Module core.backends.orchestrator.kubernetes.orchestrator_kubernetes_backend

Definition of the Kubernetes Orchestrator Backend


OrchestratorKubernetesBackend(image: str = '', job_prefix: str = 'zenml-', extra_labels: Dict[str, Any] = None, extra_annotations: Dict[str, Any] = None, namespace: str = None, image_pull_policy: str = 'IfNotPresent', kubernetes_config_path: str = '/home/runner/.kube/config', **kwargs) : Runs pipeline on a Kubernetes cluster.

This orchestrator creates a .tar.gz of the current ZenML repository, sends
it over to the artifact store, then launches a job in a Kubernetes cluster
taken from your environment or specified via a passed-on kubectl config.

    image: the Docker Image to be used for this ZenML pipeline
    job_prefix: a custom prefix for your Jobs in Kubernetes
        (default: 'zenml-')
    extra_labels: additional labels for your Jobs in Kubernetes
    extra_annotations: additional annotations for your Jobs in Kubernetes
    namespace: a custom Kubernetes namespace for this pipeline
        (default: 'default')
    image_pull_policy: Kubernetes image pull policy.
        One of ['Always', 'Never', 'IfNotPresent'].
        (default: 'IfNotPresent')
    kubernetes_config_path: Path to your Kubernetes cluster connection config.
        (default: '~/.kube/config'

### Ancestors (in MRO)

* zenml.core.backends.orchestrator.local.orchestrator_local_backend.OrchestratorLocalBackend
* zenml.core.backends.base_backend.BaseBackend

### Class variables


### Methods

`create_job_object(self, config)`

`launch_job(self, config: Dict[str, Any])`

`run(self, config: Dict[str, Any])`
:   This run function essentially calls an underlying TFX orchestrator run.
    However it is meant as a higher level abstraction with some
    opinionated decisions taken.
        config: a ZenML config dict