client.swarm.services.scheduler
===============================

.. py:module:: client.swarm.services.scheduler

.. autoapi-nested-parse::

   Scheduler: Task batching and queueing for workflow runs.

   This service manages the scheduling of tasks, including batching tasks
   with compatible resources and queueing them to the server.



Attributes
----------

.. autoapisummary::

   client.swarm.services.scheduler.logger


Classes
-------

.. autoapisummary::

   client.swarm.services.scheduler.BatchResult
   client.swarm.services.scheduler.Scheduler


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

.. py:data:: logger

.. py:class:: BatchResult

   Result of queueing a batch of tasks.


   .. py:attribute:: task_statuses
      :type:  dict[int, str]


   .. py:attribute:: batch_size
      :type:  int
      :value: 0



   .. py:attribute:: array_id
      :type:  int
      :value: 0



.. py:class:: Scheduler(gateway: jobmon.client.swarm.gateway.ServerGateway, state: jobmon.client.swarm.state.SwarmState)

   Generates and executes task queue commands.

   The Scheduler is responsible for:
   - Batching tasks with compatible resources
   - Respecting workflow and array concurrency limits
   - Queueing batches to the server
   - Returning state updates from queue responses

   Usage:
       scheduler = Scheduler(gateway=gateway, state=state)

       # Single scheduling tick
       update = await scheduler.tick(timeout=30.0)
       state.apply_update(update)

   Initialize the scheduler.

   :param gateway: ServerGateway for server communication.
   :param state: SwarmState containing all task/array state (single source of truth).


   .. py:attribute:: MAX_BATCH_SIZE
      :type:  int
      :value: 500



   .. py:property:: max_concurrently_running
      :type: int


      Workflow-level concurrency limit (delegated to state).


   .. py:method:: tick(timeout: float = -1) -> jobmon.client.swarm.state.StateUpdate
      :async:


      One scheduling iteration: batch tasks, queue them, return state changes.

      :param timeout: Maximum time to spend scheduling (-1 for unlimited).

      :returns: StateUpdate containing task status changes from queued batches.



   .. py:method:: has_work() -> bool

      Check if there are tasks ready to run with available capacity.