pipelinex.extras.ops.ignite.declaratives package

Submodules

pipelinex.extras.ops.ignite.declaratives.declarative_trainer module

class pipelinex.extras.ops.ignite.declaratives.declarative_trainer.NetworkTrain(loss_fn=None, epochs=None, seed=None, optimizer=None, optimizer_params={}, train_data_loader_params={}, val_data_loader_params={}, evaluation_metrics=None, evaluate_train_data=None, evaluate_val_data=None, progress_update=None, scheduler=None, scheduler_params={}, model_checkpoint=None, model_checkpoint_params={}, early_stopping_params={}, time_limit=None, train_dataset_size_limit=None, val_dataset_size_limit=None, cudnn_deterministic=None, cudnn_benchmark=None, mlflow_logging=True, train_params={})

Bases: object

Create a trainer for a supervised PyTorch model.

Parameters

• loss_fn (callable) – Loss function used to train. Accepts an instance of loss functions at https://pytorch.org/docs/stable/nn.html#loss-functions

• epochs (int, optional) – Max epochs to train

• seed (int, optional) – Random seed for training.

• optimizer (torch.optim, optional) – Optimizer used to train. Accepts optimizers at https://pytorch.org/docs/stable/optim.html

• optimizer_params (dict, optional) – Parameters for optimizer.

• train_data_loader_params (dict, optional) – Parameters for data loader for training. Accepts args at https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader

• val_data_loader_params (dict, optional) – Parameters for data loader for validation. Accepts args at https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader

• evaluation_metrics (dict, optional) – Metrics to compute for evaluation. Accepts dict of metrics at https://pytorch.org/ignite/metrics.html

• evaluate_train_data (str, optional) – When to compute evaluation_metrics using training dataset. Accepts events at https://pytorch.org/ignite/engine.html#ignite.engine.Events

• evaluate_val_data (str, optional) – When to compute evaluation_metrics using validation dataset. Accepts events at https://pytorch.org/ignite/engine.html#ignite.engine.Events

• progress_update (bool, optional) – Whether to show progress bar using tqdm package

• scheduler (ignite.contrib.handle.param_scheduler.ParamScheduler, optional) – Param scheduler. Accepts a ParamScheduler at https://pytorch.org/ignite/contrib/handlers.html#module-ignite.contrib.handlers.param_scheduler

• scheduler_params (dict, optional) – Parameters for scheduler

• model_checkpoint (ignite.handlers.ModelCheckpoint, optional) – Model Checkpoint. Accepts a ModelCheckpoint at https://pytorch.org/ignite/handlers.html#ignite.handlers.ModelCheckpoint

• model_checkpoint_params (dict, optional) – Parameters for ModelCheckpoint at https://pytorch.org/ignite/handlers.html#ignite.handlers.ModelCheckpoint

• early_stopping_params (dict, optional) – Parameters for EarlyStopping at https://pytorch.org/ignite/handlers.html#ignite.handlers.EarlyStopping

• time_limit (int, optioinal) – Time limit for training in seconds.

• train_dataset_size_limit (int, optional) – If specified, only the subset of training dataset is used. Useful for quick preliminary check before using the whole dataset.

• val_dataset_size_limit (int, optional) – If specified, only the subset of validation dataset is used. useful for qucik preliminary check before using the whole dataset.

• cudnn_deterministic (bool, optional) – Value for torch.backends.cudnn.deterministic. See https://pytorch.org/docs/stable/notes/randomness.html for details.

• cudnn_benchmark (bool, optional) – Value for torch.backends.cudnn.benchmark. See https://pytorch.org/docs/stable/notes/randomness.html for details.

• mlflow_logging (bool, optional) – If True and MLflow is installed, MLflow logging is enabled.

Returns

a callable to train a PyTorch model.

Return type

trainer (callable)

__init__(loss_fn=None, epochs=None, seed=None, optimizer=None, optimizer_params={}, train_data_loader_params={}, val_data_loader_params={}, evaluation_metrics=None, evaluate_train_data=None, evaluate_val_data=None, progress_update=None, scheduler=None, scheduler_params={}, model_checkpoint=None, model_checkpoint_params={}, early_stopping_params={}, time_limit=None, train_dataset_size_limit=None, val_dataset_size_limit=None, cudnn_deterministic=None, cudnn_benchmark=None, mlflow_logging=True, train_params={})

Initialize self. See help(type(self)) for accurate signature.