TFX is designed to be portable to multiple environments and orchestration frameworks. Developers can create custom orchestrators or add additional orchestrators in addition to the default orchestrators that are supported by TFX, namely Local, Vertex AI, Airflow and Kubeflow.
All orchestrators must inherit from TfxRunner. TFX orchestrators take the logical pipeline object, which contains pipeline args, components, and DAG, and are responsible for scheduling components of the TFX pipeline based on the dependencies defined by the DAG.
For example, let's look at how to create a custom orchestrator with BaseComponentLauncher. BaseComponentLauncher already handles driver, executor, and publisher of a single component. The new orchestrator just needs to schedule ComponentLaunchers based on the DAG. A simple orchestrator is provided as the LocalDagRunner, which runs the components one by one in DAG's topological order.
This orchestrator can be used in the Python DSL:
def _create_pipeline(...) -> dsl.Pipeline: ... return dsl.Pipeline(...) if __name__ == '__main__': orchestration.LocalDagRunner().run(_create_pipeline(...))
To run above Python DSL file (assuming it is named dsl.py), simply do the following: