github.com/kubeflow/training-operator@v1.7.0/sdk/python/docs/KubeflowOrgV1RunPolicy.md (about)

     1  # KubeflowOrgV1RunPolicy
     2  
     3  RunPolicy encapsulates various runtime policies of the distributed training job, for example how to clean up resources and how long the job can stay active.
     4  ## Properties
     5  Name | Type | Description | Notes
     6  ------------ | ------------- | ------------- | -------------
     7  **active_deadline_seconds** | **int** | Specifies the duration in seconds relative to the startTime that the job may be active before the system tries to terminate it; value must be positive integer. | [optional] 
     8  **backoff_limit** | **int** | Optional number of retries before marking this job failed. | [optional] 
     9  **clean_pod_policy** | **str** | CleanPodPolicy defines the policy to kill pods after the job completes. Default to None. | [optional] 
    10  **scheduling_policy** | [**KubeflowOrgV1SchedulingPolicy**](KubeflowOrgV1SchedulingPolicy.md) |  | [optional] 
    11  **suspend** | **bool** | suspend specifies whether the Job controller should create Pods or not. If a Job is created with suspend set to true, no Pods are created by the Job controller. If a Job is suspended after creation (i.e. the flag goes from false to true), the Job controller will delete all active Pods and PodGroups associated with this Job. Users must design their workload to gracefully handle this. Suspending a Job will reset the StartTime field of the Job.  Defaults to false. | [optional] 
    12  **ttl_seconds_after_finished** | **int** | TTLSecondsAfterFinished is the TTL to clean up jobs. It may take extra ReconcilePeriod seconds for the cleanup, since reconcile gets called periodically. Default to infinite. | [optional] 
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