predict - Returns predictions (with metrics if labels are available) on a test set.evaluate - Runs an evaluation loop and returns metrics.prediction_step - Performs an evaluation/test step.training_step - Performs a training step.compute_loss - Computes the loss on a batch of training inputs.create_scheduler - Sets up the learning rate scheduler if it wasn’t passed at init.create_optimizer - Sets up the optimizer if it wasn’t passed at init.Note, that you can also subclass or override the create_optimizer and create_scheduler methods create_optimizer_and_scheduler - Sets up the optimizer and learning rate scheduler if they were not passed at.

log - Logs information on the various objects watching training.get_test_dataloader - Creates the test DataLoader.get_eval_dataloader - Creates the evaluation DataLoader.get_train_dataloader - Creates the training DataLoader.To inject custom behavior you can subclass them and override the following methods:

The Trainer contains the basic training loop which supports the above features. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. It’s used in most of the example scripts.īefore instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases.

Trainer state of decay 2