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.

All state of decay 2 trainers