task

DagTaskBreakdown

Bases: Enum

Enum to define how to break down a graph into tasks for the pipeline.

Source code in lineapy/plugins/task.py

class DagTaskBreakdown(Enum):
    """
    Enum to define how to break down a graph into tasks for the pipeline.
    """

    TaskAllSessions = 1  # all sessions are one task
    TaskPerSession = 2  # each session corresponds to one task
    TaskPerArtifact = 3  # each artifact or common variable is a task

TaskDefinition dataclass

Definition of an artifact, can extend new keys(user, project, ...) in the future.

Parameters:

Name	Type	Description	Default
function_name	str	suggested function name this task that wont conflict with other linea generated tasks	required
user_input_variables	List[str]	arguments that must be provided through the framework	required
loaded_input_variables	List[str]	arguments that are provided by other tasks and must be loaded through inter task communication	required
typing_blocks	List[str]	for user_input_variables, casts the input variables to the correct type	required
call_block	str	line of code to call the function in module file	required
return_vars	List[str]	outputs that need to be serialized to be used	required
pipeline_name	str	overall pipeline name	required

Source code in lineapy/plugins/task.py

@dataclass
class TaskDefinition:
    """
    Definition of an artifact, can extend new keys(user, project, ...)
    in the future.

    Parameters
    ----------
    function_name: str
        suggested function name this task that wont conflict with other linea generated tasks
    user_input_variables: List[str]
        arguments that must be provided through the framework
    loaded_input_variables: List[str]
        arguments that are provided by other tasks and must be loaded through inter task communication
    typing_blocks: List[str]
        for user_input_variables, casts the input variables to the correct type
    call_block: str
        line of code to call the function in module file
    return_vars: List[str]
        outputs that need to be serialized to be used
    pipeline_name: str
        overall pipeline name

    """

    function_name: str
    user_input_variables: List[str]
    loaded_input_variables: List[str]
    typing_blocks: List[str]
    pre_call_block: str
    call_block: str
    post_call_block: str
    return_vars: List[str]
    pipeline_name: str

TaskGraph

Bases: object

Graph represents for task dependency It is constructed based on the "edges" variable

Parameters:

Name	Type	Description	Default
edges	TaskGraphEdge	Dictionary with task name as key and set of prerequisite tasks as value. This is the standard library graphlib style graph definition. For instance, {'C':{'A','B'}} means A and B are prerequisites for C. Both examples give us following task dependency:: A ---\ \ ---> C / B ---/	{}

Note

• If we only support Python 3.9+, we prefer to use graphlib in standard library instead of networkx for graph operation.
• We might want to get rid of the mapping for renaming slice_names to task_names.

Source code in lineapy/plugins/task.py

class TaskGraph(object):
    """
    Graph represents for task dependency
    It is constructed based on the "edges" variable

    Parameters
    ----------
    edges: TaskGraphEdge
        Dictionary with task name as key and set of prerequisite 
        tasks as value. This is the standard library `graphlib` style graph 
        definition. For instance, {'C':{'A','B'}} means A and B are 
        prerequisites for C. Both examples give us following task dependency::

            A ---\\
                  \\
                   >---> C
                  /
            B ---/


    ??? note

        - If we only support Python 3.9+, we prefer to use graphlib in standard 
          library instead of networkx for graph operation.
        - We might want to get rid of the mapping for renaming slice_names to 
          task_names.

    """

    def __init__(
        self,
        nodes: List[str],
        edges: TaskGraphEdge = {},
    ):
        self.graph = nx.DiGraph()
        self.graph.add_nodes_from(nodes)
        # parsing the other format to our tuple-based format
        # note that nesting is not allowed (enforced by the type signature)
        # per the spec in https://docs.python.org/3/library/graphlib.html
        graph_edges = list(
            chain.from_iterable(
                ((node, to_node) for node in from_node)
                for to_node, from_node in edges.items()
            )
        )
        self.graph.add_edges_from(graph_edges)

    def copy(
        self,
    ) -> TaskGraph:
        copied_taskgraph = TaskGraph([])
        copied_taskgraph.graph = self.graph.copy()
        return copied_taskgraph

    def remap_nodes(self, mapping: Dict[str, str]) -> TaskGraph:
        remapped_taskgraph = TaskGraph([])
        remapped_taskgraph.graph = nx.relabel_nodes(
            self.graph, mapping, copy=True
        )
        return remapped_taskgraph

    def insert_setup_task(self, setup_task_name: str):
        """
        insert_setup_task adds a setup task that will be run before all the original source tasks
        """

        self.graph.add_node(setup_task_name)

        for old_source in self.source_nodes:
            if not old_source == setup_task_name:
                self.graph.add_edge(setup_task_name, old_source)

    def insert_teardown_task(self, cleanup_task_name: str):
        """
        insert_cleanup_task adds a cleanup task that will be run after all the original sink tasks
        """

        self.graph.add_node(cleanup_task_name)

        for old_sink in self.sink_nodes:
            if not old_sink == cleanup_task_name:
                self.graph.add_edge(old_sink, cleanup_task_name)

    def get_taskorder(self) -> List[str]:
        try:
            return list(nx.topological_sort(self.graph))
        except NetworkXUnfeasible:
            raise Exception(
                "Current implementation of LineaPy demands it be able to linearly order different sessions, "
                "which prohibits any circular dependencies between sessions. "
                "Please check if your provided dependencies include such circular dependencies between sessions, "
                "e.g., Artifact A (Session 1) -> Artifact B (Session 2) -> Artifact C (Session 1)."
            )

    def remove_self_loops(self):
        self.graph.remove_edges_from(nx.selfloop_edges(self.graph))

    @property
    def sink_nodes(self):
        return [
            node
            for node in self.graph.nodes
            if self.graph.out_degree(node) == 0
        ]

    @property
    def source_nodes(self):
        return [
            node
            for node in self.graph.nodes
            if self.graph.in_degree(node) == 0
        ]

insert_setup_task(setup_task_name)

insert_setup_task adds a setup task that will be run before all the original source tasks

Source code in lineapy/plugins/task.py

def insert_setup_task(self, setup_task_name: str):
    """
    insert_setup_task adds a setup task that will be run before all the original source tasks
    """

    self.graph.add_node(setup_task_name)

    for old_source in self.source_nodes:
        if not old_source == setup_task_name:
            self.graph.add_edge(setup_task_name, old_source)

insert_teardown_task(cleanup_task_name)

insert_cleanup_task adds a cleanup task that will be run after all the original sink tasks

Source code in lineapy/plugins/task.py

def insert_teardown_task(self, cleanup_task_name: str):
    """
    insert_cleanup_task adds a cleanup task that will be run after all the original sink tasks
    """

    self.graph.add_node(cleanup_task_name)

    for old_sink in self.sink_nodes:
        if not old_sink == cleanup_task_name:
            self.graph.add_edge(old_sink, cleanup_task_name)

TaskSerializer

Bases: Enum

Enum to define what type of object serialization to use for inter task communication.

Source code in lineapy/plugins/task.py

class TaskSerializer(Enum):
    """Enum to define what type of object serialization to use for inter task communication."""

    # Write to local pickle directory under /tmp/
    LocalPickle = 1
    # Write to a pickle directory that can be parametrized
    ParametrizedPickle = 2

render_task_io_serialize_blocks(taskdef, task_serialization)

render_task_io_serialize_blocks renders object ser and deser code blocks.

These code blocks can be used for inter task communication. This function returns the task deserialization block first, since this block should be included first in the function to load the variables.

Source code in lineapy/plugins/task.py

def render_task_io_serialize_blocks(
    taskdef: TaskDefinition, task_serialization: TaskSerializer
) -> Tuple[List[str], List[str]]:
    """
    render_task_io_serialize_blocks renders object ser and deser code blocks.

    These code blocks can be used for inter task communication.
    This function returns the task deserialization block first,
    since this block should be included first in the function to load the variables.
    """
    task_serialization_blocks = []
    task_deserialization_block = []

    if task_serialization == TaskSerializer.LocalPickle:
        SERIALIZER_TEMPLATE = load_plugin_template(
            "task/localpickle/task_local_pickle_ser.jinja"
        )
        DESERIALIZER_TEMPLATE = load_plugin_template(
            "task/localpickle/task_local_pickle_deser.jinja"
        )
    elif task_serialization == TaskSerializer.ParametrizedPickle:
        SERIALIZER_TEMPLATE = load_plugin_template(
            "task/parameterizedpickle/task_parameterized_ser.jinja"
        )
        DESERIALIZER_TEMPLATE = load_plugin_template(
            "task/parameterizedpickle/task_parameterized_deser.jinja"
        )
    # Add more renderable task serializers here

    for loaded_input_variable in taskdef.loaded_input_variables:
        task_deserialization_block.append(
            DESERIALIZER_TEMPLATE.render(
                loaded_input_variable=loaded_input_variable,
                pipeline_name=taskdef.pipeline_name,
            )
        )
    for return_variable in taskdef.return_vars:
        task_serialization_blocks.append(
            SERIALIZER_TEMPLATE.render(
                return_variable=return_variable,
                pipeline_name=taskdef.pipeline_name,
            )
        )

    return task_deserialization_block, task_serialization_blocks

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