For more information on runner groups, see "Managing access to self-hosted runners using groups."

To assign jobs to a runner scale set, you must configure your workflow to reference the runner scale set's name. For more information, see "Using Actions Runner Controller runners in a workflow."

Deploying a runner scale set

To deploy a runner scale set, you must have ARC up and running. For more information, see "Quickstart for Actions Runner Controller."

You can deploy runner scale sets with ARC's Helm charts or by deploying the necessary manifests. Using ARC's Helm charts is the preferred method, especially if you do not have prior experience using ARC.

Notes:

  • As a security best practice, create your runner pods in a different namespace than the namespace containing your operator pods.
  • As a security best practice, create Kubernetes secrets and pass the secret references. Passing your secrets in plain text via the CLI can pose a security risk.
  • We recommend running production workloads in isolation. GitHub Actions workflows are designed to run arbitrary code, and using a shared Kubernetes cluster for production workloads could pose a security risk.
  1. To configure your runner scale set, run the following command in your terminal, using values from your ARC configuration.

    When you run the command, keep the following in mind.

    • Update the INSTALLATION_NAME value carefully. You will use the installation name as the value of runs-on in your workflows.

    • Update the NAMESPACE value to the location you want the runner pods to be created.

    • Set the GITHUB_CONFIG_URL value to the URL of your repository, organization, or enterprise. This is the entity that the runners will belong to.

    • This example command installs the latest version of the Helm chart. To install a specific version, you can pass the --version argument with the version of the chart you want to install. You can find the list of releases in the actions-runner-controller repository.

      Bash
      INSTALLATION_NAME="arc-runner-set"
      NAMESPACE="arc-runners"
      GITHUB_CONFIG_URL="https://github.com/<your_enterprise/org/repo>"
      GITHUB_PAT="<PAT>"
      helm install "${INSTALLATION_NAME}" \
          --namespace "${NAMESPACE}" \
          --create-namespace \
          --set githubConfigUrl="${GITHUB_CONFIG_URL}" \
          --set githubConfigSecret.github_token="${GITHUB_PAT}" \
          oci://ghcr.io/actions/actions-runner-controller-charts/gha-runner-scale-set
      

      For additional Helm configuration options, see values.yaml in the ARC repository.

  2. To check your installation, run the following command in your terminal.

    Bash
    helm list -A
    

    You should see an output similar to the following.

    NAME            NAMESPACE       REVISION        UPDATED                                 STATUS          CHART                                       APP VERSION
    arc             arc-systems     1               2023-04-12 11:45:59.152090536 +0000 UTC deployed        gha-runner-scale-set-controller-0.4.0       0.4.0
    arc-runner-set  arc-systems     1               2023-04-12 11:46:13.451041354 +0000 UTC deployed        gha-runner-scale-set-0.4.0                  0.4.0
    
  3. To check the manager pod, run the following command in your terminal.

    Bash
    kubectl get pods -n arc-systems
    

    If the installation was successful, the pods will show the Running status.

    NAME                                                   READY   STATUS    RESTARTS   AGE
    arc-gha-runner-scale-set-controller-594cdc976f-m7cjs   1/1     Running   0          64s
    arc-runner-set-754b578d-listener                       1/1     Running   0          12s
    

If your installation was not successful, see "Troubleshooting Actions Runner Controller errors" for troubleshooting information.

Using advanced configuration options

ARC offers several advanced configuration options.

Configuring the runner scale set name

Note: Runner scale set names are unique within the runner group they belong to. If you want to deploy multiple runner scale sets with the same name, they must belong to different runner groups.

To configure the runner scale set name, you can define an INSTALLATION_NAME or set the value of runnerScaleSetName in your copy of the values.yaml file.

## The name of the runner scale set to create, which defaults to the Helm release name
runnerScaleSetName: "my-runners"

Make sure to pass the values.yaml file in your helm install command. See the Helm Install documentation for more details.

Choosing runner destinations

Runner scale sets can be deployed at the repository, organization, or enterprise levels.

To deploy runner scale sets to a specific level, set the value of githubConfigUrl in your copy of the values.yaml to the URL of your repository, organization, or enterprise.

The following example shows how to configure ARC to add runners to octo-org/octo-repo.

githubConfigUrl: "https://github.com/octo-ent/octo-org/octo-repo"

For additional Helm configuration options, see values.yaml in the ARC repository.

Using a GitHub App for authentication

If you are not using enterprise-level runners, you can use GitHub Apps to authenticate with the GitHub API. For more information, see "Authenticating to the GitHub API."

Note: Given the security risk associated with exposing your private key in plain text in a file on disk, we recommend creating a Kubernetes secret and passing the reference instead.

You can either create a Kubernetes secret, or specify values in your values.yaml file.

Once you have created your GitHub App, create a Kubernetes secret and pass the reference to that secret in your copy of the values.yaml file.

Note: Create the secret in the same namespace where the gha-runner-scale-set chart is installed. In this example, the namespace is arc-runners to match the quickstart documentation. For more information, see "Quickstart for Actions Runner Controller."

kubectl create secret generic pre-defined-secret \
  --namespace=arc-runners \
  --from-literal=github_app_id=123456 \
  --from-literal=github_app_installation_id=654321 \
  --from-literal=github_app_private_key='-----BEGIN RSA PRIVATE KEY-----********'

In your copy of the values.yaml pass the secret name as a reference.

githubConfigSecret: pre-defined-secret

Option 2: Specify values in your values.yaml file

Alternatively, you can specify the values of app_id, installation_id and private_key in your copy of the values.yaml file.

## githubConfigSecret is the Kubernetes secret to use when authenticating with GitHub API.
## You can choose to use a GitHub App or a personal access token (classic)
githubConfigSecret:
  ## GitHub Apps Configuration
  ## IDs must be strings, use quotes
  github_app_id: "123456"
  github_app_installation_id: "654321"
  github_app_private_key: |
    -----BEGIN RSA PRIVATE KEY-----
    ...
    HkVN9...
    ...
    -----END RSA PRIVATE KEY-----

For additional Helm configuration options, see values.yaml in the ARC repository.

Managing access with runner groups

You can use runner groups to control which organizations or repositories have access to your runner scale sets. For more information on runner groups, see "Managing access to self-hosted runners using groups."

To add a runner scale set to a runner group, you must already have a runner group created. Then set the runnerGroup property in your copy of the values.yaml file. The following example adds a runner scale set to the Octo-Group runner group.

runnerGroup: "Octo-Group"

For additional Helm configuration options, see values.yaml in the ARC repository.

Configuring an outbound proxy

To force HTTP traffic for the controller and runners to go through your outbound proxy, set the following properties in your Helm chart.

proxy:
  http:
    url: http://proxy.com:1234
    credentialSecretRef: proxy-auth # a Kubernetes secret with `username` and `password` keys
  https:
    url: http://proxy.com:1234
    credentialSecretRef: proxy-auth # a Kubernetes secret with `username` and `password` keys
  noProxy:
    - example.com
    - example.org

ARC supports using anonymous or authenticated proxies. If you use authenticated proxies, you will need to set the credentialSecretRef value to reference a Kubernetes secret. You can create a secret with your proxy credentials with the following command.

Note: Create the secret in the same namespace where the gha-runner-scale-set chart is installed. In this example, the namespace is arc-runners to match the quickstart documentation. For more information, see "Quickstart for Actions Runner Controller."

Bash
  kubectl create secret generic proxy-auth \
    --namespace=arc-runners \
    --from-literal=username=proxyUsername \
    --from-literal=password=proxyPassword \

For additional Helm configuration options, see values.yaml in the ARC repository.

Setting the maximum and minimum number of runners

The maxRunners and minRunners properties provide you with a range of options to customize your ARC setup.

Note: ARC does not support scheduled maximum and minimum configurations. You can use a cronjob or any other scheduling solution to update the configuration on a schedule.

Example: Unbounded number of runners

If you comment out both the maxRunners and minRunners properties, ARC will scale up to the number of jobs assigned to the runner scale set and will scale down to 0 if there aren't any active jobs.

## maxRunners is the max number of runners the auto scaling runner set will scale up to.
# maxRunners: 0

## minRunners is the min number of runners the auto scaling runner set will scale down to.
# minRunners: 0

Example: Minimum number of runners

You can set the minRunners property to any number and ARC will make sure there is at least this number of runners active and available to take jobs assigned to the runner scale set at all times.

## maxRunners is the max number of runners the auto scaling runner set will scale up to.
# maxRunners: 0

## minRunners is the min number of runners the auto scaling runner set will scale down to.
minRunners: 20

Example: Set maximum and minimum number of runners

In this configuration, Actions Runner Controller will scale up to a maximum of 30 runners and will scale down to 20 runners when the jobs are complete.

Note: The value of minRunners can never exceed that of maxRunners, unless maxRunners is commented out.

## maxRunners is the max number of runners the auto scaling runner set will scale up to.
maxRunners: 30

## minRunners is the min number of runners the auto scaling runner set will scale down to.
minRunners: 20

Example: Jobs queue draining

In certain scenarios you might want to drain the jobs queue to troubleshoot a problem or to perform maintenance on your cluster. If you set both properties to 0, Actions Runner Controller will not create new runner pods when new jobs are available and assigned.

## maxRunners is the max number of runners the auto scaling runner set will scale up to.
maxRunners: 0

## minRunners is the min number of runners the auto scaling runner set will scale down to.
minRunners: 0

Custom TLS certificates

Note: If you are using a custom runner image that is not based on the Debian distribution, the following instructions will not work.

Some environments require TLS certificates that are signed by a custom certificate authority (CA). Since the custom certificate authority certificates are not bundled with the controller or runner containers, you must inject them into their respective trust stores.

githubServerTLS:
  certificateFrom:
    configMapKeyRef:
      name: config-map-name
      key: ca.crt
  runnerMountPath: /usr/local/share/ca-certificates/

When you do this, ensure you are using the Privacy Enhanced Mail (PEM) format and that the extension of your certificate is .crt. Anything else will be ignored.

The controller executes the following actions.

  • Creates a github-server-tls-cert volume containing the certificate specified in certificateFrom.
  • Mounts that volume on path runnerMountPath/<certificate name>.
  • Sets the NODE_EXTRA_CA_CERTS environment variable to that same path.
  • Sets the RUNNER_UPDATE_CA_CERTS environment variable to 1 (as of version 2.303.0, this will instruct the runner to reload certificates on the host).

ARC observes values set in the runner pod template and does not overwrite them.

For additional Helm configuration options, see values.yaml in the ARC repository.

Using Docker-in-Docker or Kubernetes mode for containers

If you are using container jobs and services or container actions, the containerMode value must be set to dind or kubernetes.

Using Docker-in-Docker mode

Note: The Docker-in-Docker container requires privileged mode. For more information, see Configure a Security Context for a Pod or Container in the Kubernetes documentation.

Docker-in-Docker mode is a configuration that allows you to run Docker inside a Docker container. In this configuration, for each runner pod-created ARC creates the following containers.

  • An init container
  • A runner container
  • A dind container

To enable Docker-in-Docker mode, set the containerMode.type to dind as follows.

containerMode:
  type: "dind"

The template.spec will be updated to the following default configuration.

template:
  spec:
    initContainers:
    - name: init-dind-externals
      image: ghcr.io/actions/actions-runner:latest
      command: ["cp", "-r", "-v", "/home/runner/externals/.", "/home/runner/tmpDir/"]
      volumeMounts:
        - name: dind-externals
          mountPath: /home/runner/tmpDir
    containers:
    - name: runner
      image: ghcr.io/actions/actions-runner:latest
      command: ["/home/runner/run.sh"]
      env:
        - name: DOCKER_HOST
          value: unix:///run/docker/docker.sock
      volumeMounts:
        - name: work
          mountPath: /home/runner/_work
        - name: dind-sock
          mountPath: /run/docker
          readOnly: true
    - name: dind
      image: docker:dind
      args:
        - dockerd
        - --host=unix:///run/docker/docker.sock
        - --group=$(DOCKER_GROUP_GID)
      env:
        - name: DOCKER_GROUP_GID
          value: "123"
      securityContext:
        privileged: true
      volumeMounts:
        - name: work
          mountPath: /home/runner/_work
        - name: dind-sock
          mountPath: /run/docker
        - name: dind-externals
          mountPath: /home/runner/externals
    volumes:
    - name: work
      emptyDir: {}
    - name: dind-sock
      emptyDir: {}
    - name: dind-externals
      emptyDir: {}

You cannot override these automatically injected values. If you want to customize this setup, you must unset containerMode.type, then copy this configuration and apply it directly in your copy of the values.yaml file.

For additional Helm configuration options, see values.yaml in the ARC repository.

Using Kubernetes mode

In Kubernetes mode, ARC uses runner container hooks to create a new pod in the same namespace to run the service, container job, or action.

Prerequisites

Kubernetes mode relies on persistent volumes to share job details between the runner pod and the container job pod. See the Persistent Volumes Kubernetes documentation for more information.

To use Kubernetes mode, you must do the following.

  • Create persistent volumes available for the runner pods to claim.
  • Use a solution to automatically provision persistent volumes on demand.

For testing, you can use a solution like OpenEBS.

Configuring Kubernetes mode

To enable Kubernetes mode, set the containerMode.type to kubernetes.

containerMode:
  type: "kubernetes"
  kubernetesModeWorkVolumeClaim:
    accessModes: ["ReadWriteOnce"]
    storageClassName: "dynamic-blob-storage"
    resources:
      requests:
        storage: 1Gi

For additional Helm configuration options, see values.yaml in the ARC repository.

When Kubernetes mode is enabled, workflows that are not configured with a container job will fail with an error similar to:

Jobs without a job container are forbidden on this runner, please add a 'container:' to your job or contact your self-hosted runner administrator.

In order to allow jobs without a job container to run, you need to instruct the runner to disable this check. You can do that by setting ACTIONS_RUNNER_REQUIRE_JOB_CONTAINER to false on your runner container:

template:
  spec:
    containers:
    - name: runner
      image: ghcr.io/actions/actions-runner:latest
      command: ["/home/runner/run.sh"]
      env:
        - name: ACTIONS_RUNNER_REQUIRE_JOB_CONTAINER
          value: "false"

Using a private container registry

To use a private container registry, you can copy the controller image and runner image to your private container registry. Then configure the links to those images and set the imagePullPolicy and imagePullSecrets values.

Configuring the controller image

You can update your copy of the values.yaml file and set the image properties as follows.

image:
  repository: "custom-registry.io/gha-runner-scale-set-controller"
  pullPolicy: IfNotPresent
  # Overrides the image tag whose default is the chart appVersion.
  tag: "0.4.0"

imagePullSecrets:
- name: <registry-secret-name>

The listener container inherits the imagePullPolicy defined for the controller.

Configuring the runner image

You can update your copy of the values.yaml file and set the template.spec properties as follows.

template:
  spec:
    containers:
    - name: runner
      image: "custom-registry.io/actions-runner:latest"
      imagePullPolicy: Always
      command: ["/home/runner/run.sh"]

For additional Helm configuration options, see values.yaml in the ARC repository.

Updating the pod specification for the runner pod

You can fully customize the PodSpec of the runner pod and the controller will apply the configuration you specify. The following is an example pod specification.

template:
  spec:
    containers:
    - name: runner
      image: ghcr.io/actions/actions-runner:latest
      command: ["/home/runner/run.sh"]
      resources:
        limits:
          cpu: 500m
          memory: 512Mi
      securityContext:
        readOnlyRootFilesystem: true
        allowPrivilegeEscalation: false
        capabilities:
          add:
          - NET_ADMIN

For additional Helm configuration options, see values.yaml in the ARC repository.

Enabling metrics

Note: Metrics for ARC are available as of version gha-runner-scale-set-0.5.0.

ARC can emit metrics about your runners, your jobs, and time spent on executing your workflows. Metrics can be used to identify congestion, monitor the health of your ARC deployment, visualize usage trends, optimize resource consumption, among many other use cases. Metrics are emitted by the controller-manager and listener pods in Prometheus format. For more information, see Exposition formats in the Prometheus documentation.

To enable metrics for ARC, configure the metrics property in the values.yaml file of the gha-runner-scale-set-controller chart.

The following is an example configuration.

metrics:
  controllerManagerAddr: ":8080"
  listenerAddr: ":8080"
  listenerEndpoint: "/metrics"

Note: If the metrics: object is not provided or is commented out, the following flags will be applied to the controller-manager and listener pods with empty values: --metrics-addr, --listener-metrics-addr, --listener-metrics-endpoint. This will disable metrics for ARC.

Once these properties are configured, your controller-manager and listener pods emit metrics via the listenerEndpoint bound to the ports that you specify in your values.yaml file. In the above example, the endpoint is /metrics and the port is :8080. You can use this endpoint to scrape metrics from your controller-manager and listener pods.

To turn off metrics, update your values.yaml file by removing or commenting out the metrics: object and its properties.

Available metrics for ARC

The following table shows the metrics emitted by the controller-manager and listener pods.

Note: The metrics that the controller-manager emits pertain to the controller runtime and are not owned by GitHub.

OwnerMetricTypeDescription
controller-managerpending_ephemeral_runnersgaugeNumber of ephemeral runners in a pending state
controller-managerrunning_ephemeral_runnersgaugeNumber of ephemeral runners in a running state
controller-managerfailed_ephemeral_runnersgaugeNumber of ephemeral runners in a failed state
listenerassigned_jobsgaugeNumber of jobs assigned to the runner scale set
listenerrunning_jobsgaugeNumber of jobs running or queued to run
listenerregistered_runnersgaugeNumber of runners registered by the runner scale set
listenerbusy_runnersgaugeNumber of registered runners currently running a job
listenermin_runnersgaugeMinimum number of runners configured for the runner scale set
listenermax_runnersgaugeMaximum number of runners configured for the runner scale set
listenerdesired_runnersgaugeNumber of runners desired (scale up / down target) by the runner scale set
listeneridle_runnersgaugeNumber of registered runners not running a job
listenerstarted_jobs_totalcounterTotal number of jobs started since the listener became ready [1]
listenercompleted_jobs_totalcounterTotal number of jobs completed since the listener became ready [1]
listenerjob_queue_duration_secondshistogramNumber of seconds spent waiting for workflow jobs to get assigned to the runner scale set after queueing
listenerjob_startup_duration_secondshistogramNumber of seconds spent waiting for workflow job to get started on the runner owned by the runner scale set
listenerjob_execution_duration_secondshistogramNumber of seconds spent executing workflow jobs by the runner scale set

[1]: Listener metrics that have the counter type are reset when the listener pod restarts.

High availability and automatic failover

ARC can be deployed in a high availability (active-active) configuration. If you have two distinct Kubernetes clusters deployed in separate regions, you can deploy ARC in both clusters and configure runner scale sets to use the same runnerScaleSetName. In order to do this, each runner scale set must be assigned to a distinct runner group. For example, you can have two runner scale sets each named arc-runner-set, as long as one runner scale set belongs to runner-group-A and the other runner scale set belongs to runner-group-B. For information on assigning runner scale sets to runner groups, see "Managing access to self-hosted runners using groups."

If both runner scale sets are online, jobs assigned to them will be distributed arbitrarily (assignment race). You cannot configure the job assignment algorithm. If one of the clusters goes down, the runner scale set in the other cluster will continue to acquire jobs normally without any intervention or configuration change.

Using ARC across organizations

A single installation of Actions Runner Controller allows you to configure one or more runner scale sets. These runner scale sets can be registered to a repository, organization, or enterprise. You can also use runner groups to control the permissions boundaries of these runner scale sets.

As a best practice, create a unique namespace for each organization. You could also create a namespace for each runner group or each runner scale set. You can install as many runner scale sets as needed in each namespace. This will provide you the highest levels of isolation and improve your security. You can use GitHub Apps for authentication and define granular permissions for each runner scale set.

Portions have been adapted from https://github.com/actions/actions-runner-controller/ under the Apache-2.0 license:

Copyright 2019 Moto Ishizawa

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.