dasf.ml.dl

Init module for Deep Learning algorithms.

Subpackages

• dasf.ml.dl.clusters
• dasf.ml.dl.models

Submodules

• dasf.ml.dl.lightning_fit
• dasf.ml.dl.pytorch_lightning

Classes

LightningTrainer	Initialize the LightningFit class.
NeuralNetClassifier	Class representing a Fit operation of the pipeline.

Package Contents

class dasf.ml.dl.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

modelLightningModule

The LightningModule instance representing the model to be trained.

use_gpubool, optional

Flag indicating whether to use GPU for training, by default False.

batch_sizeint, optional

The batch size for training, by default 1.

max_epochsint, optional

The maximum number of epochs for training, by default 1.

limit_train_batchesint, optional

The number of batches to consider for training, by default None.

limit_val_batchesint, optional

The number of batches to consider for validation, by default None.

devicesint, optional

The number of devices to use for training, by default “auto”.

num_nodesint, optional

The number of nodes to use for distributed training, by default 1.

shufflebool, optional

Flag indicating whether to shuffle the data during training, by default True.

strategystr, optional

The strategy to use for distributed training, by default “ddp”.

unsqueeze_dimint, optional

The dimension to unsqueeze the input data, by default None.

model

accelerator

batch_size

max_epochs

limit_train_batches

limit_val_batches

devices

num_nodes

shuffle

strategy

unsqueeze_dim

fit(train_data, val_data=None)

Perform the training of the model using torch Lightning.

Parameters

train_dataAny

A dasf map-style like dataset containing the training data.

val_dataAny, optional

A dasf map-style like dataset containing the validation data.

Parameters:

• train_data (Any)

• val_data (Any)

_fit(train_data, val_data=None)

_lazy_fit_cpu(train_data, val_data=None)

_lazy_fit_gpu(train_data, val_data=None)

_fit_cpu(train_data, val_data=None)

_fit_gpu(train_data, val_data=None)

Parameters:

• use_gpu (bool)

• batch_size (int)

• max_epochs (int)

• limit_train_batches (int)

• limit_val_batches (int)

• devices (int)

• num_nodes (int)

• shuffle (bool)

• strategy (str)

• unsqueeze_dim (int)

class dasf.ml.dl.NeuralNetClassifier(model, max_iter=100, batch_size=32)

Bases: dasf.transforms.base.Fit

Class representing a Fit operation of the pipeline.

_model

_accel = None

_strategy = None

_max_iter

_devices = 0

_ngpus = 0

_batch_size

__trainer = False

__handler

_lazy_fit_generic(X, y, accel, ngpus)

_lazy_fit_gpu(X, y=None)

Respective lazy fit mocked function for GPUs.

_lazy_fit_cpu(X, y=None)

Respective lazy fit mocked function for CPUs.

__fit_generic(X, y, accel, ngpus)

_fit_gpu(X, y=None)

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

_fit_cpu(X, y=None)

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