dicee.executer
==============

.. py:module:: dicee.executer

.. autoapi-nested-parse::

   Executor module for training, retraining and evaluating KGE models.

   This module provides the Execute and ContinuousExecute classes for managing
   the full lifecycle of knowledge graph embedding model training.



Classes
-------

.. autoapisummary::

   dicee.executer.Execute
   dicee.executer.ContinuousExecute


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

.. py:class:: Execute(args, continuous_training: bool = False)

   Executor class for training, retraining and evaluating KGE models.

   Handles the complete workflow:
   1. Loading & Preprocessing & Serializing input data
   2. Training & Validation & Testing
   3. Storing all necessary information

   .. attribute:: args

      Processed input arguments.

   .. attribute:: distributed

      Whether distributed training is enabled.

   .. attribute:: rank

      Process rank in distributed training.

   .. attribute:: world_size

      Total number of processes.

   .. attribute:: local_rank

      Local GPU rank.

   .. attribute:: trainer

      Training handler instance.

   .. attribute:: trained_model

      The trained model after training completes.

   .. attribute:: knowledge_graph

      The loaded knowledge graph.

   .. attribute:: report

      Dictionary storing training metrics and results.

   .. attribute:: evaluator

      Model evaluation handler.


   .. py:attribute:: distributed


   .. py:attribute:: args


   .. py:attribute:: is_continual_training
      :value: False



   .. py:attribute:: trainer
      :type:  Optional[dicee.trainer.DICE_Trainer]
      :value: None



   .. py:attribute:: trained_model
      :value: None



   .. py:attribute:: knowledge_graph
      :type:  Optional[dicee.knowledge_graph.KG]
      :value: None



   .. py:attribute:: report
      :type:  Dict


   .. py:attribute:: evaluator
      :type:  Optional[dicee.evaluator.Evaluator]
      :value: None



   .. py:attribute:: start_time
      :type:  Optional[float]
      :value: None



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


   .. py:method:: cleanup()


   .. py:method:: setup_executor() -> None

      Set up storage directories for the experiment.

      Creates or reuses experiment directories based on configuration.
      Saves the configuration to a JSON file.



   .. py:method:: create_and_store_kg() -> None

      Create knowledge graph and store as memory-mapped file.

      Only executed on rank 0 in distributed training.
      Skips if memmap already exists.



   .. py:method:: load_from_memmap() -> None

      Load knowledge graph from memory-mapped file.



   .. py:method:: save_trained_model() -> None

      Save a knowledge graph embedding model

      (1) Send model to eval mode and cpu.
      (2) Store the memory footprint of the model.
      (3) Save the model into disk.
      (4) Update the stats of KG again ?

      Parameter
      ----------

      :rtype: None



   .. py:method:: end(form_of_labelling: str) -> dict

      End training

      (1) Store trained model.
      (2) Report runtimes.
      (3) Eval model if required.

      Parameter
      ---------

      :rtype: A dict containing information about the training and/or evaluation



   .. py:method:: write_report() -> None

      Report training related information in a report.json file



   .. py:method:: start() -> dict

      Start training

      # (1) Loading the Data
      # (2) Create an evaluator object.
      # (3) Create a trainer object.
      # (4) Start the training

      Parameter
      ---------

      :rtype: A dict containing information about the training and/or evaluation



.. py:class:: ContinuousExecute(args)

   Bases: :py:obj:`Execute`


   A subclass of Execute Class for retraining

   (1) Loading & Preprocessing & Serializing input data.
   (2) Training & Validation & Testing
   (3) Storing all necessary info

   During the continual learning we can only modify *** num_epochs *** parameter.
   Trained model stored in the same folder as the seed model for the training.
   Trained model is noted with the current time.


   .. py:method:: continual_start() -> dict

      Start Continual Training

      (1) Initialize training.
      (2) Start continual training.
      (3) Save trained model.

      Parameter
      ---------

      :rtype: A dict containing information about the training and/or evaluation