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sgn.apps

Pipeline class and related utilities to establish and execute a graph of element tasks.

Graph

Base class for managing element graphs and pad registries.

This class provides the core functionality for building directed acyclic graphs of elements and pads. It handles element insertion, pad registration, and implicit/explicit linking between pads.

Both Pipeline and composed elements use this class for graph management.

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class Graph:
    """Base class for managing element graphs and pad registries.

    This class provides the core functionality for building directed acyclic graphs
    of elements and pads. It handles element insertion, pad registration, and
    implicit/explicit linking between pads.

    Both Pipeline and composed elements use this class for graph management.
    """

    def __init__(self) -> None:
        """Initialize an empty graph with registry and element tracking."""
        self._registry: dict[str, Pad | Element] = {}
        self.graph: dict[Pad, set[Pad]] = {}
        self.elements: list[Element] = []

    def __getitem__(self, name: str) -> Pad | Element:
        """Return a graph element or pad by name."""
        return self._registry[name]

    def _insert_element(self, element: Element) -> None:
        """Insert a single element into the graph.

        This is the core insertion logic without sink tracking.
        Subclasses can override to add additional behavior.

        Args:
            element: The element to insert
        """
        assert isinstance(
            element, ElementLike
        ), f"Element {element} is not an instance of a sgn.Element"
        assert (
            element.name not in self._registry
        ), f"Element name '{element.name}' is already in use in this graph"
        self._registry[element.name] = element
        for pad in element.pad_list:
            assert (
                pad.name not in self._registry
            ), f"Pad name '{pad.name}' is already in use in this graph"
            self._registry[pad.name] = pad
        self.graph.update(element.graph)
        self.elements.append(element)

    def insert(
        self,
        *elements: Element,
        link_map: dict[str | SinkPad, str | SourcePad] | None = None,
    ) -> Self:
        """Insert element(s) into the graph.

        Args:
            *elements:
                Iterable[Element], the ordered elements to insert into the graph
            link_map:
                dict[str | SinkPad, str | SourcePad] | None,
                a mapping of sink pad to source pad names to link

        Returns:
            Self, the graph with the elements inserted
        """
        for element in elements:
            self._insert_element(element)
        if link_map is not None:
            self.link(link_map)
        return self

    def link(self, link_map: dict[str | SinkPad, str | SourcePad]) -> Self:
        """Link pads in a graph.

        Args:
            link_map:
                dict[str, str], a mapping of sink pad to source pad names to link.
                Keys are sink pad names, values are source pad names.
                Data flows from value -> key.
        """
        for sink_pad_name, source_pad_name in link_map.items():
            if isinstance(sink_pad_name, str):
                sink_pad = self._registry[sink_pad_name]
            else:
                sink_pad = sink_pad_name
            if isinstance(source_pad_name, str):
                source_pad = self._registry[source_pad_name]
            else:
                source_pad = source_pad_name

            assert isinstance(sink_pad, SinkPad), f"not a sink pad: {sink_pad}"
            assert isinstance(source_pad, SourcePad), f"not a source pad: {source_pad}"

            graph = sink_pad.link(source_pad)
            self.graph.update(graph)

        return self

    def connect(
        self,
        source: Element | ElementGroup | PadSelection,
        sink: Element | ElementGroup | PadSelection,
        link_map: dict[str, str] | None = None,
    ) -> Self:
        """Connect elements, ElementGroups, or PadSelections using implicit linking.

        This method supports multiple linking patterns:
        1. Element-to-element linking with implicit pad matching:
           graph.connect(source_element, sink_element)
        2. Element-to-element linking with explicit mapping:
           graph.connect(source_element, sink_element, link_map={"sink": "source"})
        3. ElementGroup linking (supports elements and pad selections):
           graph.connect(group(s1, s2), sink_element)
           graph.connect(group(source, select(element, "pad1")), sink)
        4. Direct PadSelection linking:
           graph.connect(select(source, "pad1"), sink_element)

        Implicit linking strategies (when no link_map provided):
        1. Exact match: Connect when source and sink pad names are identical
        2. Partial match: Connect all matching pad names (ignores non-matching pads)
        3. N-to-1: Single sink pad, connect all source pads to it
        4. 1-to-N: Single source pad, connect to all sink pads

        Args:
            source:
                Element, ElementGroup, or PadSelection, the source for linking
            sink:
                Element, ElementGroup, or PadSelection, the sink for linking
            link_map:
                dict[str, str], optional, explicit mapping of sink pad names to
                source pad names.

        Returns:
            Self: The graph with the new links added.

        Raises:
            ValueError: If implicit linking strategy is ambiguous.
            TypeError: If arguments are of unexpected types.
        """
        if isinstance(source, SinkElement):
            msg = f"Source '{source.name}' is a SinkElement and has no source pads"
            raise ValueError(msg)
        if isinstance(sink, SourceElement):
            msg = f"Sink '{sink.name}' is a SourceElement and has no sink pads"
            raise ValueError(msg)

        source_pads = source.srcs
        sink_pads = sink.snks

        # Ensure all elements are inserted in graph
        def ensure_elements_inserted(
            obj: Element | ElementGroup | PadSelection,
        ) -> None:
            if isinstance(obj, (SourceElement, TransformElement, SinkElement)):
                if obj.name not in self._registry:
                    self.insert(obj)
            elif isinstance(obj, ElementGroup):
                for element in obj.elements:
                    if element.name not in self._registry:
                        self.insert(element)
            elif isinstance(obj, PadSelection):
                if obj.element.name not in self._registry:
                    self.insert(obj.element)

        ensure_elements_inserted(source)
        ensure_elements_inserted(sink)

        return self._connect_pads(source_pads, sink_pads, link_map)

    def _connect_pads(
        self,
        source_pads: dict[str, SourcePad],
        sink_pads: dict[str, SinkPad],
        link_map: dict[str, str] | None = None,
    ) -> Self:
        """Connect source and sink pads using implicit linking strategies.

        Args:
            source_pads: Dictionary mapping pad names to source pads
            sink_pads: Dictionary mapping pad names to sink pads
            link_map: Optional explicit mapping of sink pad names to source pad names

        Returns:
            Self with the new links added
        """
        resolved_link_map: dict[str | SinkPad, str | SourcePad]
        source_pad_names = set(source_pads.keys())
        sink_pad_names = set(sink_pads.keys())

        # Determine linking strategy
        if link_map:
            # Explicit mapping provided
            resolved_link_map = {}
            for sink_pad_name, source_pad_name in link_map.items():
                if sink_pad_name not in sink_pads:
                    msg = f"sink pad '{sink_pad_name}' not found"
                    raise KeyError(msg)
                if source_pad_name not in source_pads:
                    msg = f"source pad '{source_pad_name}' not found"
                    raise KeyError(msg)

                sink_pad = sink_pads[sink_pad_name]
                source_pad = source_pads[source_pad_name]
                resolved_link_map[sink_pad] = source_pad

            return self.link(resolved_link_map)

        elif source_pad_names == sink_pad_names:
            # One-to-one linking strategy: same pad names
            resolved_link_map = {
                sink_pads[name]: source_pads[name] for name in source_pad_names
            }
            return self.link(resolved_link_map)

        elif source_pad_names & sink_pad_names:
            # Partial matching strategy: connect all matching pad names
            matching_names = source_pad_names & sink_pad_names
            resolved_link_map = {
                sink_pads[name]: source_pads[name] for name in matching_names
            }
            return self.link(resolved_link_map)

        elif len(sink_pad_names) == 1:
            # N-to-one linking strategy
            sink_pad = next(iter(sink_pads.values()))
            for source_pad in source_pads.values():
                self.link({sink_pad: source_pad})
            return self

        elif len(source_pad_names) == 1:
            # One-to-N linking strategy
            source_pad = next(iter(source_pads.values()))
            resolved_link_map = {
                sink_pad: source_pad for sink_pad in sink_pads.values()
            }
            return self.link(resolved_link_map)

        else:
            msg = (
                "unable to determine unambiguous linking strategy from source "
                "and sink pads. an explicit link_map is required."
            )
            raise ValueError(msg)

__getitem__(name)

Return a graph element or pad by name.

Source code in src/sgn/apps.py 
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def __getitem__(self, name: str) -> Pad | Element:
    """Return a graph element or pad by name."""
    return self._registry[name]

__init__()

Initialize an empty graph with registry and element tracking.

Source code in src/sgn/apps.py 
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def __init__(self) -> None:
    """Initialize an empty graph with registry and element tracking."""
    self._registry: dict[str, Pad | Element] = {}
    self.graph: dict[Pad, set[Pad]] = {}
    self.elements: list[Element] = []

connect(source, sink, link_map=None)

Connect elements, ElementGroups, or PadSelections using implicit linking.

This method supports multiple linking patterns: 1. Element-to-element linking with implicit pad matching: graph.connect(source_element, sink_element) 2. Element-to-element linking with explicit mapping: graph.connect(source_element, sink_element, link_map={"sink": "source"}) 3. ElementGroup linking (supports elements and pad selections): graph.connect(group(s1, s2), sink_element) graph.connect(group(source, select(element, "pad1")), sink) 4. Direct PadSelection linking: graph.connect(select(source, "pad1"), sink_element)

Implicit linking strategies (when no link_map provided): 1. Exact match: Connect when source and sink pad names are identical 2. Partial match: Connect all matching pad names (ignores non-matching pads) 3. N-to-1: Single sink pad, connect all source pads to it 4. 1-to-N: Single source pad, connect to all sink pads

Parameters:

Name Type Description Default
source Element | ElementGroup | PadSelection

Element, ElementGroup, or PadSelection, the source for linking

 required
sink Element | ElementGroup | PadSelection

Element, ElementGroup, or PadSelection, the sink for linking

 required
link_map dict[str, str] | None

dict[str, str], optional, explicit mapping of sink pad names to source pad names.

 None

Returns:

Name Type Description
Self Self

The graph with the new links added.

Raises:

Type Description
ValueError

If implicit linking strategy is ambiguous.
TypeError

If arguments are of unexpected types.
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def connect(
    self,
    source: Element | ElementGroup | PadSelection,
    sink: Element | ElementGroup | PadSelection,
    link_map: dict[str, str] | None = None,
) -> Self:
    """Connect elements, ElementGroups, or PadSelections using implicit linking.

    This method supports multiple linking patterns:
    1. Element-to-element linking with implicit pad matching:
       graph.connect(source_element, sink_element)
    2. Element-to-element linking with explicit mapping:
       graph.connect(source_element, sink_element, link_map={"sink": "source"})
    3. ElementGroup linking (supports elements and pad selections):
       graph.connect(group(s1, s2), sink_element)
       graph.connect(group(source, select(element, "pad1")), sink)
    4. Direct PadSelection linking:
       graph.connect(select(source, "pad1"), sink_element)

    Implicit linking strategies (when no link_map provided):
    1. Exact match: Connect when source and sink pad names are identical
    2. Partial match: Connect all matching pad names (ignores non-matching pads)
    3. N-to-1: Single sink pad, connect all source pads to it
    4. 1-to-N: Single source pad, connect to all sink pads

    Args:
        source:
            Element, ElementGroup, or PadSelection, the source for linking
        sink:
            Element, ElementGroup, or PadSelection, the sink for linking
        link_map:
            dict[str, str], optional, explicit mapping of sink pad names to
            source pad names.

    Returns:
        Self: The graph with the new links added.

    Raises:
        ValueError: If implicit linking strategy is ambiguous.
        TypeError: If arguments are of unexpected types.
    """
    if isinstance(source, SinkElement):
        msg = f"Source '{source.name}' is a SinkElement and has no source pads"
        raise ValueError(msg)
    if isinstance(sink, SourceElement):
        msg = f"Sink '{sink.name}' is a SourceElement and has no sink pads"
        raise ValueError(msg)

    source_pads = source.srcs
    sink_pads = sink.snks

    # Ensure all elements are inserted in graph
    def ensure_elements_inserted(
        obj: Element | ElementGroup | PadSelection,
    ) -> None:
        if isinstance(obj, (SourceElement, TransformElement, SinkElement)):
            if obj.name not in self._registry:
                self.insert(obj)
        elif isinstance(obj, ElementGroup):
            for element in obj.elements:
                if element.name not in self._registry:
                    self.insert(element)
        elif isinstance(obj, PadSelection):
            if obj.element.name not in self._registry:
                self.insert(obj.element)

    ensure_elements_inserted(source)
    ensure_elements_inserted(sink)

    return self._connect_pads(source_pads, sink_pads, link_map)

insert(*elements, link_map=None)

Insert element(s) into the graph.

Parameters:

Name Type Description Default
*elements Element

Iterable[Element], the ordered elements to insert into the graph

 ()
link_map dict[str | SinkPad, str | SourcePad] | None

dict[str | SinkPad, str | SourcePad] | None, a mapping of sink pad to source pad names to link

 None

Returns:

Type Description
Self

Self, the graph with the elements inserted
Source code in src/sgn/apps.py 
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def insert(
    self,
    *elements: Element,
    link_map: dict[str | SinkPad, str | SourcePad] | None = None,
) -> Self:
    """Insert element(s) into the graph.

    Args:
        *elements:
            Iterable[Element], the ordered elements to insert into the graph
        link_map:
            dict[str | SinkPad, str | SourcePad] | None,
            a mapping of sink pad to source pad names to link

    Returns:
        Self, the graph with the elements inserted
    """
    for element in elements:
        self._insert_element(element)
    if link_map is not None:
        self.link(link_map)
    return self

Link pads in a graph.

Parameters:

Name Type Description Default
link_map dict[str | SinkPad, str | SourcePad]

dict[str, str], a mapping of sink pad to source pad names to link. Keys are sink pad names, values are source pad names. Data flows from value -> key.

 required
Source code in src/sgn/apps.py 
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def link(self, link_map: dict[str | SinkPad, str | SourcePad]) -> Self:
    """Link pads in a graph.

    Args:
        link_map:
            dict[str, str], a mapping of sink pad to source pad names to link.
            Keys are sink pad names, values are source pad names.
            Data flows from value -> key.
    """
    for sink_pad_name, source_pad_name in link_map.items():
        if isinstance(sink_pad_name, str):
            sink_pad = self._registry[sink_pad_name]
        else:
            sink_pad = sink_pad_name
        if isinstance(source_pad_name, str):
            source_pad = self._registry[source_pad_name]
        else:
            source_pad = source_pad_name

        assert isinstance(sink_pad, SinkPad), f"not a sink pad: {sink_pad}"
        assert isinstance(source_pad, SourcePad), f"not a source pad: {source_pad}"

        graph = sink_pad.link(source_pad)
        self.graph.update(graph)

    return self

Pipeline

Bases: Graph


              flowchart TD
              sgn.apps.Pipeline[Pipeline]
              sgn.apps.Graph[Graph]

                              sgn.apps.Graph --> sgn.apps.Pipeline
                


              click sgn.apps.Pipeline href "" "sgn.apps.Pipeline"
              click sgn.apps.Graph href "" "sgn.apps.Graph"

A Pipeline is essentially a directed acyclic graph of tasks that process frames.

These tasks are grouped using Pads and Elements. The Pipeline class is responsible for registering methods to produce source, transform and sink elements and to assemble those elements in a directed acyclic graph. It also establishes an event loop to execute the graph asynchronously.

Source code in src/sgn/apps.py 
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class Pipeline(Graph):
    """A Pipeline is essentially a directed acyclic graph of tasks that process frames.

    These tasks are grouped using Pads and Elements. The Pipeline class is responsible
    for registering methods to produce source, transform and sink elements and to
    assemble those elements in a directed acyclic graph. It also establishes an event
    loop to execute the graph asynchronously.
    """

    def __init__(self) -> None:
        """Class to establish and execute a graph of elements that will process frames.

        Registers methods to produce source, transform and sink elements and to assemble
        those elements in a directed acyclic graph. Also establishes an event loop.
        """
        super().__init__()
        self.loop = asyncio.get_event_loop()
        self.__loop_counter = 0
        self.sinks: dict[str, SinkElement] = {}

    def _insert_element(self, element: Element) -> None:
        """Insert element and track sink elements."""
        super()._insert_element(element)
        if isinstance(element, SinkElement):
            self.sinks[element.name] = element

    def nodes(self, pads: bool = True, intra: bool = False) -> tuple[str, ...]:
        """Get the nodes in the pipeline.

        Args:
            pads:
                bool, whether to include pads in the graph. If True, the graph will only
                consist of pads. If False, the graph will consist only of elements.
            intra:
                bool, default False, whether or not to include intra-element edges,
                e.g. from an element's sink pads to its source pads. In this case,
                whether to include Internal Pads in the graph.

        Returns:
            list[str], the nodes in the pipeline
        """
        if pads:
            pad_types = [SinkPad, SourcePad]
            if intra:
                pad_types.append(InternalPad)

            return tuple(
                sorted(
                    [
                        pad.name
                        for pad in self._registry.values()
                        if isinstance(pad, tuple(pad_types))
                    ]
                )
            )
        element_types = [TransformElement, SinkElement, SourceElement]
        return tuple(
            sorted(
                [
                    element.name
                    for element in self._registry.values()
                    if isinstance(element, tuple(element_types))
                ]
            )
        )

    def edges(
        self, pads: bool = True, intra: bool = False
    ) -> tuple[tuple[str, str], ...]:
        """Get the edges in the pipeline.

        Args:
            pads:
                bool, whether to include pads in the graph. If True, the graph will only
                consist of pads. If False, the graph will consist only of elements.
            intra:
                bool, default False, whether or not to include intra-element edges, e.g.
                from an element's sink pads to its source pads

        Returns:
        """
        edges = set()
        for target, sources in self.graph.items():
            for source in sources:
                if not intra and isinstance(source, (SinkPad, InternalPad)):
                    continue

                if pads:
                    edges.add((source.name, target.name))
                else:
                    source_element = source.element
                    target_element = target.element
                    edges.add((source_element.name, target_element.name))
        return tuple(sorted(edges))

    def to_graph(self, label: str | None = None):
        """graphviz.DiGraph representation of pipeline

        Args:
            label:
                str, label for the graph

        Returns:
            graphviz.Digraph: the graph object
        """
        return visualize(self, label=label)

    def to_dot(self, label: str | None = None) -> str:
        """Convert the pipeline to a graph using graphviz.

        Args:
            label:
                str, label for the graph

        Returns:
            str, the graph representation of the pipeline
        """
        return visualize(self, label=label).source

    def visualize(self, path: str, label: str | None = None) -> None:
        """Convert the pipeline to a graph using graphviz, then render into a visual
        file.

        Args:
            path:
                str, the relative or full path to the file to write the graph to
            label:
                str, label for the graph
        """
        visualize(self, label=label, path=Path(path))

    @async_sgn_mem_profile(logger)
    async def __execute_graph_loop(self) -> None:
        async def _partial(node):
            try:
                return await node()
            except Exception as e:
                exc_type, exc_value, exc_traceback = sys.exc_info()
                msg = f"(from pad '{node.name}'): {exc_value}."
                raise exc_type(msg) from e

        self.__loop_counter += 1
        logger.info("Executing graph loop %s:", self.__loop_counter)
        ts = graphlib.TopologicalSorter(self.graph)
        ts.prepare()
        while ts.is_active():
            # concurrently execute the next batch of ready nodes
            nodes = ts.get_ready()
            tasks = [
                self.loop.create_task(_partial(node)) for node in nodes  # type: ignore # noqa: E501
            ]
            await asyncio.gather(*tasks)
            ts.done(*nodes)

    async def _execute_graphs(self) -> None:
        """Async graph execution function."""

        while not all(sink.at_eos for sink in self.sinks.values()):
            await self.__execute_graph_loop()

    def check(self) -> None:
        """Check that pipeline elements are connected.

        Throws an RuntimeError exception if unconnected pads are
        encountered.

        """
        if not self.sinks:
            msg = "Pipeline contains no sink elements."
            raise RuntimeError(msg)
        for element in self.elements:
            for source_pad in element.source_pads:
                if not source_pad.is_linked:
                    msg = f"Source pad not linked: {source_pad}"
                    raise RuntimeError(msg)
            for sink_pad in element.sink_pads:
                if not sink_pad.is_linked:
                    msg = f"Sink pad not linked: {sink_pad}"
                    raise RuntimeError(msg)

    def run(self, auto_parallelize: bool = True) -> None:
        """Run the pipeline until End Of Stream (EOS)

        Args:
            auto_parallelize:
                If True (default), automatically detects if parallelization is
                needed and handles it transparently. If False, runs the pipeline
                normally without parallelization detection.
        """
        configure_sgn_logging()
        if auto_parallelize:
            # Import here to avoid circular imports
            from sgn.subprocess import Parallelize

            # Use automatic parallelization detection
            if Parallelize.needs_parallelization(self):
                with Parallelize(self) as parallelize:
                    parallelize.run()
                return

        # Run normally without parallelization
        self.check()
        __start = time.time()
        if not self.loop.is_running():
            self.loop.run_until_complete(self._execute_graphs())
        else:
            """If the event loop is running, e.g., running in a Jupyter
            Notebook, run the pipeline in a forked thread.
            """
            import threading

            def _run_in_fork(pipeline):
                pipeline.loop = asyncio.new_event_loop()
                asyncio.set_event_loop(pipeline.loop)
                pipeline.loop.run_until_complete(pipeline._execute_graphs())
                pipeline.loop.close()

            thread = threading.Thread(target=_run_in_fork, args=(self,))
            thread.start()
            thread.join()
        logger.info("Pipeline().run() executed in %s seconds", (time.time() - __start))

__init__()

Class to establish and execute a graph of elements that will process frames.

Registers methods to produce source, transform and sink elements and to assemble those elements in a directed acyclic graph. Also establishes an event loop.

Source code in src/sgn/apps.py 
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def __init__(self) -> None:
    """Class to establish and execute a graph of elements that will process frames.

    Registers methods to produce source, transform and sink elements and to assemble
    those elements in a directed acyclic graph. Also establishes an event loop.
    """
    super().__init__()
    self.loop = asyncio.get_event_loop()
    self.__loop_counter = 0
    self.sinks: dict[str, SinkElement] = {}

check()

Check that pipeline elements are connected.

Throws an RuntimeError exception if unconnected pads are encountered.

Source code in src/sgn/apps.py 
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def check(self) -> None:
    """Check that pipeline elements are connected.

    Throws an RuntimeError exception if unconnected pads are
    encountered.

    """
    if not self.sinks:
        msg = "Pipeline contains no sink elements."
        raise RuntimeError(msg)
    for element in self.elements:
        for source_pad in element.source_pads:
            if not source_pad.is_linked:
                msg = f"Source pad not linked: {source_pad}"
                raise RuntimeError(msg)
        for sink_pad in element.sink_pads:
            if not sink_pad.is_linked:
                msg = f"Sink pad not linked: {sink_pad}"
                raise RuntimeError(msg)

edges(pads=True, intra=False)

Get the edges in the pipeline.

Parameters:

Name Type Description Default
pads bool

bool, whether to include pads in the graph. If True, the graph will only consist of pads. If False, the graph will consist only of elements.

 True
intra bool

bool, default False, whether or not to include intra-element edges, e.g. from an element's sink pads to its source pads

 False

Returns:

Source code in src/sgn/apps.py 
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def edges(
    self, pads: bool = True, intra: bool = False
) -> tuple[tuple[str, str], ...]:
    """Get the edges in the pipeline.

    Args:
        pads:
            bool, whether to include pads in the graph. If True, the graph will only
            consist of pads. If False, the graph will consist only of elements.
        intra:
            bool, default False, whether or not to include intra-element edges, e.g.
            from an element's sink pads to its source pads

    Returns:
    """
    edges = set()
    for target, sources in self.graph.items():
        for source in sources:
            if not intra and isinstance(source, (SinkPad, InternalPad)):
                continue

            if pads:
                edges.add((source.name, target.name))
            else:
                source_element = source.element
                target_element = target.element
                edges.add((source_element.name, target_element.name))
    return tuple(sorted(edges))

nodes(pads=True, intra=False)

Get the nodes in the pipeline.

Parameters:

Name Type Description Default
pads bool

bool, whether to include pads in the graph. If True, the graph will only consist of pads. If False, the graph will consist only of elements.

 True
intra bool

bool, default False, whether or not to include intra-element edges, e.g. from an element's sink pads to its source pads. In this case, whether to include Internal Pads in the graph.

 False

Returns:

Type Description
tuple[str, ...]

list[str], the nodes in the pipeline
Source code in src/sgn/apps.py 
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def nodes(self, pads: bool = True, intra: bool = False) -> tuple[str, ...]:
    """Get the nodes in the pipeline.

    Args:
        pads:
            bool, whether to include pads in the graph. If True, the graph will only
            consist of pads. If False, the graph will consist only of elements.
        intra:
            bool, default False, whether or not to include intra-element edges,
            e.g. from an element's sink pads to its source pads. In this case,
            whether to include Internal Pads in the graph.

    Returns:
        list[str], the nodes in the pipeline
    """
    if pads:
        pad_types = [SinkPad, SourcePad]
        if intra:
            pad_types.append(InternalPad)

        return tuple(
            sorted(
                [
                    pad.name
                    for pad in self._registry.values()
                    if isinstance(pad, tuple(pad_types))
                ]
            )
        )
    element_types = [TransformElement, SinkElement, SourceElement]
    return tuple(
        sorted(
            [
                element.name
                for element in self._registry.values()
                if isinstance(element, tuple(element_types))
            ]
        )
    )

run(auto_parallelize=True)

Run the pipeline until End Of Stream (EOS)

Parameters:

Name Type Description Default
auto_parallelize bool

If True (default), automatically detects if parallelization is needed and handles it transparently. If False, runs the pipeline normally without parallelization detection.

 True
Source code in src/sgn/apps.py 
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def run(self, auto_parallelize: bool = True) -> None:
    """Run the pipeline until End Of Stream (EOS)

    Args:
        auto_parallelize:
            If True (default), automatically detects if parallelization is
            needed and handles it transparently. If False, runs the pipeline
            normally without parallelization detection.
    """
    configure_sgn_logging()
    if auto_parallelize:
        # Import here to avoid circular imports
        from sgn.subprocess import Parallelize

        # Use automatic parallelization detection
        if Parallelize.needs_parallelization(self):
            with Parallelize(self) as parallelize:
                parallelize.run()
            return

    # Run normally without parallelization
    self.check()
    __start = time.time()
    if not self.loop.is_running():
        self.loop.run_until_complete(self._execute_graphs())
    else:
        """If the event loop is running, e.g., running in a Jupyter
        Notebook, run the pipeline in a forked thread.
        """
        import threading

        def _run_in_fork(pipeline):
            pipeline.loop = asyncio.new_event_loop()
            asyncio.set_event_loop(pipeline.loop)
            pipeline.loop.run_until_complete(pipeline._execute_graphs())
            pipeline.loop.close()

        thread = threading.Thread(target=_run_in_fork, args=(self,))
        thread.start()
        thread.join()
    logger.info("Pipeline().run() executed in %s seconds", (time.time() - __start))

to_dot(label=None)

Convert the pipeline to a graph using graphviz.

Parameters:

Name Type Description Default
label str | None

str, label for the graph

 None

Returns:

Type Description
str

str, the graph representation of the pipeline
Source code in src/sgn/apps.py 
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def to_dot(self, label: str | None = None) -> str:
    """Convert the pipeline to a graph using graphviz.

    Args:
        label:
            str, label for the graph

    Returns:
        str, the graph representation of the pipeline
    """
    return visualize(self, label=label).source

to_graph(label=None)

graphviz.DiGraph representation of pipeline

Parameters:

Name Type Description Default
label str | None

str, label for the graph

 None

Returns:

Type Description

graphviz.Digraph: the graph object
Source code in src/sgn/apps.py 
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def to_graph(self, label: str | None = None):
    """graphviz.DiGraph representation of pipeline

    Args:
        label:
            str, label for the graph

    Returns:
        graphviz.Digraph: the graph object
    """
    return visualize(self, label=label)

visualize(path, label=None)

Convert the pipeline to a graph using graphviz, then render into a visual file.

Parameters:

Name Type Description Default
path str

str, the relative or full path to the file to write the graph to

 required
label str | None

str, label for the graph

 None
Source code in src/sgn/apps.py 
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def visualize(self, path: str, label: str | None = None) -> None:
    """Convert the pipeline to a graph using graphviz, then render into a visual
    file.

    Args:
        path:
            str, the relative or full path to the file to write the graph to
        label:
            str, label for the graph
    """
    visualize(self, label=label, path=Path(path))