dasf.ml.dl
==========

.. py:module:: dasf.ml.dl

.. autoapi-nested-parse::

   Init module for Deep Learning algorithms. 



Subpackages
-----------

.. toctree::
   :maxdepth: 1

   /autoapi/dasf/ml/dl/clusters/index
   /autoapi/dasf/ml/dl/models/index


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/dasf/ml/dl/lightning_fit/index
   /autoapi/dasf/ml/dl/pytorch_lightning/index


Classes
-------

.. autoapisummary::

   dasf.ml.dl.LightningTrainer
   dasf.ml.dl.NeuralNetClassifier


Package Contents
----------------

.. py:class:: LightningTrainer(model, use_gpu = False, batch_size = 1, max_epochs = 1, limit_train_batches = None, limit_val_batches = None, devices = 'auto', num_nodes = 1, shuffle = True, strategy = 'ddp', unsqueeze_dim = None)

   
   Initialize the LightningFit class.

   Parameters
   ----------
   model : LightningModule
       The LightningModule instance representing the model to be trained.
   use_gpu : bool, optional
       Flag indicating whether to use GPU for training, by default False.
   batch_size : int, optional
       The batch size for training, by default 1.
   max_epochs : int, optional
       The maximum number of epochs for training, by default 1.
   limit_train_batches : int, optional
       The number of batches to consider for training, by default None.
   limit_val_batches : int, optional
       The number of batches to consider for validation, by default None.
   devices : int, optional
       The number of devices to use for training, by default "auto".
   num_nodes : int, optional
       The number of nodes to use for distributed training, by default 1.
   shuffle : bool, optional
       Flag indicating whether to shuffle the data during training, by default True.
   strategy : str, optional
       The strategy to use for distributed training, by default "ddp".
   unsqueeze_dim : int, optional
       The dimension to unsqueeze the input data, by default None.


   .. py:attribute:: model


   .. py:attribute:: accelerator


   .. py:attribute:: batch_size


   .. py:attribute:: max_epochs


   .. py:attribute:: limit_train_batches


   .. py:attribute:: limit_val_batches


   .. py:attribute:: devices


   .. py:attribute:: num_nodes


   .. py:attribute:: shuffle


   .. py:attribute:: strategy


   .. py:attribute:: unsqueeze_dim


   .. py:method:: fit(train_data, val_data = None)

      Perform the training of the model using torch Lightning.

      Parameters
      ----------
      train_data : Any
          A dasf map-style like dataset containing the training data.
      val_data : Any, optional
          A dasf map-style like dataset containing the validation data.



   .. py:method:: _fit(train_data, val_data=None)


   .. py:method:: _lazy_fit_cpu(train_data, val_data=None)


   .. py:method:: _lazy_fit_gpu(train_data, val_data=None)


   .. py:method:: _fit_cpu(train_data, val_data=None)


   .. py:method:: _fit_gpu(train_data, val_data=None)


.. py:class:: NeuralNetClassifier(model, max_iter=100, batch_size=32)

   Bases: :py:obj:`dasf.transforms.base.Fit`


   Class representing a Fit operation of the pipeline.


   .. py:attribute:: _model


   .. py:attribute:: _accel
      :value: None



   .. py:attribute:: _strategy
      :value: None



   .. py:attribute:: _max_iter


   .. py:attribute:: _devices
      :value: 0



   .. py:attribute:: _ngpus
      :value: 0



   .. py:attribute:: _batch_size


   .. py:attribute:: __trainer
      :value: False



   .. py:attribute:: __handler


   .. py:method:: _lazy_fit_generic(X, y, accel, ngpus)


   .. py:method:: _lazy_fit_gpu(X, y=None)

      Respective lazy fit mocked function for GPUs.



   .. py:method:: _lazy_fit_cpu(X, y=None)

      Respective lazy fit mocked function for CPUs.



   .. py:method:: __fit_generic(X, y, accel, ngpus)


   .. py:method:: _fit_gpu(X, y=None)

      Respective immediate fit mocked function for local GPU(s).



   .. py:method:: _fit_cpu(X, y=None)

      Respective immediate fit mocked function for local CPU(s).