Trainer API¶
The TTS.trainer.Trainer provides a lightweight, extensible, and feature-complete training run-time. We optimized it for 🐸 but
can also be used for any DL training in different domains. It supports distributed multi-gpu, mixed-precision (apex or torch.amp) training.
Trainer¶
- class TTS.trainer.Trainer(args, config, output_path, c_logger=None, dashboard_logger=None, model=None, cudnn_benchmark=False)[source]¶
- eval_epoch()[source]¶
Main entry point for the evaluation loop. Run evaluation on the all validation samples.
- eval_step(batch, step)[source]¶
Perform a evaluation step on a batch of inputs and log the process.
- Parameters
batch (Dict) – Input batch.
step (int) – Current step number in this epoch.
- Returns
Model outputs and losses.
- Return type
Tuple[Dict, Dict]
- format_batch(batch)[source]¶
Format the dataloader output and return a batch.
- Parameters
batch (List) – Batch returned by the dataloader.
- Returns
Formatted batch.
- Return type
Dict
- static get_criterion(model)[source]¶
Receive the criterion from the model. Model must implement get_criterion().
- Parameters
model (nn.Module) – Training model.
- Returns
Criterion layer.
- Return type
nn.Module
- static get_lr(model, config)[source]¶
Set the initial learning rate by the model if model implements get_lr() else try setting the learning rate fromthe config.
- Parameters
model (nn.Module) – Training model.
config (Coqpit) – Training configuration.
- Returns
A single learning rate or a list of learning rates, one for each optimzier.
- Return type
Union[float, List[float]]
- static get_model(config)[source]¶
Initialize model from config.
- Parameters
config (Coqpit) – Model config.
- Returns
initialized model.
- Return type
nn.Module
- static get_optimizer(model, config)[source]¶
Receive the optimizer from the model if model implements get_optimizer() else check the optimizer parameters in the config and try initiating the optimizer.
- Parameters
model (nn.Module) – Training model.
config (Coqpit) – Training configuration.
- Returns
A optimizer or a list of optimizers. GAN models define a list.
- Return type
Union[torch.optim.Optimizer, List]
- static get_scheduler(model, config, optimizer)[source]¶
Receive the scheduler from the model if model implements get_scheduler() else check the config and try initiating the scheduler.
- Parameters
model (nn.Module) – Training model.
config (Coqpit) – Training configuration.
- Returns
A scheduler or a list of schedulers, one for each optimizer.
- Return type
Union[torch.optim.Optimizer, List]
- get_train_dataloader(ap, data_items, verbose)[source]¶
Initialize and return a training data loader.
- Parameters
ap (AudioProcessor) – Audio processor.
data_items (List) – Data samples used for training.
verbose (bool) – enable/disable printing loader stats at initialization.
- Returns
Initialized training data loader.
- Return type
DataLoader
- static master_params(optimizer)[source]¶
Generator over parameters owned by the optimizer.
Used to select parameters used by the optimizer for gradient clipping.
- Parameters
optimizer – Target optimizer.
- restore_model(config, restore_path, model, optimizer, scaler=None)[source]¶
Restore training from an old run. It restores model, optimizer, AMP scaler and training stats.
- Parameters
config (Coqpit) – Model config.
restore_path (str) – Path to the restored training run.
model (nn.Module) – Model to restored.
optimizer (torch.optim.Optimizer) – Optimizer to restore.
scaler (torch.cuda.amp.GradScaler, optional) – AMP scaler to restore. Defaults to None.
- Returns
[description]
- Return type
Tuple[nn.Module, torch.optim.Optimizer, torch.cuda.amp.GradScaler, int]
- save_best_model()[source]¶
Save the best model. It only saves if the current target loss is smaller then the previous.
- test_run()[source]¶
Run test and log the results. Test run must be defined by the model. Model must return figures and audios to be logged by the Tensorboard.
- train_epoch()[source]¶
Main entry point for the training loop. Run training on the all training samples.
- train_step(batch, batch_n_steps, step, loader_start_time)[source]¶
Perform a training step on a batch of inputs and log the process.
- Parameters
batch (Dict) – Input batch.
batch_n_steps (int) – Number of steps needed to complete an epoch. Needed for logging.
step (int) – Current step number in this epoch.
loader_start_time (float) – The time when the data loading is started. Needed for logging.
- Returns
Model outputs and losses.
- Return type
Tuple[Dict, Dict]