perun.api.decorator
===================

.. py:module:: perun.api.decorator

.. autoapi-nested-parse::

   Decorator module.



Attributes
----------

.. autoapisummary::

   perun.api.decorator.log


Functions
---------

.. autoapisummary::

   perun.api.decorator.monitor
   perun.api.decorator.perun
   perun.api.decorator.register_callback
   perun.api.decorator.register_live_callback


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

.. py:data:: log

.. py:function:: monitor(region_name: str | None = None) -> Callable

   Decorate function to monitor its energy usage.


.. py:function:: perun(configuration_file: str = './.perun.ini', **conf_kwargs: Any) -> Callable

   Decorate function to monitor its energy usage.


.. py:function:: register_callback(func: Callable[[perun.data_model.data.DataNode], None]) -> None

   Register a function to run after perun has finished collection data.

   :param func: Function to be called.
   :type func: Callable[[DataNode], None]


.. py:function:: register_live_callback(obj: Callable[[], Callable[[dict[str, perun.processing.Number]], None]], id: str) -> None

   Register a function that initializes a live callback function that will be run after each datapoint is collected on the monitoring subprocess.

   This is useful for live monitoring of metrics in real-time.

   The function passed should return a callable that accepts the metric identifier and the metric value.

   This structure is for systems that need to establish a connection to an external server, like MLFlow and Weights and Biases, as such objects are not serializable and can sometimes cause issues with the multiprocessing module.

   :param obj: Function that initializes the live callback.
               It should return a callable that accepts the metric identifier and the metric value. It should take no arguments.
   :type obj: Callable[[], Callable[[str, Union[int, float]], None]]
   :param id: Identifier for the live callback, used to register it in the Perun instance.
   :type id: str