:class:`SchedulerManager`
=========================


.. py:class:: pyedb.workflows.job_manager.backend.service.SchedulerManager(scheduler_type: pyedb.workflows.job_manager.backend.job_submission.SchedulerType)

   
   Thin async wrapper around cluster scheduler commands.

   Provides live introspection of SLURM and LSF clusters including:

   * List of partitions / queues with resource information
   * Per-partition: total & free cores, total & free memory
   * Global job table (running, pending, etc.)

   All methods are **coroutines** so they can be awaited from the REST layer
   without blocking the event-loop.

   :Parameters:

       **scheduler_type** : :obj:`SchedulerType`
           Type of scheduler (SLURM or LSF only)





   :Raises:

       :obj:`ValueError`
           If scheduler_type is not SLURM or LSF









   ..
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.. py:currentmodule:: SchedulerManager

Overview
--------

.. tab-set::



   .. tab-item:: Methods

      .. list-table::
          :header-rows: 0
          :widths: auto

          * - :py:attr:`~get_partitions`
            - Get list of scheduler partitions/queues with resource information.
          * - :py:attr:`~get_jobs`
            - Get global job table (all users).



   .. tab-item:: Attributes

      .. list-table::
          :header-rows: 0
          :widths: auto

          * - :py:attr:`~scheduler_type`
            - 






Import detail
-------------

.. code-block:: python

    from pyedb.workflows.job_manager.backend.service import SchedulerManager


Attribute detail
----------------

.. py:attribute:: scheduler_type



Method detail
-------------

.. py:method:: get_partitions() -> List[Dict[str, Any]]
   :async:


   
   Get list of scheduler partitions/queues with resource information.



   :Returns:

       :obj:`List`\[:obj:`Dict`\[:class:`python:str`, :obj:`Any`]]
           List of partition dictionaries with keys:
           - name: Partition/queue name
           - cores_total: Total available cores
           - cores_used: Currently used cores
           - memory_total_gb: Total memory in GB
           - memory_used_gb: Currently used memory in GB




   :Raises:

       :obj:`RuntimeError`
           If scheduler command execution fails









   ..
       !! processed by numpydoc !!

.. py:method:: get_jobs() -> List[Dict[str, Any]]
   :async:


   
   Get global job table (all users).



   :Returns:

       :obj:`List`\[:obj:`Dict`\[:class:`python:str`, :obj:`Any`]]
           List of job dictionaries with keys:
           - job_id: Scheduler job ID
           - partition: Partition/queue name
           - user: Job owner username
           - state: Job state (RUNNING, PENDING, etc.)
           - nodes: Number of nodes allocated
           - cpus: Number of CPUs allocated
           - memory_gb: Memory allocated in GB




   :Raises:

       :obj:`RuntimeError`
           If scheduler command execution fails









   ..
       !! processed by numpydoc !!