ksuit.trackers.base_tracker
===========================

.. py:module:: ksuit.trackers.base_tracker


Classes
-------

.. autoapisummary::

   ksuit.trackers.base_tracker.BaseTracker


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

.. py:class:: BaseTracker(metric_property_provider, path_provider)

   Base class for all experiment trackers.

   Initialize the BaseTracker.

   :param metric_property_provider: The metric property provider gives additional information such
                                    as whether higher values are better.
   :param path_provider: Gives access to paths (e.g., output_path: where checkpoints/logs are stored).


   .. py:attribute:: logger


   .. py:attribute:: metric_property_provider


   .. py:attribute:: path_provider


   .. py:attribute:: summary
      :type:  dict[str, Any]


   .. py:method:: init(accelerator, run_name, stage_name, stage_hp, run_id, output_uri)

      Initialize the tracker for a specific run.

      :param accelerator: The accelerator used for training (e.g., "cpu", "cuda").
      :param run_name: The name of the run.
      :param stage_name: The stage of the run.
      :param stage_hp: The hyperparameters for the run stage.
      :param run_id: The ID of the run.
      :param output_uri: The URI where the output will be stored.



   .. py:method:: log(data)

      Log data to the tracker.

      :param data: The data to log. This should be a dictionary where the values are the data to log.



   .. py:method:: close()

      Close the file used by the tracker and save the summary.



   .. py:method:: set_summary(key, value)

      Set a summary value.
      :param key: The key for the summary value.
      :param value: The value to set.



   .. py:method:: update_summary(data)

      Update the summary with new data.

      :param data: The data to update the summary with. This should be a dictionary where the values are the data to log.



   .. py:method:: summarize_logvalues()

      Summarize the log values from the entries.
      This method is called after the training is finished and summarizes the log values.
      It computes the min/max values for each log value and stores them in the summary.