dicee.trainer.torch_trainer_ddp

Classes

`TorchDDPTrainer`	A Trainer based on torch.nn.parallel.DistributedDataParallel
`NodeTrainer`

Functions

make_iterable_verbose(→ Iterable)

Module Contents

dicee.trainer.torch_trainer_ddp.make_iterable_verbose(iterable_object, verbose, desc='Default', position=None, leave=True) → Iterable[source]

class dicee.trainer.torch_trainer_ddp.TorchDDPTrainer(args, callbacks)[source]

Bases: dicee.abstracts.AbstractTrainer

A Trainer based on torch.nn.parallel.DistributedDataParallel

Arguments

entity_idxs: mapping.
relation_idxs: mapping.
form: ?
store: ?
label_smoothing_rate: Using hard targets (0,1) drives weights to infinity. An outlier produces enormous gradients.

Return type:: torch.utils.data.Dataset

fit(*args, **kwargs)[source]: Train model

class dicee.trainer.torch_trainer_ddp.NodeTrainer(trainer, model: torch.nn.Module, train_dataset_loader: torch.utils.data.DataLoader, callbacks, num_epochs: int)[source]

trainer

local_rank

global_rank

optimizer

train_dataset_loader

loss_func

callbacks

model

num_epochs

loss_history = []

extract_input_outputs(z: list)[source]

train()[source]: Training loop for DDP