📆 Events

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

class composer.core.Event(value)

Enum to represent events in the training loop.

The following pseudocode shows where each event fires in the training loop:

# <INIT>
# <FIT_START>
for epoch in range(NUM_EPOCHS):
    # <EPOCH_START>
    for inputs, targets in dataloader:
        # <AFTER_DATALOADER>

        # <BATCH_START>

        # <BEFORE_FORWARD>
        outputs = model.forward(inputs)
        # <AFTER_FORWARD>

        # <BEFORE_LOSS>
        loss = model.loss(outputs, targets)
        # <AFTER_LOSS>

        # <BEFORE_BACKWARD>
        loss.backward()
        # <AFTER_BACKWARD>

        optimizer.step()

        # <BATCH_END>

        if should_eval(batch=True):
            # <EVAL_START>
            # <EVAL_BATCH_START>
            # <EVAL_BEFORE_FORWARD>
            # <EVAL_AFTER_FORWARD>
            # <EVAL_BATCH_END>
            # <EVAL_END>

        # <BATCH_CHECKPOINT>
    # <EPOCH_END>

    if should_eval(batch=False):
        # <EVAL_START>
        # <EVAL_BATCH_START>
        # <EVAL_BEFORE_FORWARD>
        # <EVAL_AFTER_FORWARD>
        # <EVAL_BATCH_END>
        # <EVAL_END>

    # <EPOCH_CHECKPOINT>

INIT

Invoked in the constructor of Trainer. Model surgery (see module_surgery) typically occurs here.

FIT_START

Invoked at the beginning of each call to Trainer.fit(). Dataset transformations typically occur here.

EPOCH_START

Start of an epoch.

BATCH_START

Start of a batch.

AFTER_DATALOADER

Immediately after the dataloader is called. Typically used for on-GPU dataloader transforms.

BEFORE_TRAIN_BATCH

Before the forward-loss-backward computation for a training batch. When using gradient accumulation, this is still called only once.

BEFORE_FORWARD

Before the call to model.forward().

AFTER_FORWARD

After the call to model.forward().

BEFORE_LOSS

Before the call to model.loss().

AFTER_LOSS

After the call to model.loss().

BEFORE_BACKWARD

Before the call to loss.backward().

AFTER_BACKWARD

After the call to loss.backward().

AFTER_TRAIN_BATCH

After the forward-loss-backward computation for a training batch. When using gradient accumulation, this event still fires only once.

BATCH_END

End of a batch, which occurs after the optimizer step and any gradient scaling.

BATCH_CHECKPOINT

After Event.BATCH_END and any batch-wise evaluation. Saving checkpoints at this event allows the checkpoint saver to use the results from any batch-wise evaluation to determine whether a checkpoint should be saved.

EPOCH_END

End of an epoch.

EPOCH_CHECKPOINT

After Event.EPOCH_END and any epoch-wise evaluation. Saving checkpoints at this event allows event allows the checkpoint saver to use the results from any epoch-wise evaluation to determine whether a checkpointshould be saved.

EVAL_START

Start of evaluation through the validation dataset.

EVAL_BATCH_START

Before the call to model.validate(batch)

EVAL_BEFORE_FORWARD

Before the call to model.validate(batch)

EVAL_AFTER_FORWARD

After the call to model.validate(batch)

EVAL_BATCH_END

After the call to model.validate(batch)

EVAL_END

End of evaluation through the validation dataset.