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

.. py:module:: dicee.executer


Classes
-------

.. autoapisummary::

   dicee.executer.Execute
   dicee.executer.ContinuousExecute


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

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

   A class for Training, Retraining and Evaluation a model.

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


   .. py:attribute:: args


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



   .. py:attribute:: trainer
      :value: None



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



   .. py:attribute:: knowledge_graph
      :value: None



   .. py:attribute:: report


   .. py:attribute:: evaluator
      :value: None



   .. py:attribute:: start_time
      :value: None



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


   .. 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