Module core.backends.orchestrator.beam.zenml_beam_orchestrator

Classes

ZenMLBeamlDagRunner(beam_orchestrator_args: Union[List[str], NoneType] = None, config: Union[tfx.orchestration.config.pipeline_config.PipelineConfig, NoneType] = None) : This is the same as the super class from tfx: tfx.orchestration.beam.beam_dag_runner.BeamDagRunner with the exception being that the pipeline_run is not overridden. Full credit to Google LLC for the original source code found at: https://github.com/tensorflow/tfx/tree/master/tfx/orchestration/beam/

Initializes BeamDagRunner as a TFX orchestrator.

Args:
  beam_orchestrator_args: beam args for the beam orchestrator. Note that
    this is different from the beam_pipeline_args within
    additional_pipeline_args, which is for beam pipelines in components.
  config: Optional pipeline config for customizing the launching of each
    component. Defaults to pipeline config that supports
    InProcessComponentLauncher and DockerComponentLauncher.

### Ancestors (in MRO)

* tfx.orchestration.beam.beam_dag_runner.BeamDagRunner
* tfx.orchestration.tfx_runner.TfxRunner

### Methods

`run(self, tfx_pipeline: tfx.orchestration.pipeline.Pipeline) ‑> NoneType`
:   Deploys given logical pipeline on Beam.
    
    Args:
      tfx_pipeline: Logical pipeline containing pipeline args and components.