#Integrate with Alauda DevOps Pipelines

This page shows how to leverage the Alauda Build of Kueue's scheduling and resource management capabilities when running the Alauda DevOps Pipelines(Tekton Pipelines).

#Prerequisites

• You have installed the Alauda DevOps Pipelines.
• You have installed the Alauda Build of Kueue.
• You have installed the Alauda Build of Hami(for demonstrating vGPU).
• The Alauda Container Platform Web CLI has communication with your cluster.

#Procedure

1. Create a project and namespace in Alauda Container Platform, for example, the project name is test, and the namespace name is test-1.

2. Create the assets by running the following command:

   cat <<EOF| kubectl create -f -
   apiVersion: kueue.x-k8s.io/v1beta2
   kind: ClusterQueue
   metadata:
     name: cluster-queue
   spec:
     namespaceSelector: {}
     resourceGroups:
     - coveredResources: ["cpu", "memory", "pods", "nvidia.com/gpualloc", "nvidia.com/total-gpucores", "nvidia.com/total-gpumem"]
       flavors:
       - name: "default-flavor"
         resources:
         - name: "cpu"
           nominalQuota: 9
         - name: "memory"
           nominalQuota: 36Gi
         - name: "pods"
           nominalQuota: 5
         - name: "nvidia.com/gpualloc"
           nominalQuota: "2"
         - name: "nvidia.com/total-gpucores"
           nominalQuota: "50"
         - name: "nvidia.com/total-gpumem"
           nominalQuota: "20000"
   ---
   apiVersion: kueue.x-k8s.io/v1beta2
   kind: ResourceFlavor
   metadata:
     name: default-flavor
   ---
   apiVersion: kueue.x-k8s.io/v1beta2
   kind: LocalQueue
   metadata:
     namespace: test-1
     name: test
   spec:
     clusterQueue: cluster-queue
   EOF

3. Create a Pipeline resource in the Alauda Container Platform by Web CLI or UI:

   apiVersion: tekton.dev/v1
   kind: Pipeline
   metadata:
     name: test
     namespace: test-1
   spec:
     tasks:
       - name: run-script
         taskSpec:
           description: test
           metadata: {}
           spec: null
           steps:
             - computeResources:
                 limits:
                   cpu: "2"
                   memory: 2Gi
                   nvidia.com/gpualloc: "2"
                   nvidia.com/gpucores: "50"
                   nvidia.com/gpumem: 8k
                 requests:
                   cpu: "1"
                   memory: 1Gi
               image: nvidia/cuda:11.0-base
               imagePullPolicy: IfNotPresent
               name: run-script
               script: |
                 #!/bin/sh
                 nvidia-smi
               securityContext:
                 allowPrivilegeEscalation: false
                 capabilities:
                   drop:
                     - ALL
                 runAsNonRoot: true
                 runAsUser: 65532
                 seccompProfile:
                   type: RuntimeDefault
         timeout: 30m0s

4. Create a PipelineRun resource in the Alauda Container Platform by Web CLI or UI:

   apiVersion: tekton.dev/v1
   kind: PipelineRun
   metadata:
     annotations:
       chains.tekton.dev/signed: "true"
       integrations.tekton.dev/integrations: |
         []
     generateName: test-
     labels:
       tekton.dev/pipeline: test
       kueue.x-k8s.io/queue-name: test
     namespace: test-1
   spec:
     pipelineRef:
       name: test
     taskRunTemplate:
       podTemplate:
         securityContext:
           fsGroup: 65532
           fsGroupChangePolicy: OnRootMismatch
       serviceAccountName: default
     timeouts:
       pipeline: 1h0m0s
   1. kueue.x-k8s.io/queue-name: test label: Specifies the LocalQueue that manages all pods of the PipelineRun.
   2. spec.pipelineRef.name: Specifies the Pipeline resource that is referenced by the PipelineRun.

5. Observe pods of the PipelineRun:

   kubectl -n test-1 get pod | grep test

   You will see that this pod is in a SchedulingGated state:

   test-dw4q7-run-script-pod   0/1     SchedulingGated   0          13s   <none>   <none>   <none>           <none>

6. Update the nvidia.com/total-gpucores quotas:

   cat <<EOF| kubectl apply -f -
   apiVersion: kueue.x-k8s.io/v1beta2
   kind: ClusterQueue
   metadata:
     name: cluster-queue
   spec:
     namespaceSelector: {}
     resourceGroups:
     - coveredResources: ["cpu", "memory", "pods", "nvidia.com/gpualloc", "nvidia.com/total-gpucores", "nvidia.com/total-gpumem"]
       flavors:
       - name: "default-flavor"
         resources:
         - name: "cpu"
           nominalQuota: 9
         - name: "memory"
           nominalQuota: 36Gi
         - name: "pods"
           nominalQuota: 5
         - name: "nvidia.com/gpualloc"
           nominalQuota: "2"
         - name: "nvidia.com/total-gpucores"
           nominalQuota: "100"
         - name: "nvidia.com/total-gpumem"
           nominalQuota: "20000"
   EOF

   You will see that this pod is in a Running state:

   test-dw4q7-run-script-pod   1/1     Running   0          13s   <none>   <none>   <none>           <none>