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tfx.orchestration.kubeflow.v2.container.kubeflow_v2_run_executor.main

Parses the arguments for _run_executor() then invokes it.

argv Unparsed arguments for run_executor.py. Known argument names include --executor_class_path: Python class of executor in format of .. --json_serialized_invocation_args: Full JSON-serialized parameters for this execution. The remaining part of the arguments will be parsed as the beam args used by each component executors. Some commonly used beam args are as follows: --runner: The beam pipeline runner environment. Can be DirectRunner (for running locally) or DataflowRunner (for running on GCP Dataflow service). --project: The GCP project ID. Neede when runner==DataflowRunner --direct_num_workers: Number of threads or subprocesses executing the work load. For more about the beam arguments please refer to: https://cloud.google.com/dataflow/docs/guides/specifying-exec-params

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