client.dag
==========

.. py:module:: client.dag

.. autoapi-nested-parse::

   The DAG captures the interconnected graph of tasks and their dependencies.



Attributes
----------

.. autoapisummary::

   client.dag.logger


Classes
-------

.. autoapisummary::

   client.dag.Dag


Module Contents
---------------

.. py:data:: logger

.. py:class:: Dag(requester: Optional[jobmon.core.requester.Requester] = None)

   Bases: :py:obj:`object`


   The DAG captures the interconnected graph of tasks and their dependencies.


   .. py:attribute:: nodes
      :type:  Set[jobmon.client.node.Node]


   .. py:attribute:: requester
      :value: None



   .. py:property:: dag_id
      :type: int


      Database unique ID of this DAG.


   .. py:method:: add_node(node: jobmon.client.node.Node) -> None

      Add a node to this dag.

      :param node: Node to add to the dag
      :type node: jobmon.client.node.Node



   .. py:method:: bind(chunk_size: int = 500) -> int

      Retrieve an id for a matching dag from the server.

      If it doesn't exist, first create one, including its edges.



   .. py:method:: validate() -> None

      Validate the nodes and their dependencies.



   .. py:method:: _is_cyclic(dag_map: Dict[jobmon.client.node.Node, Set[jobmon.client.node.Node]]) -> bool

      Return true if the nodes are cyclic.

      This method is effectively a depth-first search looking for already-seen nodes,
      implemented using the "stack of iterators" pattern to get around Python's recursion
      limit of 1000.



   .. py:method:: _bulk_bind_nodes(chunk_size: int) -> None


   .. py:method:: _get_dag_id() -> Optional[int]


   .. py:method:: _bulk_insert_edges(dag_id: int, chunk_size: int = 500) -> None


   .. py:method:: __hash__() -> int

      Determined by hashing all sorted node hashes and their downstream.



   .. py:method:: __repr__() -> str

      A representation string for a Dag instance.