composer.Event

Events represent specific points in the training loop where a Algorithm and Callback can run.

Note

By convention, Callback should not be modifying the state, and are used for non-essential reporting functions such as logging or timing. Methods that need to modify state should be Algorithm.

Events List

Available events include:

Name	Description
INIT	Immediately after model initialization, and before creation of optimizers and schedulers. Model surgery typically occurs here.
TRAINING_START	Start of training. For multi-GPU training, runs after the DDP process fork.
EPOCH_START, EPOCH_END	Start and end of an Epoch.
BATCH_START, BATCH_END	Start and end of a batch, inclusive of the optimizer step and any gradient scaling.
AFTER_DATALOADER	Immediately after the dataloader is called. Typically used for on-GPU dataloader transforms.
BEFORE_TRAIN_BATCH, AFTER_TRAIN_BATCH	Before and after the forward-loss-backward computation for a training batch. When using gradient_accumulation, these are still called only once.
BEFORE_FORWARD, AFTER_FORWARD	Before and after the call to model.forward()
BEFORE_LOSS, AFTER_LOSS	Before and after the loss computation.
BEFORE_BACKWARD, AFTER_BACKWARD	Before and after the backward pass.
TRAINING_END	End of training.
EVAL_START, EVAL_END	Start and end of evaluation through the validation dataset.
EVAL_BATCH_START, EVAL_BATCH_END	Before and after the call to model.validate(batch)
EVAL_BEFORE_FORWARD, EVAL_AFTER_FORWARD	Before and after the call to model.validate(batch)

API Reference