perun.monitoring.subprocess
===========================

.. py:module:: perun.monitoring.subprocess

.. autoapi-nested-parse::

   Perun subprocess module.



Attributes
----------

.. autoapisummary::

   perun.monitoring.subprocess.log


Functions
---------

.. autoapisummary::

   perun.monitoring.subprocess.prepSensors
   perun.monitoring.subprocess.createNode
   perun.monitoring.subprocess.perunSubprocess


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

.. py:data:: log

.. py:function:: prepSensors(backends: dict[str, perun.backend.Backend], l_assigned_sensors: dict[str, tuple]) -> tuple[perun.data_model.measurement_type.MetricMetaData, list[perun.data_model.sensor.Sensor]]

   Prepare sensors for monitoring.

   :param backends: A dictionary of backends.
   :type backends: dict[str, Backend]
   :param l_assigned_sensors: A dictionary of sensor configurations.
   :type l_assigned_sensors: dict[str, tuple]

   :returns: A tuple containing the following:
             - timesteps (list[int]): A list of timesteps.
             - t_metadata (MetricMetaData): Metadata for the metrics.
             - rawValues (list[list[Number]]): A list of raw sensor values.
             - lSensors (list[Sensor]): A list of sensors.
   :rtype: tuple[list[int], MetricMetaData, list[list[Number]], list[Sensor]]


.. py:function:: createNode(timesteps: list[int], t_metadata: perun.data_model.measurement_type.MetricMetaData, rawValues: list[list[perun.data_model.measurement_type.Number]], lSensors: list[perun.data_model.sensor.Sensor], perunConfig: configparser.ConfigParser) -> perun.data_model.data.DataNode

   Create a data node from the sensor data.

   :param timesteps: List of timesteps.
   :type timesteps: list[int]
   :param t_metadata: Metadata for the metrics.
   :type t_metadata: MetricMetaData
   :param rawValues: List of raw sensor values.
   :type rawValues: list[list[Number]]
   :param lSensors: A list of sensors.
   :type lSensors: list[Sensor]
   :param perunConfig: The perun configuration.
   :type perunConfig: ConfigParser

   :returns: A data node.
   :rtype: DataNode


.. py:function:: perunSubprocess(queue: multiprocessing.Queue, rank: int, l_assigned_sensors: dict[str, tuple], perunConfig: configparser.ConfigParser, sp_ready_event: multiprocessing.synchronize.Event, start_event: multiprocessing.synchronize.Event, stop_event: multiprocessing.synchronize.Event, close_event: multiprocessing.synchronize.Event, sampling_period: float, live_callback_inits: dict[str, Callable[[], Callable[[dict[str, perun.data_model.measurement_type.Number]], None]]]) -> None

   Parallel function that samples energy values from hardware libraries.

   :param queue: Multiprocessing Queue object where the results are sent after finish
   :type queue: Queue
   :param rank: Local MPI Rank
   :type rank: int
   :param l_assigned_sensors: Local MPI rank sensor configuration
   :type l_assigned_sensors: Dict[str, Tuple]
   :param perunConfig: Global perun configuration
   :type perunConfig: ConfigParser
   :param sp_ready_event: Indicates monitoring supbrocess is ready, multiprocessing module
   :type sp_ready_event: EventClass
   :param start_event: Indicates start of the monitored application
   :type start_event: EventClass
   :param stop_event: Indicates the stop of a monitored application
   :type stop_event: EventClass
   :param close_event: Indicates that perun is closing, and the subprocess needs to close
   :type close_event: EventClass
   :param sampling_period: Sampling period in seconds
   :type sampling_period: float
   :param live_callback_inits: Dictionary of live callback initializers, where the key is the name of the callback and the value is a function that returns a callable that accepts metric identifier and value.
   :type live_callback_inits: dict[str, Callable[[], Callable[[str, Number], None]]]