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View Job Workloads

Once a job is created, it will be displayed in the job list.

  1. In the job list, click the ┇ on the right side of a job and select Job Workload Details .

    Click Menu Item

  2. A pop-up window will appear asking you to choose which Pod to view. Click Enter .

    Pop-up Enter

  3. You will be redirected to the container management interface, where you can view the container’s working status, labels and annotations, and any events that have occurred.

    View Details

  4. You can also view detailed logs of the current Pod for the recent period. By default, 100 lines of logs are displayed. To view more detailed logs or to download logs, click the blue Insight text at the top.

    Logs

  5. Additionally, you can use the ... in the upper right corner to view the current Pod's YAML, and to upload or download files. Below is an example of a Pod's YAML.

kind: Pod
apiVersion: v1
metadata:
  name: neko-tensorboard-job-test-202404181843-skxivllb-worker-0
  namespace: default
  uid: ddedb6ff-c278-47eb-ae1e-0de9b7c62f8c
  resourceVersion: '41092552'
  creationTimestamp: '2024-04-18T10:43:36Z'
  labels:
    training.kubeflow.org/job-name: neko-tensorboard-job-test-202404181843-skxivllb
    training.kubeflow.org/operator-name: pytorchjob-controller
    training.kubeflow.org/replica-index: '0'
    training.kubeflow.org/replica-type: worker
  annotations:
    cni.projectcalico.org/containerID: 0cfbb9af257d5e69027c603c6cb2d3890a17c4ae1a145748d5aef73a10d7fbe1
    cni.projectcalico.org/podIP: ''
    cni.projectcalico.org/podIPs: ''
    hami.io/bind-phase: success
    hami.io/bind-time: '1713437016'
    hami.io/vgpu-devices-allocated: GPU-29d5fa0d-935b-2966-aff8-483a174d61d1,NVIDIA,1024,20:;
    hami.io/vgpu-devices-to-allocate: ;
    hami.io/vgpu-node: worker-a800-1
    hami.io/vgpu-time: '1713437016'
    k8s.v1.cni.cncf.io/network-status: |-
      [{
          "name": "kube-system/calico",
          "ips": [
              "10.233.97.184"
          ],
          "default": true,
          "dns": {}
      }]
    k8s.v1.cni.cncf.io/networks-status: |-
      [{
          "name": "kube-system/calico",
          "ips": [
              "10.233.97.184"
          ],
          "default": true,
          "dns": {}
      }]
  ownerReferences:
    - apiVersion: kubeflow.org/v1
      kind: PyTorchJob
      name: neko-tensorboard-job-test-202404181843-skxivllb
      uid: e5a8b05d-1f03-4717-8e1c-4ec928014b7b
      controller: true
      blockOwnerDeletion: true
spec:
  volumes:
    - name: 0-dataset-pytorch-examples
      persistentVolumeClaim:
        claimName: pytorch-examples
    - name: kube-api-access-wh9rh
      projected:
        sources:
          - serviceAccountToken:
              expirationSeconds: 3607
              path: token
          - configMap:
              name: kube-root-ca.crt
              items:
                - key: ca.crt
                  path: ca.crt
          - downwardAPI:
              items:
                - path: namespace
                  fieldRef:
                    apiVersion: v1
                    fieldPath: metadata.namespace
        defaultMode: 420
  containers:
    - name: pytorch
      image: m.daocloud.io/docker.io/pytorch/pytorch
      command:
        - bash
      args:
        - '-c'
        - >-
          ls -la /root && which pip && pip install pytorch_lightning tensorboard
          && python /root/Git/pytorch/examples/mnist/main.py
      ports:
        - name: pytorchjob-port
          containerPort: 23456
          protocol: TCP
      env:
        - name: PYTHONUNBUFFERED
          value: '1'
        - name: PET_NNODES
          value: '1'
      resources:
        limits:
          cpu: '4'
          memory: 8Gi
          nvidia.com/gpucores: '20'
          nvidia.com/gpumem: '1024'
          nvidia.com/vgpu: '1'
        requests:
          cpu: '4'
          memory: 8Gi
          nvidia.com/gpucores: '20'
          nvidia.com/gpumem: '1024'
          nvidia.com/vgpu: '1'
      volumeMounts:
        - name: 0-dataset-pytorch-examples
          mountPath: /root/Git/pytorch/examples
        - name: kube-api-access-wh9rh
          readOnly: true
          mountPath: /var/run/secrets/kubernetes.io/serviceaccount
      terminationMessagePath: /dev/termination-log
      terminationMessagePolicy: File
      imagePullPolicy: Always
  restartPolicy: Never
  terminationGracePeriodSeconds: 30
  dnsPolicy: ClusterFirst
  serviceAccountName: default
  serviceAccount: default
  nodeName: worker-a800-1
  securityContext: {}
  affinity: {}
  schedulerName: hami-scheduler
  tolerations:
    - key: node.kubernetes.io/not-ready
      operator: Exists
      effect: NoExecute
      tolerationSeconds: 300
    - key: node.kubernetes.io/unreachable
      operator: Exists
      effect: NoExecute
      tolerationSeconds: 300
  priorityClassName: baize-high-priority
  priority: 100000
  enableServiceLinks: true
  preemptionPolicy: PreemptLowerPriority
status:
  phase: Succeeded
  conditions:
    - type: Initialized
      status: 'True'
      lastProbeTime: null
      lastTransitionTime: '2024-04-18T10:43:36Z'
      reason: PodCompleted
    - type: Ready
      status: 'False'
      lastProbeTime: null
      lastTransitionTime: '2024-04-18T10:46:34Z'
      reason: PodCompleted
    - type: ContainersReady
      status: 'False'
      lastProbeTime: null
      lastTransitionTime: '2024-04-18T10:46:34Z'
      reason: PodCompleted
    - type: PodScheduled
      status: 'True'
      lastProbeTime: null
      lastTransitionTime: '2024-04-18T10:43:36Z'
  hostIP: 10.20.100.211
  podIP: 10.233.97.184
  podIPs:
    - ip: 10.233.97.184
  startTime: '2024-04-18T10:43:36Z'
  containerStatuses:
    - name: pytorch
      state:
        terminated:
          exitCode: 0
          reason: Completed
          startedAt: '2024-04-18T10:43:39Z'
          finishedAt: '2024-04-18T10:46:34Z'
          containerID: >-
            containerd://09010214bcf3315e81d38fba50de3943c9d2b48f50a6cc2e83f8ef0e5c6eeec1
      lastState: {}
      ready: false
      restartCount: 0
      image: m.daocloud.io/docker.io/pytorch/pytorch:latest
      imageID: >-
        m.daocloud.io/docker.io/pytorch/pytorch@sha256:11691e035a3651d25a87116b4f6adc113a27a29d8f5a6a583f8569e0ee5ff897
      containerID: >-
        containerd://09010214bcf3315e81d38fba50de3943c9d2b48f50a6cc2e83f8ef0e5c6eeec1
      started: false
  qosClass: Guaranteed

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