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## Status
Even though actions-runner-controller is used in production environments, it is still in its early stage of development, hence versioned 0.x.
actions-runner-controller complies to Semantic Versioning 2.0.0 in which v0.x means that there could be backward-incompatible changes for every release.
The documentation is kept inline with master@HEAD, we do our best to highlight any features that require a specific ARC version or higher however this is not always easily done due to there being many moving parts. Additionally, we actively do not retain compatibly with every GitHub Enterprise Server version nor every Kubernetes version so you will need to ensure you stay current within a reasonable timespan.
## About
[GitHub Actions](https://github.com/features/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](https://github.com/actions/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.
## Getting Started
To give ARC a try with just a handful of commands, Please refer to [Quick start guide](https://github.com/actions-runner-controller/actions-runner-controller/blob/master/docs/QuickStartGuide.md).
For an overview of ARC, please refer to [ARC Overview](https://github.com/actions-runner-controller/actions-runner-controller/blob/master/docs/Actions-Runner-Controller-Overview.md)
For more information, please refer to detailed documentation below!
## Installation
By default, actions-runner-controller uses [cert-manager](https://cert-manager.io/docs/installation/kubernetes/) for certificate management of Admission Webhook. Make sure you have already installed cert-manager before you install. The installation instructions for the cert-manager can be found below.
- [Installing cert-manager on Kubernetes](https://cert-manager.io/docs/installation/kubernetes/)
After installing cert-manager, install the custom resource definitions and actions-runner-controller with `kubectl` or `helm`. This will create an actions-runner-system namespace in your Kubernetes and deploy the required resources.
**Kubectl Deployment:**
```shell
# REPLACE "v0.25.2" with the version you wish to deploy
kubectl create -f https://github.com/actions-runner-controller/actions-runner-controller/releases/download/v0.25.2/actions-runner-controller.yaml
```
**Helm Deployment:**
Configure your values.yaml, see the chart's [README](../charts/actions-runner-controller/README.md) for the values documentation
```shell
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 >= [3.6.0](https://docs.github.com/en/enterprise-server@3.6/admin/release-notes).
When deploying the solution for a GHES environment you need to provide an additional environment variable as part of the controller deployment:
```shell
kubectl set env deploy controller-manager -c manager GITHUB_ENTERPRISE_URL=**Repository Permissions** * Actions (read) * Administration (read / write) * Checks (read) (if you are going to use [Webhook Driven Scaling](#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](https://docs.github.com/en/rest/reference/permissions-required-for-github-apps) 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](#webhook-driven-scaling). The webhook can be configured centrally in the GitHub app itself or separately. In either case you need to subscribe to the `Workflow Job` event. --- **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](https://github.com/settings/apps/new?url=http://github.com/actions-runner-controller/actions-runner-controller&webhook_active=false&public=false&administration=write&actions=read) 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](https://github.com/organizations/:org/settings/apps/new?url=http://github.com/actions-runner-controller/actions-runner-controller&webhook_active=false&public=false&administration=write&organization_self_hosted_runners=write&actions=read&checks=read) 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 the downloaded private key file (`PRIVATE_KEY_FILE_PATH`) to Kubernetes as a secret.
**Kubectl Deployment:**
```shell
$ 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=${PRIVATE_KEY_FILE_PATH}
```
**Helm Deployment:**
Configure your values.yaml, see the chart's [README](../charts/actions-runner-controller/README.md) for deploying the secret via Helm
### Deploying Using PAT Authentication
Personal Access Tokens can be used to register a self-hosted runner by *actions-runner-controller*.
Log-in to a GitHub account that has `admin` privileges for the repository, and [create a personal access token](https://github.com/settings/tokens/new) with the appropriate scopes listed below:
**Required Scopes for Repository Runners**
* repo (Full control)
**Required Scopes for Organization Runners**
* repo (Full control)
* admin:org (Full control)
* admin:public_key (read:public_key)
* admin:repo_hook (read:repo_hook)
* admin:org_hook (Full control)
* notifications (Full control)
* workflow (Full control)
**Required Scopes for Enterprise Runners**
* admin:enterprise (manage_runners:enterprise)
_Note: When you deploy enterprise runners they will get access to organizations, however, access to the repositories themselves is **NOT** allowed by default. Each GitHub organization must allow enterprise runner groups to be used in repositories as an initial one-time configuration step, this only needs to be done once after which it is permanent for that runner group._
_Note: GitHub does not document exactly what permissions you get with each PAT scope beyond a vague description. The best documentation they provide on the topic can be found [here](https://docs.github.com/en/developers/apps/building-oauth-apps/scopes-for-oauth-apps) if you wish to review. The docs target OAuth apps and so are incomplete and may not be 100% accurate._
---
Once you have created the appropriate token, deploy it as a secret to your Kubernetes cluster that you are going to deploy the solution on:
**Kubectl Deployment:**
```shell
kubectl create secret generic controller-manager \
-n actions-runner-system \
--from-literal=github_token=${GITHUB_TOKEN}
```
**Helm Deployment:**
Configure your values.yaml, see the chart's [README](../charts/actions-runner-controller/README.md) for deploying the secret via Helm
### Deploying Multiple Controllers
> This feature requires controller version => [v0.18.0](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/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) at 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.
1. All resources per stack must have a unique name, in the case of Helm this can be done by giving each install a unique release name, or via the `fullnameOverride` properties.
2. `authSecret.name` needs to be unique per stack when each stack is tied to runners in different GitHub organizations and repositories AND you want your GitHub credentials to be narrowly scoped.
3. `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...`
4. The MutatingWebhookConfiguration in each stack must include a namespace selector for that stack's 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 stack's mutating webhook)
## Usage
[GitHub self-hosted runners can be deployed at various levels in a management hierarchy](https://docs.github.com/en/actions/hosting-your-own-runners/about-self-hosted-runners#about-self-hosted-runners):
- The repository level
- The organization level
- The enterprise level
Runners can be deployed as 1 of 2 abstractions:
- A `RunnerDeployment` (similar to k8s's `Deployments`, based on `Pods`)
- A `RunnerSet` (based on k8s's `StatefulSets`)
We go into details about the differences between the 2 later, initially lets look at how to deploy a basic `RunnerDeployment` at the 3 possible management hierarchies.
### Repository Runners
To launch a single self-hosted runner, you need to create a manifest file that includes a `RunnerDeployment` resource as follows. This example launches a self-hosted runner with name *example-runnerdeploy* for the *actions-runner-controller/actions-runner-controller* repository.
```yaml
# runnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: example-runnerdeploy
spec:
replicas: 1
template:
spec:
repository: mumoshu/actions-runner-controller-ci
```
Apply the created manifest file to your Kubernetes.
```shell
$ kubectl apply -f runnerdeployment.yaml
runnerdeployment.actions.summerwind.dev/example-runnerdeploy created
```
You can see that 1 runner and its underlying pod has been created as specified by `replicas: 1` attribute:
```shell
$ kubectl get runners
NAME REPOSITORY STATUS
example-runnerdeploy2475h595fr mumoshu/actions-runner-controller-ci Running
$ kubectl get pods
NAME READY STATUS RESTARTS AGE
example-runnerdeploy2475ht2qbr 2/2 Running 0 1m
```
The runner you created has been registered directly to the defined repository, you should be able to see it in the settings of the repository.
Now you can use your self-hosted runner. See the [official documentation](https://help.github.com/en/actions/automating-your-workflow-with-github-actions/using-self-hosted-runners-in-a-workflow) 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.
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: example-runnerdeploy
spec:
replicas: 1
template:
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.
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: example-runnerdeploy
spec:
replicas: 1
template:
spec:
enterprise: your-enterprise-name
```
Now you can see the runner on the enterprise level (if you have enterprise access permissions).
### RunnerDeployments
In our previous examples we were deploying a single runner via the `RunnerDeployment` kind, the amount of runners deployed can be statically set via the `replicas:` field, we can increase this value to deploy additional sets of runners instead:
```yaml
# runnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: example-runnerdeploy
spec:
# This will deploy 2 runners now
replicas: 2
template:
spec:
repository: mumoshu/actions-runner-controller-ci
```
Apply the manifest file to your cluster:
```shell
$ 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`:
```shell
$ 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](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/v0.20.0)
We can also deploy sets of RunnerSets the same way, a basic `RunnerSet` would look like this:
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerSet
metadata:
name: example
spec:
replicas: 1
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.metadata.labels` it 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](https://github.com/actions/runner) and the latter is for the container that runs a `dockerd`.
For a more complex example, see the below:
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerSet
metadata:
name: example
spec:
replicas: 1
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"
# This is an advanced configuration. Don't touch it unless you know what you're doing.
securityContext:
# Usually, the runner container's privileged field is derived from dockerdWithinRunnerContainer.
# But in the case where you need to run privileged job steps even if you don't use docker/don't need dockerd within the runner container,
# just specified `privileged: true` like this.
# See https://github.com/actions-runner-controller/actions-runner-controller/issues/1282
# Do note that specifying `privileged: false` while using dind is very likely to fail, even if you use some vm-based container runtimes
# like firecracker and kata. Basically they run containers within dedicated micro vms and so
# it's more like you can use `privileged: true` safer with those runtimes.
#
# privileged: true
- 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](https://github.com/actions-runner-controller/actions-runner-controller/pull/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.
### 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 the passing of the `--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 that 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
> If you are using controller version < [v0.22.0](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/v0.22.0) and you are not using GHES, and so you 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. 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](https://github.com/actions-runner-controller/actions-runner-controller/issues/206#issuecomment-748601907)_**
#### 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 anti-flap configuration also has the final say on if a runner can be scaled down or not regardless of the chosen scaling method.
This delay is configurable via 2 methods:
1. By setting a new default via the controller's `--default-scale-down-delay` flag
2. By setting by setting the attribute `scaleDownDelaySecondsAfterScaleOut:` in a `HorizontalRunnerAutoscaler` kind's `spec:`.
Below is a complete basic example of one of the pull driven scaling metrics.
```yaml
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:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
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](#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 `1m`, this can be configured in seconds or minutes.
Be aware that the shorter the sync period the quicker you will consume your rate limit budget, depending on your environment this may or may not be a risk. Consider monitoring ARCs rate limit budget when configuring this feature to find the optimal performance sync period.
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
name: example-runner-deployment-autoscaler
spec:
scaleTargetRef:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
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**
1. Supports named repositories allowing you to restrict the runner to a specified set of repositories server-side.
2. Scales the runner count based on the depth of the job queue meaning a 1:1 scaling of runners to queued jobs.
3. Like all scaling metrics, you can manage workflow allocation to the RunnerDeployment through the use of [GitHub labels](#runner-labels).
**Drawbacks of this metric**
1. 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.
2. May not scale quickly 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.
3. Relatively large amounts of API requests are required to maintain this metric, you may run into API rate limit issues depending on the size of your environment and how aggressive your sync period configuration is.
Example `RunnerDeployment` backed by a `HorizontalRunnerAutoscaler`:
```yaml
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:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
minReplicas: 1
maxReplicas: 5
metrics:
- type: TotalNumberOfQueuedAndInProgressWorkflowRuns
repositoryNames:
# A repository name is the REPO part of `github.com/OWNER/REPO`
- 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**
1. Supports named repositories server-side the same as the `TotalNumberOfQueuedAndInProgressWorkflowRuns` metric [#313](https://github.com/actions-runner-controller/actions-runner-controller/pull/313)
2. 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](https://github.com/actions-runner-controller/actions-runner-controller/pull/223)
3. Like all scaling metrics, you can manage workflow allocation to the RunnerDeployment through the use of [GitHub labels](#runner-labels)
4. Supports scaling desired runner count on both a percentage increase / decrease basis as well as on a fixed increase / decrease count basis [#223](https://github.com/actions-runner-controller/actions-runner-controller/pull/223) [#315](https://github.com/actions-runner-controller/actions-runner-controller/pull/315)
**Drawbacks of this metric**
1. May not scale quickly enough for some users' needs. This metric is pull based and so the number of busy runners 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.
2. 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`:
```yaml
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
name: example-runner-deployment-autoscaler
spec:
scaleTargetRef:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
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
```
```yaml
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
name: example-runner-deployment-autoscaler
spec:
scaleTargetRef:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
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
> This feature requires controller version => [v0.20.0](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/v0.20.0)
> To configure pull driven scaling see the [Pull Driven Scaling](#pull-driven-scaling) section
Alternatively ARC can be configured to scale based on the `workflow_job` webhook event. The primary benefit of autoscaling on webhooks compared to the pull driven scaling is that ARC is immediately notified of the scaling need.
Webhooks are processed by a separate webhook server. The webhook server receives `workflow_job` webhook events and scales RunnerDeployments / RunnerSets by updating HRAs configured for the webhook trigger. Below is an example set-up where a HRA has been configured to scale a `RunnerDeployment` from a `workflow_job` event:
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: example-runners
spec:
template:
spec:
repository: example/myrepo
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
name: example-runners
spec:
minReplicas: 1
maxReplicas: 10
scaleTargetRef:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runners
scaleUpTriggers:
- githubEvent:
workflowJob: {}
duration: "30m"
```
The lifecycle of a runner provisioned from a webhook is different to a runner provisioned from the pull based scaling method:
1. GitHub sends a `workflow_job` event to ARC with `status=queued`
2. ARC finds a HRA with a `workflow_job` webhook scale trigger that backs a RunnerDeployment / RunnerSet with matching runner labels
3. The matched HRA adds a unit to its `capacityReservations` list
4. ARC adds a replica and sets the EffectiveTime of that replica to current + `HRA.spec.scaleUpTriggers[].duration`
At this point there are a few things that can happen, either the job gets allocated to the runner or the runner is left dangling due to it not being used, if the runner gets assigned the job that triggered the scale up the lifecycle looks like this:
1. The new runner gets allocated the job and processes it
2. Upon the job ending GitHub sends another `workflow_job` event to ARC but with `status=completed`
3. The HRA removes the oldest capacity reservation from its `capacityReservations` and picks a runner to terminate ensuring it isn't busy via the GitHub API beforehand
If the job is cancelled before it is allocated to a runner then the lifecycle looks like this:
1. Upon the job cancellation GitHub sends another `workflow_job` event to ARC but with `status=cancelled`
2. The HRA removes the oldest capacity reservation from its `capacityReservations` and picks a runner to terminate ensuring it isn't busy via the GitHub API beforehand
If runner is never used due to other runners matching needed runner group and required runner labels are allocated the job then the lifecycle looks like this:
1. The scale trigger duration specified via `HRA.spec.scaleUpTriggers[].duration` elapses
2. The HRA thinks the capacity reservation is expired, removes it from HRA's `capacityReservations` and terminates the expired runner ensuring it isn't busy via the GitHub API beforehand
1. The HRA removes a capacity reservation from its `capacityReservations` and terminates the expired runner ensuring it isn't busy via the GitHub API beforehand
Your `HRA.spec.scaleUpTriggers[].duration` value should be set long enough to account for the following things:
1. the potential amount of time it could take for a pod to become `Running` e.g. you need to scale horizontally because there isn't a node avaliable
2. the amount of time it takes for GitHub to allocate a job to that runner
3. the amount of time it takes for the runner to notice the allocated job and starts running it
##### Install with Helm
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](../charts/actions-runner-controller/README.md)_
```console
$ helm upgrade --install --namespace actions-runner-system --create-namespace \
--wait actions-runner-controller actions-runner-controller/actions-runner-controller \
--set "githubWebhookServer.enabled=true,service.type=NodePort,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 load balancer targeted to the node port,
and register the hostname or the IP address of the external load balancer to the GitHub Webhook.
**With a custom Kubernetes ingress controller:**
> **CAUTION:** The Kubernetes ingress controllers described below is just a suggestion from the community and
> the ARC team will not provide any user support for ingress controllers as it's not a part of this project.
>
> The following guide on creating an ingress has been contributed by the awesome ARC community and is provided here as-is.
> You may, however, still be able to ask for help on the community on GitHub Discussions if you have any problems.
Kubernetes provides `Ingress` resources to let you configure your ingress controller to expose a Kubernetes service.
If you plan to expose ARC via Ingress, you might not be required to make it a `NodePort` service
(although nothing would prevent an ingress controller to expose NodePort services too):
```console
$ helm upgrade --install --namespace actions-runner-system --create-namespace \
--wait actions-runner-controller actions-runner-controller/actions-runner-controller \
--set "githubWebhookServer.enabled=true"
```
The command above will create a new deployment and a service for receiving Github Webhooks on the `actions-runner-system` namespace.
Now we need to expose this service so that GitHub can send these webhooks over the network with TSL protection.
You can do it in any way you prefer, here we'll suggest doing it with a k8s Ingress.
For the sake of this example we'll expose this service on the following URL:
- https://your.domain.com/actions-runner-controller-github-webhook-server
Where `your.domain.com` should be replaced by your own domain.
> Note: This step assumes you already have a configured `cert-manager` and domain name for your cluster.
Let's start by creating an Ingress file called `arc-webhook-server.yaml` with the following contents:
```yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: actions-runner-controller-github-webhook-server
namespace: actions-runner-system
annotations:
kubernetes.io/ingress.class: nginx
nginx.ingress.kubernetes.io/backend-protocol: "HTTP"
spec:
tls:
- hosts:
- your.domain.com
secretName: your-tls-secret-name
rules:
- http:
paths:
- path: /actions-runner-controller-github-webhook-server
pathType: Prefix
backend:
service:
name: actions-runner-controller-github-webhook-server
port:
number: 80
```
Make sure to set the `spec.tls.secretName` to the name of your TLS secret and
`spec.tls.hosts[0]` to your own domain.
Then create this resource on your cluster with the following command:
```bash
kubectl apply -n actions-runner-system -f arc-webhook-server.yaml
```
**Configuring GitHub for sending webhooks for our newly created webhook server:**
After this step your webhook server should be ready to start receiving webhooks from GitHub.
To configure GitHub to start sending you webhooks, go to the settings page of your repository
or organization then click on `Webhooks`, then on `Add webhook`.
There set the "Payload URL" field with the webhook URL you just created,
if you followed the example ingress above the URL would be something like this:
- https://your.domain.com/actions-runner-controller-github-webhook-server
> Remember to replace `your.domain.com` with your own domain.
Then click on "let me select individual events" and choose `Workflow Jobs`.
Then click on `Add Webhook`.
GitHub will then send a `ping` event to your webhook server to check if it is working, if it is you'll see a green V mark
alongside your webhook on the Settings -> Webhooks page.
Once you were able to confirm that the Webhook server is ready and running from GitHub create or update your
`HorizontalRunnerAutoscaler` resources by learning the following configuration examples.
##### Install with Kustomize
To install this feature using Kustomize, add `github-webhook-server` resources to your `kustomization.yaml` file as in the example below:
```yaml
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
resources:
# You should already have this
- github.com/actions-runner-controller/actions-runner-controller/config//default?ref=v0.22.2
# Add the below!
- github.com/actions-runner-controller/actions-runner-controller/config//github-webhook-server?ref=v0.22.2
Finally, you will have to configure an ingress so that you may configure the webhook in github. An example of such ingress can be find below:
```yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: actions-runners-webhook-server
spec:
rules:
- http:
paths:
- path: /
backend:
service:
name: github-webhook-server
port:
number: 80
pathType: Exact
```
#### Autoscaling to/from 0
> This feature requires controller version => [v0.19.0](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/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 on 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 quickly.
#### Scheduled Overrides
> This feature requires controller version => [v0.19.0](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/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 the 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:
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
name: example-runner-deployment-autoscaler
spec:
scaleTargetRef:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
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:
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: HorizontalRunnerAutoscaler
metadata:
name: example-runner-deployment-autoscaler
spec:
scaleTargetRef:
kind: RunnerDeployment
# # In case the scale target is RunnerSet:
# kind: RunnerSet
name: example-runner-deployment
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 gets 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 creates 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 scenario, try writing two or more entries under `scheduledOverrides`.
The earlier entry is prioritized higher than later entries. So you usually define one-time overrides at 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.
### Alternative Runners
ARC also offers a few alternative runner options
#### Runner with DinD
When using the default runner, the runner pod starts up 2 containers: runner and DinD (Docker-in-Docker). ARC maintains an alternative all in one runner image with docker running in the same container as the runner. This may be prefered from a resource or complexity perspective or to be compliant with a `LimitRange` namespace configuration.
```yaml
# dindrunnerdeployment.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: example-dindrunnerdeploy
spec:
replicas: 1
template:
spec:
image: summerwind/actions-runner-dind
dockerdWithinRunnerContainer: true
repository: mumoshu/actions-runner-controller-ci
env: []
```
#### Runner with rootless DinD
When using the DinD runner, it assumes that the main runner is rootful, which can be problematic in a regulated or more security-conscious environment, such as co-tenanting across enterprise projects. The `actions-runner-dind-rootless` image runs rootless Docker inside the container as `runner` user. Note that this user does not have sudo access, so anything requiring admin privileges must be built into the runner's base image (like running `apt` to install additional software).
#### Runner with K8s Jobs
When using the default runner, jobs that use a container will run in docker. This necessitates privileged mode, either on the runner pod or the sidecar container
By setting the container mode, you can instead invoke these jobs using a [kubernetes implementation](https://github.com/actions/runner-container-hooks/tree/main/packages/k8s) while not executing in privileged mode.
The runner will dynamically spin up pods and k8s jobs in the runner's namespace to run the workflow, so a `workVolumeClaimTemplate` is required for the runner's working directory, and a service account with the [appropriate permissions.](https://github.com/actions/runner-container-hooks/tree/main/packages/k8s#pre-requisites)
There are some [limitations](https://github.com/actions/runner-container-hooks/tree/main/packages/k8s#limitations) to this approach, mainly [job containers](https://docs.github.com/en/actions/using-jobs/running-jobs-in-a-container) are required on all workflows.
```yaml
# runner.yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: Runner
metadata:
name: example-runner
spec:
repository: example/myrepo
containerMode: kubernetes
serviceAccountName: my-service-account
workVolumeClaimTemplate:
storageClassName: "my-dynamic-storage-class"
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 10Gi
env: []
```
### Additional Tweaks
You can pass details through the spec selector. Here's an eg. of what you may like to do:
```yaml
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:
priorityClassName: "high"
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 environment 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 subdirectory 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"
# This is an advanced configuration. Don't touch it unless you know what you're doing.
containers:
- name: runner
# Usually, the runner container's privileged field is derived from dockerdWithinRunnerContainer.
# But in the case where you need to run privileged job steps even if you don't use docker/don't need dockerd within the runner container,
# just specified `privileged: true` like this.
# See https://github.com/actions-runner-controller/actions-runner-controller/issues/1282
# Do note that specifying `privileged: false` while using dind is very likely to fail, even if you use some vm-based container runtimes
# like firecracker and kata. Basically they run containers within dedicated micro vms and so
# it's more like you can use `privileged: true` safer with those runtimes.
#
# privileged: true
```
### 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](https://kubernetes.io/docs/concepts/storage/volumes/)
#### RAM Disk
Example how to place the runner work dir, docker sidecar and /tmp within the runner onto a ramdisk.
```yaml
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
ephemeral: true # recommended to not leak data between builds.
```
#### NVME SSD
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 a 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 `ephemeral: false`.
```yaml
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
ephemeral: true # VERY important. otherwise data inside the workdir and /tmp is not cleared between builds
```
#### Docker image layers caching
> **Note**: Ensure that the volume mount is added to the container that is running the Docker daemon.
`docker` stores pulled and built image layers in the [daemon's (not client)](https://docs.docker.com/get-started/overview/#docker-architecture) [local storage area](https://docs.docker.com/storage/storagedriver/#sharing-promotes-smaller-images) which is usually at `/var/lib/docker`.
By leveraging RunnerSet's dynamic PV provisioning feature and your CSI driver, you can let ARC maintain a pool of PVs that are
reused across runner pods to retain `/var/lib/docker`.
_Be sure to add the volume mount to the container that is supposed to run the docker daemon._
_Be sure to trigger several workflow runs before checking if the cache is effective. ARC requires an `Available` PV to be reused for the new runner pod, and a PV becomes `Available` only after some time after the previous runner pod that was using the PV terminated. See [the related discussion](https://github.com/actions-runner-controller/actions-runner-controller/discussions/1605)._
By default, ARC creates a sidecar container named `docker` within the runner pod for running the docker daemon. In that case,
it's where you need the volume mount so that the manifest looks like:
```yaml
kind: RunnerSet
metadata:
name: example
spec:
template:
spec:
containers:
- name: docker
volumeMounts:
- name: var-lib-docker
mountPath: /var/lib/docker
volumeClaimTemplates:
- metadata:
name: var-lib-docker
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 10Mi
storageClassName: var-lib-docker
```
With `dockerdWithinRunnerContainer: true`, you need to add the volume mount to the `runner` container.
#### Go module and build caching
`Go` is known to cache builds under `$HOME/.cache/go-build` and downloaded modules under `$HOME/pkg/mod`.
The module cache dir can be customized by setting `GOMOD_CACHE` so by setting it to somewhere under `$HOME/.cache`,
we can have a single PV to host both build and module cache, which might improve Go module downloading and building time.
_Be sure to trigger several workflow runs before checking if the cache is effective. ARC requires an `Available` PV to be reused for the new runner pod, and a PV becomes `Available` only after some time after the previous runner pod that was using the PV terminated. See [the related discussion](https://github.com/actions-runner-controller/actions-runner-controller/discussions/1605)._
```yaml
kind: RunnerSet
metadata:
name: example
spec:
template:
spec:
containers:
- name: runner
env:
- name: GOMODCACHE
value: "/home/runner/.cache/go-mod"
volumeMounts:
- name: cache
mountPath: "/home/runner/.cache"
volumeClaimTemplates:
- metadata:
name: cache
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 10Mi
storageClassName: cache
```
#### PV-backed runner work directory
ARC works by automatically creating runner pods for running [`actions/runner`](https://github.com/actions/runner) and [running `config.sh`](https://docs.github.com/en/actions/hosting-your-own-runners/adding-self-hosted-runners#adding-a-self-hosted-runner-to-a-repository) which you had to ran manually without ARC.
`config.sh` is the script provided by `actions/runner` to pre-configure the runner process before being started. One of the options provided by `config.sh` is `--work`,
which specifies the working directory where the runner runs your workflow jobs in.
The volume and the partition that hosts the work directory should have several or dozens of GBs free space that might be used by your workflow jobs.
By default, ARC uses `/runner/_work` as work directory, which is powered by Kubernetes's `emptyDir`. [`emptyDir` is usually backed by a directory created within a host's volume](https://kubernetes.io/docs/concepts/storage/volumes/#emptydir), somewhere under `/var/lib/kuberntes/pods`. Therefore
your host's volume that is backing `/var/lib/kubernetes/pods` must have enough free space to serve all the concurrent runner pods that might be deployed onto your host at the same time.
So, in case you see a job failure seemingly due to "disk full", it's very likely you need to reconfigure your host to have more free space.
In case you can't rely on host's volume, consider using `RunnerSet` and backing the work directory with a ephemeral PV.
Kubernetes 1.23 or greater provides the support for [generic ephemeral volumes](https://kubernetes.io/docs/concepts/storage/ephemeral-volumes/#generic-ephemeral-volumes), which is designed to support this exact use-case. It's defined in the Pod spec API so it isn't currently available for `RunnerDeployment`. `RunnerSet` is based on Kubernetes' `StatefulSet` which mostly embeds the Pod spec under `spec.template.spec`, so there you go.
```yaml
kind: RunnerSet
metadata:
name: example
spec:
template:
spec:
containers:
- name: runner
volumeMounts:
- mountPath: /runner/_work
name: work
- name: docker
volumeMounts:
- mountPath: /runner/_work
name: work
volumes:
- name: work
ephemeral:
volumeClaimTemplate:
spec:
accessModes: [ "ReadWriteOnce" ]
storageClassName: "runner-work-dir"
resources:
requests:
storage: 10Gi
```
### Runner Labels
To run a workflow job on a self-hosted runner, you can use the following syntax in your workflow:
```yaml
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.
```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`:
```yaml
jobs:
release:
runs-on: custom-runner
```
When using labels there are a few things to be aware of:
1. `self-hosted` is implict with every runner as this is an automatic label GitHub apply to any self-hosted runner. As a result ARC can treat all runners as having this label without having it explicitly defined in a runner's manifest. You do not need to explicitly define this label in your runner manifests (you can if you want though).
2. In addition to the `self-hosted` label, GitHub also applies a few other [default](https://docs.github.com/en/actions/hosting-your-own-runners/using-self-hosted-runners-in-a-workflow#using-default-labels-to-route-jobs) labels to any self-hosted runner. The other default labels relate to the architecture of the runner and so can't be implicitly applied by ARC as ARC doesn't know if the runner is `linux` or `windows`, `x64` or `ARM64` etc. If you wish to use these labels in your workflows and have ARC scale runners accurately you must also add them to your runner manifests.
### 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](https://docs.github.com/en/actions/hosting-your-own-runners/managing-access-to-self-hosted-runners-using-groups) before they can be referenced.
To add the runner to the group `NewGroup`, specify the group in your `Runner` or `RunnerDeployment` spec.
```yaml
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: custom-runner
spec:
replicas: 1
template:
spec:
group: NewGroup
```
GitHub supports custom visibility 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 does 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) are provided in the webhook server and `useRunnerGroupsVisibility` is set to true, e.g.
```yaml
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:
```yaml
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"
```
### Using IRSA (IAM Roles for Service Accounts) in EKS
> This feature requires controller version => [v0.15.0](https://github.com/actions-runner-controller/actions-runner-controller/releases/tag/v0.15.0)
Similar to 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](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html) for more details.
Once you set up the service account, all you need is to add `serviceAccountName` and `fsGroup` to any pods that use the IAM-role enabled service account.
For `RunnerDeployment`, you can set those two fields under the runner spec at `RunnerDeployment.Spec.Template`:
```yaml
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 maintains a list in README files at
Windows
#### RunnerDeployment
```yaml
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: k8s-runners-windows
namespace: actions-runner-system
spec:
template:
spec:
image:
Linux
#### RunnerDeployment
```yaml
---
apiVersion: actions.summerwind.dev/v1alpha1
kind: RunnerDeployment
metadata:
name: k8s-runners-linux
namespace: actions-runner-system
spec:
template:
spec:
image: