kedro/runner/parallel_runner.py
def _run_node_synchronization(
    node: Node, catalog: DataCatalog, is_async: bool = False, run_id: str = None
) -> Node:
    """Run a single `Node` with inputs from and outputs to the `catalog`.
    `KedroContext` class is initialized in every subprocess because of Windows
    (latest OSX with Python 3.8) limitation.
    Windows has no "fork", so every subprocess is a brand new process created via "spawn",
    and KedroContext needs to be created in every subprocess in order to make
    KedroContext logging setup and hook manager work.

    Args:
        node: The ``Node`` to run.
        catalog: A ``DataCatalog`` containing the node's inputs and outputs.
        is_async: If True, the node inputs and outputs are loaded and saved
            asynchronously with threads. Defaults to False.
        run_id: The id of the pipeline run.

    Returns:
        The node argument.

    """

    if multiprocessing.get_start_method() == "spawn":  # type: ignore
        # pylint: disable=import-outside-toplevel
        import kedro.framework.context.context as context  # pragma: no cover

        context.load_context(Path.cwd())  # pragma: no cover
    # The hard-coded current working directory causes
    # parallel runner to not work in notebook environment,
    # but we will revisit this when we work on access `project_path`
    # from within the runner and data in KedroContext
    # See https://github.com/quantumblacklabs/private-kedro/issues/701.
    return run_node(node, catalog, is_async, run_id)
Run a single `Node` with inputs from and outputs to the `catalog`.
    `KedroContext` class is initialized in every subprocess because of Windows
    (latest OSX with Python 3.8) limitation.
    Windows has no "fork", so every subprocess is a brand new process created via "spawn",
    and KedroContext needs to be created in every subprocess in order to make
    KedroContext logging setup and hook manager work.

    Args:
        node: The ``Node`` to run.
        catalog: A ``DataCatalog`` containing the node's inputs and outputs.
        is_async: If True, the node inputs and outputs are loaded and saved
            asynchronously with threads. Defaults to False.
        run_id: The id of the pipeline run.

    Returns:
        The node argument.

    
Run a single `Node` with inputs from and outputs to the `catalog`.
`KedroContext` class is initialized in every subprocess because of Windows
(latest OSX with Python 3.8) limitation.
Windows has no "fork", so every subprocess is a brand new process created via "spawn",
and KedroContext needs to be created in every subprocess in order to make
KedroContext logging setup and hook manager work.
Run a single ` Node ` with inputs from and outputs to the ` catalog ` . ` KedroContext ` class is initialized in every subprocess because of Windows ( latest OSX with Python 3 . 8 ) limitation . Windows has no " fork " so every subprocess is a brand new process created via " spawn " and KedroContext needs to be created in every subprocess in order to make KedroContext logging setup and hook manager work .
def _run_node_synchronization(
    node: Node, catalog: DataCatalog, is_async: bool = False, run_id: str = None
) -> Node:
    if multiprocessing.get_start_method() == "spawn":  
        import kedro.framework.context.context as context  
        context.load_context(Path.cwd())  
    return run_node(node, catalog, is_async, run_id)
def _run_node_synchronization ( node : Node , catalog : DataCatalog , is_async : bool = False , run_id : str = None ) -> Node : if multiprocessing . get_start_method ( ) == "spawn" : import kedro . framework . context . context as context context . load_context ( Path . cwd ( ) ) return run_node ( node , catalog , is_async , run_id )
Run a single `Node` with inputs from and outputs to the `catalog`.
Run a single ` Node ` with inputs from and outputs to the ` catalog ` .
"""Run a single `Node` with inputs from and outputs to the `catalog`.
    `KedroContext` class is initialized in every subprocess because of Windows
    (latest OSX with Python 3.8) limitation.
    Windows has no "fork", so every subprocess is a brand new process created via "spawn",
    and KedroContext needs to be created in every subprocess in order to make
    KedroContext logging setup and hook manager work.

    Args:
        node: The ``Node`` to run.
        catalog: A ``DataCatalog`` containing the node's inputs and outputs.
        is_async: If True, the node inputs and outputs are loaded and saved
            asynchronously with threads. Defaults to False.
        run_id: The id of the pipeline run.

    Returns:
        The node argument.

    """ # type: ignore # pylint: disable=import-outside-toplevel # pragma: no cover # pragma: no cover # The hard-coded current working directory causes # parallel runner to not work in notebook environment, # but we will revisit this when we work on access `project_path` # from within the runner and data in KedroContext # See https://github.com/quantumblacklabs/private-kedro/issues/701.
The node argument.
The node argument .
node
Node
The ``Node`` to run.
The ` ` Node ` ` to run .
catalog
DataCatalog
A ``DataCatalog`` containing the node's inputs and outputs.
A ` ` DataCatalog ` ` containing the node ' s inputs and outputs .
is_async
bool
If True, the node inputs and outputs are loaded and saved
asynchronously with threads. Defaults to False.
If True the node inputs and outputs are loaded and saved asynchronously with threads . Defaults to False .
run_id
str
The id of the pipeline run.
The id of the pipeline run .