actions-runner-controller

This controller operates self-hosted runners for GitHub Actions on your Kubernetes cluster.

ToC:

Motivation
Installation
- GitHub Enterprise Support
Setting Up Authentication with GitHub API
- Deploying Using GitHub App Authentication
- Deploying Using PAT Authentication
Deploying Multiple Controllers
Usage
Troubleshooting
Contributing

Motivation

GitHub Actions is a very useful tool for automating development. GitHub Actions jobs are run in the cloud by default, but you may want to run your jobs in your environment. Self-hosted runner can be used for such use cases, but requires the provisioning and configuration of a virtual machine instance. Instead if you already have a Kubernetes cluster, it makes more sense to run the self-hosted runner on top of it.

actions-runner-controller makes that possible. Just create a Runner resource on your Kubernetes, and it will run and operate the self-hosted runner for the specified repository. Combined with Kubernetes RBAC, you can also build simple Self-hosted runners as a Service.

Installation

By default, actions-runner-controller uses cert-manager for certificate management of Admission Webhook. Make sure you have already installed cert-manager before you install. The installation instructions for cert-manager can be found below.

Installing cert-manager on Kubernetes

Subsequent to this, install the custom resource definitions and actions-runner-controller with kubectl or helm. This will create actions-runner-system namespace in your Kubernetes and deploy the required resources.

Kubectl Deployment:

# REPLACE "v0.22.0" with the version you wish to deploy
kubectl apply -f https://github.com/actions-runner-controller/actions-runner-controller/releases/download/v0.22.0/actions-runner-controller.yaml

Helm Deployment:

Configure your values.yaml, see the chart's README for the values documentation

helm repo add actions-runner-controller https://actions-runner-controller.github.io/actions-runner-controller
helm upgrade --install --namespace actions-runner-system --create-namespace \
             --wait actions-runner-controller actions-runner-controller/actions-runner-controller

GitHub Enterprise Support

The solution supports both GHEC (GitHub Enterprise Cloud) and GHES (GitHub Enterprise Server) editions as well as regular GitHub. Both PAT (personal access token) and GitHub App authentication works for installations that will be deploying either repository level and / or organization level runners. If you need to deploy enterprise level runners then you are restricted to PAT based authentication as GitHub doesn't support GitHub App based authentication for enterprise runners currently.

If you are deploying this solution into a GHES environment then you will need to be running version >= 3.3.0.

When deploying the solution for a GHES environment you need to provide an additional environment variable as part of the controller deployment:

kubectl set env deploy controller-manager -c manager GITHUB_ENTERPRISE_URL=<GHEC/S URL> --namespace actions-runner-system

Note: The repository maintainers do not have an enterprise environment (cloud or server). Support for the enterprise specific feature set is community driven and on a best effort basis. PRs from the community are welcomed to add features and maintain support.

Setting Up Authentication with GitHub API

There are two ways for actions-runner-controller to authenticate with the GitHub API (only 1 can be configured at a time however):

Using a GitHub App (not supported for enterprise level runners due to lack of support from GitHub)
Using a PAT

Functionality wise, there isn't much of a difference between the 2 authentication methods. The primarily benefit of authenticating via a GitHub App is an increased API quota.

If you are deploying the solution for a GHES environment you are able to configure your rate limit settings making the main benefit irrelevant. If you're deploying the solution for a GHEC or regular GitHub environment and you run into rate limit issues, consider deploying the solution using the GitHub App authentication method instead.

Deploying Using GitHub App Authentication

You can create a GitHub App for either your user account or any organization, below are the app permissions required for each supported type of runner:

Note: Links are provided further down to create an app for your logged in user account or an organization with the permissions for all runner types set in each link's query string

Required Permissions for Repository Runners:
Repository Permissions

Actions (read)
Administration (read / write)
Checks (read) (if you are going to use Webhook Driven Scaling)
Metadata (read)

Required Permissions for Organization Runners:
Repository Permissions

Actions (read)
Metadata (read)

Organization Permissions

Self-hosted runners (read / write)

Note: All API routes mapped to their permissions can be found here if you wish to review

Subscribe to events

At this point you have a choice of configuring a webhook, a webhook is needed if you are going to use webhook driven scaling. The webhook can be configured centrally in the GitHub app itself or separately. In either case the event details are:

Check run (required for all webhook driven scaling events)
Workflow job (optionally) (required for webhook driven scaling with workflow_job events

Setup Steps

If you want to create a GitHub App for your account, open the following link to the creation page, enter any unique name in the "GitHub App name" field, and hit the "Create GitHub App" button at the bottom of the page.

Create GitHub Apps on your account

If you want to create a GitHub App for your organization, replace the :org part of the following URL with your organization name before opening it. Then enter any unique name in the "GitHub App name" field, and hit the "Create GitHub App" button at the bottom of the page to create a GitHub App.

Create GitHub Apps on your organization

You will see an App ID on the page of the GitHub App you created as follows, the value of this App ID will be used later.

Download the private key file by pushing the "Generate a private key" button at the bottom of the GitHub App page. This file will also be used later.

Go to the "Install App" tab on the left side of the page and install the GitHub App that you created for your account or organization.

When the installation is complete, you will be taken to a URL in one of the following formats, the last number of the URL will be used as the Installation ID later (For example, if the URL ends in settings/installations/12345, then the Installation ID is 12345).

https://github.com/settings/installations/${INSTALLATION_ID}
https://github.com/organizations/eventreactor/settings/installations/${INSTALLATION_ID}

Finally, register the App ID (APP_ID), Installation ID (INSTALLATION_ID), and downloaded private key file (PRIVATE_KEY_FILE_PATH) to Kubernetes as Secret.

Kubectl Deployment:

$ kubectl create secret generic controller-manager \
    -n actions-runner-system \
    --from-literal=github_app_id=${APP_ID} \
    --from-literal=github_app_installation_id=${INSTALLATION_ID} \
    --from-file=github_app_private_key=${PRIVATE_KEY_FILE_PATH}

Helm Deployment:

Configure your values.yaml, see the chart's README for deploying the secret via Helm

Deploying Using PAT Authentication

Personal Access Tokens can be used to register a self-hosted runner by actions-runner-controller.

Log-in to a GitHub account that has admin privileges for the repository, and create a personal access token with the appropriate scopes listed below:

Required Scopes for Repository Runners

repo (Full control)

Required Scopes for Organization Runners

repo (Full control)
admin:org (Full control)
admin:public_key (read:public_key)
admin:repo_hook (read:repo_hook)
admin:org_hook (Full control)
notifications (Full control)
workflow (Full control)

Required Scopes for Enterprise Runners

admin:enterprise (manage_runners:enterprise)

Note: When you deploy enterprise runners they will get access to organizations, however, access to the repositories themselves is NOT allowed by default. Each GitHub organization must allow enterprise runner groups to be used in repositories as an initial one time configuration step, this only needs to be done once after which it is permanent for that runner group.

Note: GitHub do not document exactly what permissions you get with each PAT scope beyond a vague description. The best documentation they provide on the topic can be found here if you wish to review. The docs target OAuth apps and so are incomplete and amy not be 100% accurate.

Once you have created the appropriate token, deploy it as a secret to your Kubernetes cluster that you are going to deploy the solution on:

Kubectl Deployment:

kubectl create secret generic controller-manager \
    -n actions-runner-system \
    --from-literal=github_token=${GITHUB_TOKEN}

Helm Deployment:

Configure your values.yaml, see the chart's README for deploying the secret via Helm

Deploying Multiple Controllers

This feature requires controller version => v0.18.0

Note: Be aware when using this feature that CRDs are cluster wide and so you should upgrade all of your controllers (and your CRDs) as the same time if you are doing an upgrade. Do not mix and match CRD versions with different controller versions. Doing so risks out of control scaling.

By default the controller will look for runners in all namespaces, the watch namespace feature allows you to restrict the controller to monitoring a single namespace. This then lets you deploy multiple controllers in a single cluster. You may want to do this either because you wish to scale beyond the API rate limit of a single PAT / GitHub App configuration or you wish to support multiple GitHub organizations with runners installed at the organization level in a single cluster.

This feature is configured via the controller's --watch-namespace flag. When a namespace is provided via this flag, the controller will only monitor runners in that namespace.

You can deploy multiple controllers either in a single shared namespace, or in a unique namespace per controller.

If you plan on installing all instances of the controller stack into a single namespace there are a few things you need to do for this to work.

All resources per stack must have a unique, in the case of Helm this can be done by giving each install a unique release name, or via the fullnameOverride properties.
authSecret.name needs be unique per stack when each stack is tied to runners in different GitHub organizations and repositories AND you want your GitHub credentials to narrowly scoped.
leaderElectionId needs to be unique per stack. If this is not unique to the stack the controller tries to race onto the leader election lock resulting in only one stack working concurrently. Your controller will be stuck with a log message something like this attempting to acquire leader lease arc-controllers/actions-runner-controller...
The MutatingWebhookConfiguration in each stack must include a namespace selector for that stacks corresponding runner namespace, this is already configured in the helm chart.

Alternatively, you can install each controller stack into a unique namespace (relative to other controller stacks in the cluster). Implementing ARC this way avoids the first, second and third pitfalls (you still need to set the corresponding namespace selector for each stacks mutating webhook)

Usage

GitHub self-hosted runners can be deployed at various levels in a management hierarchy:

The repository level
The organization level
The enterprise level

There are two ways to use this controller:

Manage runners one by one with Runner.
Manage a set of runners with RunnerDeployment.

Repository Runners

To launch a single self-hosted runner, you need to create a manifest file includes Runner resource as follows. This example launches a self-hosted runner with name example-runner for the actions-runner-controller/actions-runner-controller repository.

# runner.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: Runner
metadata:
  name: example-runner
spec:
  repository: example/myrepo
  env: []

Apply the created manifest file to your Kubernetes.

$ kubectl apply -f runner.yaml
runner.actions.summerwind.dev/example-runner created

You can see that the Runner resource has been created.

$ kubectl get runners
NAME             REPOSITORY                             STATUS
example-runner   actions-runner-controller/actions-runner-controller   Running

You can also see that the runner pod has been running.

$ kubectl get pods
NAME           READY   STATUS    RESTARTS   AGE
example-runner 2/2     Running   0          1m

The runner you created has been registered to your repository.

Now you can use your self-hosted runner. See the official documentation on how to run a job with it.

Organization Runners

To add the runner to an organization, you only need to replace the repository field with organization, so the runner will register itself to the organization.

# runner.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: Runner
metadata:
  name: example-org-runner
spec:
  organization: your-organization-name

Now you can see the runner on the organization level (if you have organization owner permissions).

Enterprise Runners

To add the runner to an enterprise, you only need to replace the repository field with enterprise, so the runner will register itself to the enterprise.

# runner.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: Runner
metadata:
  name: example-enterprise-runner
spec:
  enterprise: your-enterprise-name

Now you can see the runner on the enterprise level (if you have enterprise access permissions).

RunnerDeployments

You can manage sets of runners instead of individually through the RunnerDeployment kind and its replicas: attribute. This kind is required for many of the advanced features.

There are RunnerReplicaSet and RunnerDeployment kinds that corresponds to the ReplicaSet and Deployment kinds but for the Runner kind.

You typically only need RunnerDeployment rather than RunnerReplicaSet as the former is for managing the latter.

# runnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: example-runnerdeploy
spec:
  replicas: 2
  template:
    spec:
      repository: mumoshu/actions-runner-controller-ci
      env: []

Apply the manifest file to your cluster:

$ kubectl apply -f runnerdeployment.yaml
runnerdeployment.actions.summerwind.dev/example-runnerdeploy created

You can see that 2 runners have been created as specified by replicas: 2:

$ kubectl get runners
NAME                             REPOSITORY                             STATUS
example-runnerdeploy2475h595fr   mumoshu/actions-runner-controller-ci   Running
example-runnerdeploy2475ht2qbr   mumoshu/actions-runner-controller-ci   Running

RunnerSets

This feature requires controller version => v0.20.0

Ensure you see the limitations before using this kind!!!!!

For scenarios where you require the advantages of a StatefulSet, for example persistent storage, ARC implements a runner based on Kubernete's StatefulSets, the RunnerSet.

A basic RunnerSet would look like this:

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerSet
metadata:
  name: example
spec:
  ephemeral: false
  replicas: 2
  repository: mumoshu/actions-runner-controller-ci
  # Other mandatory fields from StatefulSet
  selector:
    matchLabels:
      app: example
  serviceName: example
  template:
    metadata:
      labels:
        app: example

As it is based on StatefulSet, selector and template.medatada.labels needs to be defined and have the exact same set of labels. serviceName must be set to some non-empty string as it is also required by StatefulSet.

Runner-related fields like ephemeral, repository, organization, enterprise, and so on should be written directly under spec.

Fields like volumeClaimTemplates that originates from StatefulSet should also be written directly under spec.

Pod-related fields like security contexts and volumes are written under spec.template.spec like StatefulSet.

Similarly, container-related fields like resource requests and limits, container image names and tags, security context, and so on are written under spec.template.spec.containers. There are two reserved container name, runner and docker. The former is for the container that runs actions runner and the latter is for the container that runs a dockerd.

For a more complex example, see the below:

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerSet
metadata:
  name: example
spec:
  ephemeral: false
  replicas: 2
  repository: mumoshu/actions-runner-controller-ci
  dockerdWithinRunnerContainer: true
  template:
    spec:
      securityContext:
        # All level/role/type/user values will vary based on your SELinux policies.
        # See https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux_atomic_host/7/html/container_security_guide/docker_selinux_security_policy for information about SELinux with containers
        seLinuxOptions: 
          level: "s0"
          role: "system_r"
          type: "super_t"
          user: "system_u"
      containers:
      - name: runner
        env: []
        resources:
          limits:
            cpu: "4.0"
            memory: "8Gi"
          requests:
            cpu: "2.0"
            memory: "4Gi"
      - name: docker
        resources:
          limits:
            cpu: "4.0"
            memory: "8Gi"
          requests:
            cpu: "2.0"
            memory: "4Gi"

You can also read the design and usage documentation written in the original pull request that introduced RunnerSet for more information #629.

Under the hood, RunnerSet relies on Kubernetes's StatefulSet and Mutating Webhook. A statefulset is used to create a number of pods that has stable names and dynamically provisioned persistent volumes, so that each statefulset-managed pod gets the same persistent volume even after restarting. A mutating webhook is used to dynamically inject a runner's "registration token" which is used to call GitHub's "Create Runner" API.

Limitations

For autoscaling the RunnerSet kind only supports pull driven scaling or the workflow_job event for webhook driven scaling.
Whilst RunnerSets support all runner modes as well as autoscaling, currently PVs are NOT automatically cleaned up as they are still bound to their respective PVCs when a runner is deleted by the controller. This has major implications when using RunnerSets in the standard runner mode, ephemeral: true, see persistent runners for more details. As a result of this, using the default ephemeral configuration or implementing autoscaling for your RunnerSets, you will get a build up of PVCs and PVs without some sort of custom solution for cleaning up.

Persistent Runners

Every runner managed by ARC is "ephemeral" by default. The life of an ephemeral runner managed by ARC looks like this- ARC creates a runner pod for the runner. As it's an ephemeral runner, the --ephemeral flag is passed to the actions/runner agent that runs within the runner container of the runner pod.

--ephemeral is an actions/runner feature that instructs the runner to stop and de-register itself after the first job run.

Once the ephemeral runner has completed running a workflow job, it stops with a status code of 0, hence the runner pod is marked as completed, removed by ARC.

As it's removed after a workflow job run, the runner pod is never reused across multiple GitHub Actions workflow jobs, providing you a clean environment per each workflow job.

Although not generally recommended, it's possible to disable passing --ephemeral flag by explicitly setting ephemeral: false in the RunnerDeployment or RunnerSet spec. When disabled, your runner becomes "persistent". A persistent runner does not stop after workflow job ends, and in this mode actions/runner is known to clean only runner's work dir after each job. Whilst this can seem helpful it creates a non-deterministic environment which is not ideal for a CI/CD environment. Between runs your actions cache, docker images stored in the dind and layer cache, globally installed packages etc are retained across multiple workflow job runs which can cause issues which are hard to debug and inconsistent.

Persistent runners are available as an option for some edge cases however they are not preferred as they can create challenges around providing a deterministic and secure environment.

Autoscaling

Since the release of GitHub's workflow_job webhook, webhook driven scaling is the preferred way of autoscaling as it enables targeted scaling of your RunnerDeployment / RunnerSet as it includes the runs-on information needed to scale the appropriate runners for that workflow run. More broadly, webhook driven scaling is the preferred scaling option as it is far quicker compared to the pull driven scaling and is easy to setup.

If you are using controller version < v0.22.0 and you are not using GHES, and so can't set your rate limit budget, it is recommended that you use 100 replicas or fewer to prevent being rate limited.

A RunnerDeployment or RunnerSet can scale the number of runners between minReplicas and maxReplicas fields driven by either pull based scaling metrics or via a webhook event (see limitations section of stateful runners for cavaets of this kind). Whether the autoscaling is driven from a webhook event or pull based metrics it is implemented by backing a RunnerDeployment or RunnerSet kind with a HorizontalRunnerAutoscaler kind.

Important!!! If you opt to configure autoscaling, ensure you remove the replicas: attribute in the RunnerDeployment / RunnerSet kinds that are configured for autoscaling #206

Anti-Flapping Configuration

For both pull driven or webhook driven scaling an anti-flapping implementation is included, by default a runner won't be scaled down within 10 minutes of it having been scaled up. This delay is configurable by including the attribute scaleDownDelaySecondsAfterScaleOut: in a HorizontalRunnerAutoscaler kind's spec:.

This configuration has the final say on if a runner can be scaled down or not regardless of the chosen scaling method. Depending on your requirements, you may want to consider adjusting this by setting the scaleDownDelaySecondsAfterScaleOut: attribute.

Below is a complete basic example with one of the pull driven scaling metrics.

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: example-runner-deployment
spec:
  template:
    spec:
      repository: example/myrepo
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  # Runners in the targeted RunnerDeployment won't be scaled down
  # for 5 minutes instead of the default 10 minutes now
  scaleDownDelaySecondsAfterScaleOut: 300
  scaleTargetRef:
    name: example-runner-deployment
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: PercentageRunnersBusy
    scaleUpThreshold: '0.75'
    scaleDownThreshold: '0.25'
    scaleUpFactor: '2'
    scaleDownFactor: '0.5'

Pull Driven Scaling

To configure webhook driven scaling see the Webhook Driven Scaling section

The pull based metrics are configured in the metrics attribute of a HRA (see snippet below). The period between polls is defined by the controller's --sync-period flag. If this flag isn't provided then the controller defaults to a sync period of 10 minutes. The default value is set to 10 minutes to prevent default deployments rate limiting themselves from the GitHub API, you will most likely want to adjust this.

apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  scaleTargetRef:
    # Your RunnerDeployment Here
    name: example-runner-deployment
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  minReplicas: 1
  maxReplicas: 5
  # Your chosen scaling metrics here
  metrics: []

Metric Options:

TotalNumberOfQueuedAndInProgressWorkflowRuns

The TotalNumberOfQueuedAndInProgressWorkflowRuns metric polls GitHub for all pending workflow runs against a given set of repositories. The metric will scale the runner count up to the total number of pending jobs at the sync time up to the maxReplicas configuration.

Benefits of this metric

Supports named repositories allowing you to restrict the runner to a specified set of repositories server-side.
Scales the runner count based on the depth of the job queue meaning a more 1:1 scaling of runners to queued jobs (caveat, see drawback #4)
Like all scaling metrics, you can manage workflow allocation to the RunnerDeployment through the use of GitHub labels.

Drawbacks of this metric

A list of repositories must be included within the scaling metric. Maintaining a list of repositories may not be viable in larger environments or self-serve environments.
May not scale quick enough for some users needs. This metric is pull based and so the queue depth is polled as configured by the sync period, as a result scaling performance is bound by this sync period meaning there is a lag to scaling activity.
Relatively large amounts of API requests required to maintain this metric, you may run in API rate limit issues depending on the size of your environment and how aggressive your sync period configuration is.
The GitHub API doesn't provide a way to filter workflow jobs to just those targeting self-hosted runners. If your environment's workflows target both self-hosted and GitHub hosted runners then the queue depth this metric scales against isn't a true 1:1 mapping of queue depth to required runner count. As a result of this, this metric may scale too aggressively for your actual self-hosted runner count needs.

Example RunnerDeployment backed by a HorizontalRunnerAutoscaler:

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: example-runner-deployment
spec:
  template:
    spec:
      repository: example/myrepo
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  scaleTargetRef:
    name: example-runner-deployment
    # IMPORTANT : If your HRA is targeting a RunnerSet you must specify the kind in the scaleTargetRef:, uncomment the below
    #kind: RunnerSet
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: TotalNumberOfQueuedAndInProgressWorkflowRuns
    repositoryNames:
    - example/myrepo

PercentageRunnersBusy

The HorizontalRunnerAutoscaler will poll GitHub for the number of runners in the busy state which live in the RunnerDeployment's namespace, it will then scale depending on how you have configured the scale factors.

Benefits of this metric

Supports named repositories server-side the same as the TotalNumberOfQueuedAndInProgressWorkflowRuns metric #313
Supports GitHub organization wide scaling without maintaining an explicit list of repositories, this is especially useful for those that are working at a larger scale. #223
Like all scaling metrics, you can manage workflow allocation to the RunnerDeployment through the use of GitHub labels
Supports scaling desired runner count on both a percentage increase / decrease basis as well as on a fixed increase / decrease count basis #223 #315

Drawbacks of this metric

May not scale quick enough for some users needs. This metric is pull based and so the number of busy runners are polled as configured by the sync period, as a result scaling performance is bound by this sync period meaning there is a lag to scaling activity.
We are scaling up and down based on indicative information rather than a count of the actual number of queued jobs and so the desired runner count is likely to under provision new runners or overprovision them relative to actual job queue depth, this may or may not be a problem for you.

Examples of each scaling type implemented with a RunnerDeployment backed by a HorizontalRunnerAutoscaler:

---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  scaleTargetRef:
    name: example-runner-deployment
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: PercentageRunnersBusy
    scaleUpThreshold: '0.75'    # The percentage of busy runners at which the number of desired runners are re-evaluated to scale up
    scaleDownThreshold: '0.3'   # The percentage of busy runners at which the number of desired runners are re-evaluated to scale down
    scaleUpFactor: '1.4'        # The scale up multiplier factor applied to desired count
    scaleDownFactor: '0.7'      # The scale down multiplier factor applied to desired count

---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  scaleTargetRef:
    name: example-runner-deployment
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  minReplicas: 1
  maxReplicas: 5
  metrics:
  - type: PercentageRunnersBusy
    scaleUpThreshold: '0.75'    # The percentage of busy runners at which the number of desired runners are re-evaluated to scale up
    scaleDownThreshold: '0.3'   # The percentage of busy runners at which the number of desired runners are re-evaluated to scale down
    scaleUpAdjustment: 2        # The scale up runner count added to desired count
    scaleDownAdjustment: 1      # The scale down runner count subtracted from the desired count

Webhook Driven Scaling

To configure pull driven scaling see the Pull Driven Scaling section

Webhooks are processed by a seperate webhook server. The webhook server receives GitHub Webhook events and scales RunnerDeployments by updating corresponding HorizontalRunnerAutoscalers.

Today, the Webhook server can be configured to respond GitHub check_run, workflow_job, pull_request and push events by scaling up the matching HorizontalRunnerAutoscaler by N replica(s), where N is configurable within HorizontalRunnerAutoscaler's spec:.

More concretely, you can configure the targeted GitHub event types and the N in scaleUpTriggers:

kind: HorizontalRunnerAutoscaler
spec:
  scaleTargetRef:
    name: example-runners
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scaleUpTriggers:
  - githubEvent:
      checkRun:
        types: ["created"]
        status: "queued"
    amount: 1
    duration: "5m"

With the above example, the webhook server scales example-runners by 1 replica for 5 minutes on each check_run event with the type of created and the status of queued received.

Of note is the HRA.spec.scaleUpTriggers[].duration attribute. This attribute is used to calculate if the replica number added via the trigger is expired or not. On each reconcilation loop, the controller sums up all the non-expiring replica numbers from previous scale up triggers. It then compares the summed desired replica number against the current replica number. If the summed desired replica number > the current number then it means the replica count needs to scale up.

As mentioned previously, the scaleDownDelaySecondsAfterScaleOut property has the final say still. If the latest scale-up time + the anti-flapping duration is later than the current time, it doesn’t immediately scale up and instead retries the calculation again later to see if it needs to scale yet.

The primary benefit of autoscaling on Webhook compared to the pull driven scaling is that it is far quicker as it allows you to immediately add runners resource rather than waiting for the next sync period.

You can learn the implementation details in #282

To enable this feature, you first need to install the GitHub webhook server. To install via our Helm chart, see the values documentation for all configuration options

$ helm upgrade --install --namespace actions-runner-system --create-namespace \
             --wait actions-runner-controller actions-runner-controller/actions-runner-controller \
             --set "githubWebhookServer.enabled=true,githubWebhookServer.ports[0].nodePort=33080"

The above command will result in exposing the node port 33080 for Webhook events. Usually, you need to create an external loadbalancer targeted to the node port, and register the hostname or the IP address of the external loadbalancer to the GitHub Webhook.

Once you were able to confirm that the Webhook server is ready and running from GitHub - this is usually verified by the GitHub sending PING events to the Webhook server - create or update your HorizontalRunnerAutoscaler resources by learning the following configuration examples.

Example 1: Scale on each workflow_job event
Example 2: Scale up on each check_run event
Example 3: Scale on each pull_request event against a given set of branches
Example 4: Scale on each push event

Note: All these examples should have minReplicas & maxReplicas as mandatory parameter even for webhook driven scaling.

Example 1: Scale on each `workflow_job` event

This feature requires controller version => v0.20.0

Note: GitHub does not include the runner group information of a repository in the payload of workflow_job event in the initial queued event. The runner group information is only include for workflow_job events when the job has already been allocated to a runner (events with a status of in_progress or completed). Please do raise feature requests against GitHub for this information to be included in the initial queued event if this would improve autoscaling runners for you.

The most flexible webhook GitHub offers is the workflow_job webhook, it includes the runs-on information in the payload allowing scaling based on runner labels.

This webhook should cover most people's needs, please experiment with this webhook first before considering the others.

kind: RunnerDeployment
metadata:
   name: example-runners
spec:
  template:
    spec:
      repository: example/myrepo
---
kind: HorizontalRunnerAutoscaler
spec:
  scaleTargetRef:
    name: example-runners
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scaleUpTriggers:
  - githubEvent: {}
    duration: "30m"

This webhook requires you to explicitly set the labels in the RunnerDeployment / RunnerSet if you are using them in your workflow to match the agents (field runs-on). Only self-hosted will be considered as included by default.

You can configure your GitHub webhook settings to only include Workflows Job events, so that it sends us three kinds of workflow_job events per a job run.

Each kind has a status of queued, in_progress and completed. With the above configuration, actions-runner-controller adds one runner for a workflow_job event whose status is queued. Similarly, it removes one runner for a workflow_job event whose status is completed. The cavaet to this to remember is that this the scale down is within the bounds of your scaleDownDelaySecondsAfterScaleOut configuration, if this time hasn't past the scale down will be defered.

Example 2: Scale up on each `check_run` event

Note: This should work almost like https://github.com/philips-labs/terraform-aws-github-runner

To scale up replicas of the runners for example/myrepo by 1 for 5 minutes on each check_run, you write manifests like the below:

kind: RunnerDeployment
metadata:
   name: example-runners
spec:
  template:
    spec:
      repository: example/myrepo
---
kind: HorizontalRunnerAutoscaler
spec:
  scaleTargetRef:
    name: example-runners
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scaleUpTriggers:
  - githubEvent:
      checkRun:
        types: ["created"]
        status: "queued"
    amount: 1
    duration: "5m"

To scale up replicas of the runners for myorg organization by 1 for 5 minutes on each check_run, you write manifests like the below:

kind: RunnerDeployment
metadata:
   name: example-runners
spec:
  template:
    spec:
      organization: myorg
---
kind: HorizontalRunnerAutoscaler
spec:
  scaleTargetRef:
    name: example-runners
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scaleUpTriggers:
  - githubEvent:
      checkRun:
        types: ["created"]
        status: "queued"
        # Optionally restrict autoscaling to being triggered by events from specific repositories within your organization still
        # repositories: ["myrepo", "myanotherrepo"]
    amount: 1
    duration: "5m"

Example 3: Scale on each `pull_request` event against a given set of branches

To scale up replicas of the runners for example/myrepo by 1 for 5 minutes on each pull_request against the main or develop branch you write manifests like the below:

kind: RunnerDeployment
metadata:
   name: example-runners
spec:
  template:
    spec:
      repository: example/myrepo
---
kind: HorizontalRunnerAutoscaler
spec:
  scaleTargetRef:
    name: example-runners
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scaleUpTriggers:
  - githubEvent:
      pullRequest:
        types: ["synchronize"]
        branches: ["main", "develop"]
    amount: 1
    duration: "5m"

See "activity types" for the list of valid values for scaleUpTriggers[].githubEvent.pullRequest.types.

Example 4: Scale on each push event

To scale up replicas of the runners for example/myrepo by 1 for 5 minutes on each push write manifests like the below:

kind: RunnerDeployment
metadata:
   name: example-runners
spec:
  repository: example/myrepo
---
kind: HorizontalRunnerAutoscaler
spec:
  scaleTargetRef:
    name: example-runners
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scaleUpTriggers:
  - githubEvent:
      push:
    amount: 1
    duration: "5m"

Autoscaling to/from 0

This feature requires controller version => v0.19.0

The regular RunnerDeployment / RunnerSet replicas: attribute as well as the HorizontalRunnerAutoscaler minReplicas: attribute supports being set to 0.

The main use case for scaling from 0 is with the HorizontalRunnerAutoscaler kind. To scale from 0 whilst still being able to provision runners as jobs are queued we must use the HorizontalRunnerAutoscaler with only certain scaling configurations, only the below configurations support scaling from 0 whilst also being able to provision runners as jobs are queued:

TotalNumberOfQueuedAndInProgressWorkflowRuns
PercentageRunnersBusy + TotalNumberOfQueuedAndInProgressWorkflowRuns
PercentageRunnersBusy + Webhook-based autoscaling
Webhook-based autoscaling only

PercentageRunnersBusy can't be used alone as, by its definition, it needs one or more GitHub runners to become busy to be able to scale. If there isn't a runner to pick up a job and enter a busy state then the controller will never know to provision a runner to begin with as this metric has no knowledge of the job queue and is relying using the number of busy runners as a means for calculating the desired replica count.

If a HorizontalRunnerAutoscaler is configured with a secondary metric of TotalNumberOfQueuedAndInProgressWorkflowRuns then be aware that the controller will check the primary metric of PercentageRunnersBusy first and will only use the secondary metric to calculate the desired replica count if the primary metric returns 0 desired replicas.

Webhook-based autoscaling is the best option as it is relatively easy to configure and also it can scale scale quickly.

Scheduled Overrides

This feature requires controller version => v0.19.0

Scheduled Overrides allows you to configure HorizontalRunnerAutoscaler so that its spec: gets updated only during a certain period of time. This feature is usually used for following scenarios:

You want to reduce your infrastructure costs by scaling your Kubernetes nodes down outside a given period
You want to scale for scheduled spikes in workloads

The most basic usage of this feature is to set a non-repeating override:

apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  scaleTargetRef:
    name: example-runner-deployment
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scheduledOverrides:
  # Override minReplicas to 100 only between 2021-06-01T00:00:00+09:00 and 2021-06-03T00:00:00+09:00
  - startTime: "2021-06-01T00:00:00+09:00"
    endTime: "2021-06-03T00:00:00+09:00"
    minReplicas: 100
  minReplicas: 1

A scheduled override without recurrenceRule is considered a one-off override, that is active between startTime and endTime. In the second scenario, it overrides minReplicas to 100 only between 2021-06-01T00:00:00+09:00 and 2021-06-03T00:00:00+09:00.

A more advanced configuration is to include a recurrenceRule in the override:

apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
  name: example-runner-deployment-autoscaler
spec:
  scaleTargetRef:
    name: example-runner-deployment
    # Uncomment the below in case the target is not RunnerDeployment but RunnerSet
    #kind: RunnerSet
  scheduledOverrides:
  # Override minReplicas to 0 only between 0am sat to 0am mon
  - startTime: "2021-05-01T00:00:00+09:00"
    endTime: "2021-05-03T00:00:00+09:00"
    recurrenceRule:
      frequency: Weekly
      # Optional sunset datetime attribute
      # untilTime: "2022-05-01T00:00:00+09:00"
    minReplicas: 0
  minReplicas: 1

A recurring override is initially active between startTime and endTime, and then it repeatedly get activated after a certain period of time denoted by frequency.

frequecy can take one of the following values:

Daily
Weekly
Monthly
Yearly

By default, a scheduled override repeats forever. If you want it to repeat until a specific point in time, define untilTime. The controller create the last recurrence of the override until the recurrence's startTime is equal or earlier than untilTime.

Do ensure that you have enough slack for untilTime so that a delayed or offline actions-runner-controller is much less likely to miss the last recurrence. For example, you might want to set untilTime to M minutes after the last recurrence's startTime, so that actions-runner-controller being offline up to M minutes doesn't miss the last recurrence.

Combining Multiple Scheduled Overrides:

In case you have a more complex scenarios, try writing two or more entries under scheduledOverrides.

The earlier entry is prioritized higher than later entries. So you usually define one-time overrides in the top of your list, then yearly, monthly, weekly, and lastly daily overrides.

A common use case for this may be to have 1 override to scale to 0 during the week outside of core business hours and another override to scale to 0 during all hours of the weekend.

Runner with DinD

When using default runner, runner pod starts up 2 containers: runner and DinD (Docker-in-Docker). This might create issues if there's LimitRange set to namespace.

# dindrunnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: example-dindrunnerdeploy
spec:
  replicas: 2
  template:
    spec:
      image: summerwind/actions-runner-dind
      dockerdWithinRunnerContainer: true
      repository: mumoshu/actions-runner-controller-ci
      env: []

This also helps with resources, as you don't need to give resources separately to docker and runner.

Additional Tweaks

You can pass details through the spec selector. Here's an eg. of what you may like to do:

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: actions-runner
  namespace: default
spec:
  replicas: 2
  template:
    metadata:
      annotations:
        cluster-autoscaler.kubernetes.io/safe-to-evict: "true"
    spec:
      nodeSelector:
        node-role.kubernetes.io/test: ""

      securityContext:
        #All level/role/type/user values will vary based on your SELinux policies.
        #See https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux_atomic_host/7/html/container_security_guide/docker_selinux_security_policy for information about SELinux with containers
        seLinuxOptions:
          level: "s0"
          role: "system_r"
          type: "super_t"
          user: "system_u"

      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/test
        operator: Exists

      topologySpreadConstraints:
        - maxSkew: 1
          topologyKey: kubernetes.io/hostname
          whenUnsatisfiable: ScheduleAnyway
          labelSelector:
            matchLabels:
              runner-deployment-name: actions-runner

      repository: mumoshu/actions-runner-controller-ci
      # The default "summerwind/actions-runner" images are available at DockerHub:
      # https://hub.docker.com/r/summerwind/actions-runner
      # You can also build your own and specify it like the below:
      image: custom-image/actions-runner:latest
      imagePullPolicy: Always
      resources:
        limits:
          cpu: "4.0"
          memory: "8Gi"
        requests:
          cpu: "2.0"
          memory: "4Gi"
      # Timeout after a node crashed or became unreachable to evict your pods somewhere else (default 5mins)
      tolerations:
        - key: "node.kubernetes.io/unreachable"
          operator: "Exists"
          effect: "NoExecute"
          tolerationSeconds: 10
      # true (default) = The runner restarts after running jobs, to ensure a clean and reproducible build environment
      # false = The runner is persistent across jobs and doesn't automatically restart
      # This directly controls the behaviour of `--once` flag provided to the github runner
      ephemeral: false
      # true (default) = A privileged docker sidecar container is included in the runner pod.
      # false = A docker sidecar container is not included in the runner pod and you can't use docker.
      # If set to false, there are no privileged container and you cannot use docker.
      dockerEnabled: false
      # Optional Docker containers network MTU
      # If your network card MTU is smaller than Docker's default 1500, you might encounter Docker networking issues.
      # To fix these issues, you should setup Docker MTU smaller than or equal to that on the outgoing network card.
      # More information:
      # - https://mlohr.com/docker-mtu/
      dockerMTU: 1500
      # Optional Docker registry mirror
      # Docker Hub has an aggressive rate-limit configuration for free plans.
      # To avoid disruptions in your CI/CD pipelines, you might want to setup an external or on-premises Docker registry mirror.
      # More information:
      # - https://docs.docker.com/docker-hub/download-rate-limit/
      # - https://cloud.google.com/container-registry/docs/pulling-cached-images
      dockerRegistryMirror: https://mirror.gcr.io/
      # false (default) = Docker support is provided by a sidecar container deployed in the runner pod.
      # true = No docker sidecar container is deployed in the runner pod but docker can be used within the runner container instead. The image summerwind/actions-runner-dind is used by default.
      dockerdWithinRunnerContainer: true
      #Optional environement variables for docker container
      # Valid only when dockerdWithinRunnerContainer=false
      dockerEnv:
        - name: HTTP_PROXY
          value: http://example.com      
      # Docker sidecar container image tweaks examples below, only applicable if dockerdWithinRunnerContainer = false
      dockerdContainerResources:
        limits:
          cpu: "4.0"
          memory: "8Gi"
        requests:
          cpu: "2.0"
          memory: "4Gi"
      # Additional N number of sidecar containers
      sidecarContainers:
        - name: mysql
          image: mysql:5.7
          env:
            - name: MYSQL_ROOT_PASSWORD
              value: abcd1234
          securityContext:
            runAsUser: 0
      # workDir if not specified (default = /runner/_work)
      # You can customise this setting allowing you to change the default working directory location
      # for example, the below setting is the same as on the ubuntu-18.04 image
      workDir: /home/runner/work
      # You can mount some of the shared volumes to the dind container using dockerVolumeMounts, like any other volume mounting.
      # NOTE: in case you want to use an hostPath like the following example, make sure that Kubernetes doesn't schedule more than one runner
      # per physical host. You can achieve that by setting pod anti-affinity rules and/or resource requests/limits.
      volumes:
        - name: docker-extra
          hostPath:
            path: /mnt/docker-extra
            type: DirectoryOrCreate
        - name: repo
          hostPath:
            path: /mnt/repo
            type: DirectoryOrCreate
      dockerVolumeMounts:
        - mountPath: /var/lib/docker
          name: docker-extra
      # You can mount some of the shared volumes to the runner container using volumeMounts.
      # NOTE: Do not try to mount the volume onto the runner workdir itself as it will not work. You could mount it however on a sub directory in the runner workdir
      # Please see https://github.com/actions-runner-controller/actions-runner-controller/issues/630#issuecomment-862087323 for more information.
      volumeMounts:
        - mountPath: /home/runner/work/repo
          name: repo
      # Optional storage medium type of runner volume mount.
      # More info: https://kubernetes.io/docs/concepts/storage/volumes/#emptydir
      # "" (default) = Node's default medium
      # Memory = RAM-backed filesystem (tmpfs)
      # NOTE: Using RAM-backed filesystem gives you fastest possible storage on your host nodes.
      volumeStorageMedium: ""
      # Total amount of local storage resources required for runner volume mount.
      # The default limit is undefined.
      # NOTE: You can make sure that nodes' resources are never exceeded by limiting used storage size per runner pod.
      # You can even disable the runner mount completely by setting limit to zero if dockerdWithinRunnerContainer = true.
      # Please see https://github.com/actions-runner-controller/actions-runner-controller/pull/674 for more information.
      volumeSizeLimit: 4Gi
      # Optional name of the container runtime configuration that should be used for pods.
      # This must match the name of a RuntimeClass resource available on the cluster.
      # More info: https://kubernetes.io/docs/concepts/containers/runtime-class
      runtimeClassName: "runc"

Custom Volume mounts

You can configure your own custom volume mounts. For example to have the work/docker data in memory or on NVME ssd, for i/o intensive builds. Other custom volume mounts should be possible as well, see kubernetes documentation

RAM Disk Runner
Example how to place the runner work dir, docker sidecar and /tmp within the runner onto a ramdisk.

kind: RunnerDeployment
spec:
  template:
    spec:
      dockerVolumeMounts:
        - mountPath: /var/lib/docker
          name: docker
      volumeMounts:
        - mountPath: /tmp
          name: tmp
      volumes:
        - name: docker
          emptyDir:
            medium: Memory
        - name: work # this volume gets automatically used up for the workdir
          emptyDir:
            medium: Memory
        - name: tmp
          emptyDir:
            medium: Memory
      emphemeral: true # recommended to not leak data between builds.

NVME SSD Runner
In this example we provide NVME backed storage for the workdir, docker sidecar and /tmp within the runner. Here we use a working example on GKE, which will provide the NVME disk at /mnt/disks/ssd0. We will be placing the respective volumes in subdirs here and in order to be able to run multiple runners we will use the pod name as prefix for subdirectories. Also the disk will fill up over time and disk space will not be freed until the node is removed.

Beware that running these persistent backend volumes leave data behind between 2 different jobs on the workdir and /tmp with emphemeral: false.

kind: RunnerDeployment
spec:
  template:
    spec:
      env:
      - name: POD_NAME
        valueFrom:
          fieldRef:
            fieldPath: metadata.name
      dockerVolumeMounts:
      - mountPath: /var/lib/docker
        name: docker
        subPathExpr: $(POD_NAME)-docker
      - mountPath: /runner/_work
        name: work
        subPathExpr: $(POD_NAME)-work
      volumeMounts:
      - mountPath: /runner/_work
        name: work
        subPathExpr: $(POD_NAME)-work
      - mountPath: /tmp
        name: tmp
        subPathExpr: $(POD_NAME)-tmp
      dockerEnv:
      - name: POD_NAME
        valueFrom:
          fieldRef:
            fieldPath: metadata.name
      volumes:
      - hostPath:
          path: /mnt/disks/ssd0
        name: docker
      - hostPath:
          path: /mnt/disks/ssd0
        name: work
      - hostPath:
          path: /mnt/disks/ssd0
        name: tmp
    emphemeral: true # VERY important. otherwise data inside the workdir and /tmp is not cleared between builds

Runner Labels

To run a workflow job on a self-hosted runner, you can use the following syntax in your workflow:

jobs:
  release:
    runs-on: self-hosted

When you have multiple kinds of self-hosted runners, you can distinguish between them using labels. In order to do so, you can specify one or more labels in your Runner or RunnerDeployment spec.

# runnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: custom-runner
spec:
  replicas: 1
  template:
    spec:
      repository: actions-runner-controller/actions-runner-controller
      labels:
        - custom-runner

Once this spec is applied, you can observe the labels for your runner from the repository or organization in the GitHub settings page for the repository or organization. You can now select a specific runner from your workflow by using the label in runs-on:

jobs:
  release:
    runs-on: custom-runner

Note that if you specify self-hosted in your workflow, then this will run your job on any self-hosted runner, regardless of the labels that they have.

Runner Groups

Runner groups can be used to limit which repositories are able to use the GitHub Runner at an organization level. Runner groups have to be created in GitHub first before they can be referenced.

To add the runner to the group NewGroup, specify the group in your Runner or RunnerDeployment spec.

# runnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: custom-runner
spec:
  replicas: 1
  template:
    spec:
      group: NewGroup

GitHub supports custom visilibity in a Runner Group to make it available to a specific set of repositories only. By default if no GitHub authentication is included in the webhook server ARC will be assumed that all runner groups to be usable in all repositories. Currently, GitHub do not include the repository runner group membership information in the workflow_job event (or any webhook). To make the ARC "runner group aware" additional GitHub API calls are needed to find out what runner groups are visible to the webhook's repository. This behaviour will impact your rate-limit budget and so the option needs to be explicitly configured by the end user.

This option will be enabled when proper GitHub authentication options (token, app or basic auth) is provided in the webhook server and useRunnerGroupsVisibility is set to true, e.g.

githubWebhookServer:
  enabled: false
  replicaCount: 1
  useRunnerGroupsVisibility: true

Runner Entrypoint Features

Environment variable values must all be strings

The entrypoint script is aware of a few environment variables for configuring features:

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: example-runnerdeployment
spec:
  template:
    spec:
      env:
        # Issues a sleep command at the start of the entrypoint
        - name: STARTUP_DELAY_IN_SECONDS
          value: "2"
        # Disables the wait for the docker daemon to be available check
        - name: DISABLE_WAIT_FOR_DOCKER
          value: "true"
        # Disables automatic runner updates
        - name: DISABLE_RUNNER_UPDATE
          value: "true"
        # Configure runner with --ephemeral instead of --once flag
        # WARNING | THIS ENV VAR IS DEPRECATED AND WILL BE REMOVED
        # IN A FUTURE VERSION OF ARC. IN 0.22.0 ARC SETS --ephemeral VIA 
        # THE CONTROLLER SETTING THIS ENV VAR ON POD CREATION.
        # THIS ENV VAR WILL BE REMOVED, SEE ISSUE #1196 FOR DETAILS
        - name: RUNNER_FEATURE_FLAG_EPHEMERAL
          value: "true"

Using IRSA (IAM Roles for Service Accounts) in EKS

This feature requires controller version => v0.15.0

As similar as for regular pods and deployments, you firstly need an existing service account with the IAM role associated. Create one using e.g. eksctl. You can refer to the EKS documentation for more details.

Once you set up the service account, all you need is to add serviceAccountName and fsGroup to any pods that uses the IAM-role enabled service account.

For RunnerDeployment, you can set those two fields under the runner spec at RunnerDeployment.Spec.Template:

apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
  name: example-runnerdeploy
spec:
  template:
    spec:
      repository: USER/REO
      serviceAccountName: my-service-account
      securityContext:
        fsGroup: 1000

Software Installed in the Runner Image

Cloud Tooling
The project supports being deployed on the various cloud Kubernetes platforms (e.g. EKS), it does not however aim to go beyond that. No cloud specific tooling is bundled in the base runner, this is an active decision to keep the overhead of maintaining the solution manageable.

Bundled Software
The GitHub hosted runners include a large amount of pre-installed software packages. GitHub maintain a list in README files at https://github.com/actions/virtual-environments/tree/main/images/linux

This solution maintains a few runner images with latest aligning with GitHub's Ubuntu version, these images do not contain all of the software installed on the GitHub runners. The images contain the following subset of packages from the GitHub runners:

Basic CLI packages
git
docker
build-essentials

The virtual environments from GitHub contain a lot more software packages (different versions of Java, Node.js, Golang, .NET, etc) which are not provided in the runner image. Most of these have dedicated setup actions which allow the tools to be installed on-demand in a workflow, for example: actions/setup-java or actions/setup-node

If there is a need to include packages in the runner image for which there is no setup action, then this can be achieved by building a custom container image for the runner. The easiest way is to start with the summerwind/actions-runner image and installing the extra dependencies directly in the docker image:

FROM summerwind/actions-runner:latest

RUN sudo apt update -y \
  && sudo apt install YOUR_PACKAGE
  && sudo rm -rf /var/lib/apt/lists/*

You can then configure the runner to use a custom docker image by configuring the image field of a Runner or RunnerDeployment:

apiVersion: actions.summerwind.dev/v1alpha1
kind: Runner
metadata:
  name: custom-runner
spec:
  repository: actions-runner-controller/actions-runner-controller
  image: YOUR_CUSTOM_DOCKER_IMAGE

Using without cert-manager

Assuming you are installing in the default namespace, ensure your certificate has SANs:

webhook-service.actions-runner-system.svc
webhook-service.actions-runner-system.svc.cluster.local

It is possible to use a self-signed certificate by following a guide like this one using openssl.

Install your certificate as a TLS secret:

$ kubectl create secret tls webhook-server-cert \
  -n actions-runner-system \
  --cert=path/to/cert/file \
  --key=path/to/key/file

Set the Helm chart values as follows:

$ CA_BUNDLE=$(cat path/to/ca.pem | base64)
$ helm --upgrade install actions-runner-controller/actions-runner-controller \
  certManagerEnabled=false \
  admissionWebHooks.caBundle=${CA_BUNDLE}

Troubleshooting

See troubleshooting guide for solutions to various problems people have ran into consistently.

Contributing

For more details on contributing to the project (including requirements) please check out Getting Started with Contributing.

License

apex-anthonybob/actions-runner-controller

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