dicee.trainer.torch_trainer
===========================

.. py:module:: dicee.trainer.torch_trainer


Classes
-------

.. autoapisummary::

   dicee.trainer.torch_trainer.TorchTrainer


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

.. py:class:: TorchTrainer(args, callbacks)

   Bases: :py:obj:`dicee.abstracts.AbstractTrainer`


    TorchTrainer for using single GPU or multi CPUs on a single node

    Arguments
   ----------
   args: ?

   callbacks: list of Abstract callback instances



   .. py:attribute:: loss_function
      :value: None



   .. py:attribute:: optimizer
      :value: None



   .. py:attribute:: model
      :value: None



   .. py:attribute:: train_dataloaders
      :value: None



   .. py:attribute:: training_step
      :value: None



   .. py:attribute:: process


   .. py:method:: fit(*args, train_dataloaders, **kwargs) -> None

       Training starts

       Arguments
      ----------
      args:tuple
      (BASEKGE,)
      kwargs:Tuple
          empty dictionary
      :rtype: batch loss (float)



   .. py:method:: forward_backward_update(x_batch: torch.Tensor, y_batch: torch.Tensor) -> torch.Tensor

       Compute forward, loss, backward, and parameter update

       Arguments
      ----------
      x_batch:(torch.Tensor) mini-batch inputs
      y_batch:(torch.Tensor) mini-batch outputs

      :rtype: batch loss (float)



   .. py:method:: extract_input_outputs_set_device(batch: list) -> Tuple

       Construct inputs and outputs from a batch of inputs with outputs From a batch of inputs and put

       Arguments
      ----------
      batch: (list) mini-batch inputs on CPU

      :rtype: (tuple) mini-batch on select device