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

class composer.Event(value)

Enum to represent events.

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

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 is still called only once.

BATCH_END

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

EPOCH_END

End of an epoch.

TRAINING_END

End of training.

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.