View source on GitHub
|
TFX interactive context for interactive TFX notebook development.
tfx.orchestration.experimental.interactive.interactive_context.InteractiveContext(
pipeline_name=None, pipeline_root=None, metadata_connection_config=None
)
Used in the notebooks
| Used in the tutorials |
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Args:
pipeline_name: Optional name of the pipeline for ML Metadata tracking purposes. If not specified, a name will be generated for you.pipeline_root: Optional path to the root of the pipeline's outputs. If not specified, an ephemeral temporary directory will be created and used.metadata_connection_config: Optional metadata_store_pb2.ConnectionConfig instance used to configure connection to a ML Metadata connection. If not specified, an ephemeral SQLite MLMD connection contained in the pipeline_root directory with file name "metadata.sqlite" will be used.
Methods
export_to_pipeline
export_to_pipeline(
*args, **kwargs
)
Exports a notebook to a .py file as a runnable pipeline.
Args:
notebook_filepath: String path of the notebook file, e.g. '/path/to/notebook.ipynb'.export_filepath: String path for the exported pipeline python file, e.g. '/path/to/exported_pipeline.py'.runner_type: String indicating type of runner, e.g. 'beam', 'airflow'.
run
run(
*args, **kwargs
)
Run a given TFX component in the interactive context.
Args:
component: Component instance to be run.enable_cache: whether caching logic should be enabled in the driver.beam_pipeline_args: Optional Beam pipeline args for beam jobs within executor. Executor will use beam DirectRunner as Default.
Returns:
execution_result.ExecutionResult object.
show
show(
*args, **kwargs
)
Show the given object in an IPython notebook display.