The CAST AI autoscaler supports running your workloads on Spot/Preemptible instances. This guide will help you configure and run it in 5 minutes.
When to use: spot instances are optional
When a pod is marked only with
tolerations, the Kubernetes scheduler could place such a pod/pods on regular nodes as well.
... tolerations: - key: scheduling.cast.ai/spot operator: Exists ...
When to use: only use spot instances
If you want to make sure that a pod is scheduled on spot instances only, add
nodeSelector as well as per the example below.
The autoscaler will then ensure that only a spot instance is picked whenever your pod requires additional workload in the cluster.
... tolerations: - key: scheduling.cast.ai/spot operator: Exists nodeSelector: scheduling.cast.ai/spot: "true" ...
Step-by-step deployment on Spot Instance¶
In this step-by-step guide, we demonstrate how to use Spot Instances with your CAST AI clusters.
To do that, we will use an example NGINX deployment configured to run only on Spot/Preemptible instances.
1. Enable relevant policies¶
To start using Spot instances autoscaler enable the following policies under the
Policies menu in the UI:
- Spot/Preemptible instances policy
This policy allows the autoscaler to use spot instances
Unschedulable pods policy
- This policy requests an additional workload to be scheduled based on your deployment requirements (i.e. run on spot instances)
2. Example deployment¶
Save the following yaml file, and name it:
apiVersion: apps/v1 kind: Deployment metadata: name: nginx-deployment labels: app: nginx spec: replicas: 1 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: nodeSelector: scheduling.cast.ai/spot: "true" tolerations: - key: scheduling.cast.ai/spot operator: Exists containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 resources: requests: cpu: '2' limits: cpu: '3'
2.1. Apply the example deployment¶
kubeconfig set in your current shell session, you can execute the following (or use other means of applying deployment files):
kubectl apply -f ngninx.yaml
2.2. Wait several minutes¶
Once the deployment is created, it will take up to several minutes for the autoscaler to pick up the information about your pending deployment and schedule the relevant workloads in order to satisfy the deployment needs, such as:
- This deployment tolerates spot instances
- This deployment must run only on spot instances
3. Spot Instance added¶
- You can see your newly added spot instance in the cluster node list.
3.1. AWS instance list¶
Just to double-check, go to the AWS console and check that the added node has the
Lifecycle: spot indicator.