nebula.core.training.lightning

Contents

nebula.core.training.lightning#

Attributes#

logging_training

Classes#

`NebulaProgressBar`	Nebula progress bar for training.
`Lightning`

Module Contents#

nebula.core.training.lightning.logging_training#

class nebula.core.training.lightning.NebulaProgressBar#

Bases: lightning.pytorch.callbacks.ProgressBar

Nebula progress bar for training. Logs the percentage of completion of the training process using logging.

enable = True#

disable()#: Disable the progress bar logging.

on_train_epoch_start(trainer, pl_module)#: Called when the training epoch starts.

on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)#: Called at the end of each training batch.

on_train_epoch_end(trainer, pl_module)#: Called at the end of the training epoch.

on_validation_epoch_start(trainer, pl_module)#

on_validation_epoch_end(trainer, pl_module)#

on_test_batch_start(trainer, pl_module, batch, batch_idx, dataloader_idx)#

on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)#: Called at the end of each test batch.

on_test_epoch_start(trainer, pl_module)#

on_test_epoch_end(trainer, pl_module)#

class nebula.core.training.lightning.Lightning(model, data, config=None, logger=None)#

DEFAULT_MODEL_WEIGHT = 1#

BYPASS_MODEL_WEIGHT = 0#

model#

data#

config#

epochs = 1#

round = 0#

property logger#

get_round()#

set_model(model)#

set_data(data)#

create_trainer()#

validate_neighbour_model(neighbour_model_param)#

get_hash_model()#

Returns:: SHA256 hash of model parameters
Return type:: str

set_epochs(epochs)#

serialize_model(model)#

deserialize_model(data)#

set_model_parameters(params, initialize=False)#

get_model_parameters(bytes=False)#

async train()#

async test()#

get_model_weight()#

on_round_start()#

on_round_end()#

on_learning_cycle_end()#