Definition of the data Processing Backend
: Use this class to run a ZenML pipeline locally.
Every ZenML pipeline runs in backends. A dedicated processing backend can be used to efficiently process large amounts of incoming data in parallel, potentially distributed across multiple machines. This can happen on local processing backends as well as cloud-based variants like Google Cloud Dataflow. More powerful machines with higher core counts and clock speeds can be leveraged to increase processing throughput significantly. ### Ancestors (in MRO) * zenml.core.backends.base_backend.BaseBackend ### Class variables `BACKEND_KEY` : `BACKEND_TYPE` : ### Methods `get_beam_args(self, pipeline_name: str = None, pipeline_root: str = None) ‑> Union[List[str], NoneType]` : Returns a list of beam args for the pipeline. Args: pipeline_name: Name of the pipeline. pipeline_root: Root dir of pipeline.