def __init__(
self,
nodes: Iterable[Union[Node, "Pipeline"]],
*,
tags: Union[str, Iterable[str]] = None,
):
"""Initialise ``Pipeline`` with a list of ``Node`` instances.
Args:
nodes: The iterable of nodes the ``Pipeline`` will be made of. If you
provide pipelines among the list of nodes, those pipelines will
be expanded and all their nodes will become part of this
new pipeline.
tags: Optional set of tags to be applied to all the pipeline nodes.
Raises:
ValueError:
When an empty list of nodes is provided, or when not all
nodes have unique names.
CircularDependencyError:
When visiting all the nodes is not
possible due to the existence of a circular dependency.
OutputNotUniqueError:
When multiple ``Node`` instances produce the same output.
ConfirmNotUniqueError:
When multiple ``Node`` instances attempt to confirm the same
dataset.
Example:
::
>>> from kedro.pipeline import Pipeline
>>> from kedro.pipeline import node
>>>
>>> # In the following scenario first_ds and second_ds
>>> # are data sets provided by io. Pipeline will pass these
>>> # data sets to first_node function and provides the result
>>> # to the second_node as input.
>>>
>>> def first_node(first_ds, second_ds):
>>> return dict(third_ds=first_ds+second_ds)
>>>
>>> def second_node(third_ds):
>>> return third_ds
>>>
>>> pipeline = Pipeline([
>>> node(first_node, ['first_ds', 'second_ds'], ['third_ds']),
>>> node(second_node, dict(third_ds='third_ds'), 'fourth_ds')])
>>>
>>> pipeline.describe()
>>>
"""
if nodes is None:
raise ValueError(
"`nodes` argument of `Pipeline` is None. It must be an "
"iterable of nodes and/or pipelines instead."
)
nodes = list(nodes) # in case it's a generator
_validate_duplicate_nodes(nodes)
nodes = list(
chain.from_iterable(
[[n] if isinstance(n, Node) else n.nodes for n in nodes]
)
)
_validate_transcoded_inputs_outputs(nodes)
_tags = set(_to_list(tags))
nodes = [n.tag(_tags) for n in nodes]
self._nodes_by_name = {node.name: node for node in nodes}
_validate_unique_outputs(nodes)
_validate_unique_confirms(nodes)
# input -> nodes with input
self._nodes_by_input = defaultdict(set) # type: Dict[str, Set[Node]]
for node in nodes:
for input_ in node.inputs:
self._nodes_by_input[_strip_transcoding(input_)].add(node)
# output -> node with output
self._nodes_by_output = {} # type: Dict[str, Node]
for node in nodes:
for output in node.outputs:
self._nodes_by_output[_strip_transcoding(output)] = node
self._nodes = nodes
self._topo_sorted_nodes = _topologically_sorted(self.node_dependencies)
def __init__(
self,
nodes: Iterable[Union[Node, "Pipeline"]],
*,
name: str = None,
tags: Union[str, Iterable[str]] = None
): # pylint: disable=missing-type-doc
"""Initialise ``Pipeline`` with a list of ``Node`` instances.
Args:
nodes: The list of nodes the ``Pipeline`` will be made of. If you
provide pipelines among the list of nodes, those pipelines will
be expanded and all their nodes will become part of this
new pipeline.
name: (DEPRECATED, use `tags` method instead) The name of the pipeline.
If specified, this name will be used to tag all of the nodes
in the pipeline.
tags: Optional set of tags to be applied to all the pipeline nodes.
Raises:
ValueError:
When an empty list of nodes is provided, or when not all
nodes have unique names.
CircularDependencyError:
When visiting all the nodes is not
possible due to the existence of a circular dependency.
OutputNotUniqueError:
When multiple ``Node`` instances produce the same output.
Example:
::
>>> from kedro.pipeline import Pipeline
>>> from kedro.pipeline import node
>>>
>>> # In the following scenario first_ds and second_ds
>>> # are data sets provided by io. Pipeline will pass these
>>> # data sets to first_node function and provides the result
>>> # to the second_node as input.
>>>
>>> def first_node(first_ds, second_ds):
>>> return dict(third_ds=first_ds+second_ds)
>>>
>>> def second_node(third_ds):
>>> return third_ds
>>>
>>> pipeline = Pipeline([
>>> node(first_node, ['first_ds', 'second_ds'], ['third_ds']),
>>> node(second_node, dict(third_ds='third_ds'), 'fourth_ds')])
>>>
>>> pipeline.describe()
>>>
"""
_validate_no_node_list(nodes)
nodes = list(
chain.from_iterable(
[[n] if isinstance(n, Node) else n.nodes for n in nodes]
)
)
_validate_duplicate_nodes(nodes)
_validate_transcoded_inputs_outputs(nodes)
_tags = set(_to_list(tags))
if name:
warnings.warn(
"`name` parameter is deprecated for the `Pipeline`"
" constructor, use `Pipeline.tag` method instead",
DeprecationWarning,
)
_tags.add(name)
nodes = [n.tag(_tags) for n in nodes]
self._name = name
self._nodes_by_name = {node.name: node for node in nodes}
_validate_unique_outputs(nodes)
# input -> nodes with input
self._nodes_by_input = defaultdict(set) # type: Dict[str, Set[Node]]
for node in nodes:
for input_ in node.inputs:
self._nodes_by_input[_get_transcode_compatible_name(input_)].add(node)
# output -> node with output
self._nodes_by_output = {} # type: Dict[str, Node]
for node in nodes:
for output in node.outputs:
self._nodes_by_output[_get_transcode_compatible_name(output)] = node
self._nodes = nodes
self._topo_sorted_nodes = _topologically_sorted(self.node_dependencies)
