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gradient_accumulation_scheduler

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GradientAccumulationScheduler

Change gradient accumulation factor according to scheduling.

Gradient Accumulator

Change gradient accumulation factor according to scheduling. Trainer also calls optimizer.step() for the last indivisible step number.

class pytorch_lightning.callbacks.gradient_accumulation_scheduler.GradientAccumulationScheduler(scheduling)[source]

Bases: pytorch_lightning.callbacks.base.Callback

Change gradient accumulation factor according to scheduling.

Parameters

scheduling (Dict[int, int]) – scheduling in format {epoch: accumulation_factor}

Note

The argument scheduling is a dictionary. Each key represent an epoch and its associated accumulation factor value. Warning: Epoch are zero-indexed c.f it means if you want to change the accumulation factor after 4 epochs, set Trainer(accumulate_grad_batches={4: factor}) or GradientAccumulationScheduler(scheduling={4: factor}). For more info check the example below.

Raises
  • TypeError – If scheduling is an empty dict, or not all keys and values of scheduling are integers.

  • IndexError – If minimal_epoch is less than 0.

Example:

>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import GradientAccumulationScheduler

# from epoch 5, it starts accumulating every 2 batches. Here we have 4 instead of 5
because epoch (key) should be zero-indexed.
>>> accumulator = GradientAccumulationScheduler(scheduling={4: 2})
>>> trainer = Trainer(callbacks=[accumulator])

# alternatively, pass the scheduling dict directly to the Trainer
>>> trainer = Trainer(accumulate_grad_batches={4: 2})
on_train_epoch_start(trainer, *_)[source]

Called when the train epoch begins.

Return type

None

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