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K8S Resource Log Selector

Note

This example is a replication of an Argo Workflow example in Hera. The upstream example can be found here.

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from hera.workflows import Resource, Workflow

with Workflow(generate_name="k8s-jobs-log-selector-", entrypoint="tf-jobtmpl") as w:
    tf_jobtmpl = Resource(
        name="tf-jobtmpl",
        action="create",
        success_condition="status.replicaStatuses.Worker.succeeded = 2",
        failure_condition="status.replicaStatuses.Worker.failed > 0",
        manifest=r"""apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
  name: tfjob-examples
spec:
  tfReplicaSpecs:
     Worker:
       replicas: 2
       restartPolicy: Never
       template:
         metadata:
           # We add this label to the pods created by TFJob custom resource to inform Argo Workflows
           # that we want to include the logs from the created pods. Once the pods are created with this
           # label, you can then use `argo logs -c tensorflow` to the logs from this particular container.
           # Note that `workflow.name` is a supported global variable provided by Argo Workflows.
           #
           # The Kubeflow training controller will take this CRD and automatically created worker pods with
           # labels, such as `job-role` and `replica-index`. If you'd like to query logs for pods with
           # specific labels, you can specify the label selector explicitly via `argo logs -l <logs-label-selector>`.
           # For example, you can use `argo logs -c tensorflow -l replica-index=0` to see the first worker pod's logs.
           labels:
             workflows.argoproj.io/workflow: {{workflow.name}}
         spec:
           containers:
             - name: tensorflow
               image: "Placeholder for TensorFlow distributed training image"
""",
    )

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apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: k8s-jobs-log-selector-
spec:
  entrypoint: tf-jobtmpl
  templates:
  - name: tf-jobtmpl
    resource:
      action: create
      failureCondition: status.replicaStatuses.Worker.failed > 0
      manifest: "apiVersion: kubeflow.org/v1\nkind: TFJob\nmetadata:\n  name: tfjob-examples\n\
        spec:\n  tfReplicaSpecs:\n     Worker:\n       replicas: 2\n       restartPolicy:\
        \ Never\n       template:\n         metadata:\n           # We add this label\
        \ to the pods created by TFJob custom resource to inform Argo Workflows\n\
        \           # that we want to include the logs from the created pods. Once\
        \ the pods are created with this\n           # label, you can then use `argo\
        \ logs -c tensorflow` to the logs from this particular container.\n      \
        \     # Note that `workflow.name` is a supported global variable provided\
        \ by Argo Workflows.\n           #\n           # The Kubeflow training controller\
        \ will take this CRD and automatically created worker pods with\n        \
        \   # labels, such as `job-role` and `replica-index`. If you'd like to query\
        \ logs for pods with\n           # specific labels, you can specify the label\
        \ selector explicitly via `argo logs -l <logs-label-selector>`.\n        \
        \   # For example, you can use `argo logs -c tensorflow -l replica-index=0`\
        \ to see the first worker pod's logs.\n           labels:\n             workflows.argoproj.io/workflow:\
        \ {{workflow.name}}\n         spec:\n           containers:\n            \
        \ - name: tensorflow\n               image: \"Placeholder for TensorFlow distributed\
        \ training image\"\n"
      successCondition: status.replicaStatuses.Worker.succeeded = 2

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