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You can automatically increase or decrease the number of self-hosted runners in your environment in response to the webhook events you receive with a particular label. For example, you can create automation that adds a new self-hosted runner each time you receive a
workflow_job webhook event with the
queued activity, which notifies you that a new job is ready for processing. The webhook payload includes label data, so you can identify the type of runner the job is requesting. Once the job has finished, you can then create automation that removes the runner in response to the
GitHub recommends and partners closely with two open source projects that you can use for autoscaling your runners. One or both solutions may be suitable, based on your needs.
The following repositories have detailed instructions for setting up these autoscalers:
- actions-runner-controller/actions-runner-controller - A Kubernetes controller for GitHub Actions self-hosted runners.
- philips-labs/terraform-aws-github-runner - A Terraform module for scalable GitHub Actions runners on Amazon Web Services.
Each solution has certain specifics that may be important to consider:
|ランタイム||Kubernetes||Linux and Windows VMs|
|Supported Clouds||Azure, Amazon Web Services, Google Cloud Platform, on-premises||Amazon Web Services|
|Where runners can be scaled||Enterprise, organization, and repository levels. By runner label and runner group.||Organization and repository levels. By runner label and runner group.|
|How runners can be scaled||Webhook events, Scheduled, Pull-based||Webhook events, Scheduled (org-level runners only)|
GitHub recommends implementing autoscaling with ephemeral self-hosted runners; autoscaling with persistent self-hosted runners is not recommended. In certain cases, GitHub cannot guarantee that jobs are not assigned to persistent runners while they are shut down. With ephemeral runners, this can be guaranteed because GitHub only assigns one job to a runner.
This approach allows you to manage your runners as ephemeral systems, since you can use automation to provide a clean environment for each job. This helps limit the exposure of any sensitive resources from previous jobs, and also helps mitigate the risk of a compromised runner receiving new jobs.
To add an ephemeral runner to your environment, include the
--ephemeral parameter when registering your runner using
./config.sh --url https://github.com/octo-org --token example-token --ephemeral
The GitHub Actions service will then automatically de-register the runner after it has processed one job. You can then create your own automation that wipes the runner after it has been de-registered.
Note: If a job is labeled for a certain type of runner, but none matching that type are available, the job does not immediately fail at the time of queueing. Instead, the job will remain queued until the 24 hour timeout period expires.
You can create your own autoscaling environment by using payloads received from the
workflow_job webhook. This webhook is available at the repository, organization, and enterprise levels, and the payload for this event contains an
action key that corresponds to the stages of a workflow job's life-cycle; for example when jobs are
completed. You must then create your own scaling automation in response to these webhook payloads.
- For more information about the
workflow_jobwebhook, see "Webhook events and payloads."
- To learn how to work with webhooks, see "Creating webhooks."
You can register and delete repository and organization self-hosted runners using the API. To authenticate to the API, your autoscaling implementation can use an access token or a GitHub app.
Your access token will require the following scope:
- For private repositories, use an access token with the
- For public repositories, use an access token with the
- For organizations, use an access token with the
To authenticate using a GitHub App, it must be assigned the following permissions:
- For repositories, assign the
- For organizations, assign the
You can register and delete enterprise self-hosted runners using the API. To authenticate to the API, your autoscaling implementation can use an access token.
Your access token will require the