Enable setting of training iteration in Trainers

Is your feature request related to a problem? Please describe. Currently, SupervisedTrainer supports controlling the number of iterations by adjusting epoch_length and max_epochs. It would be nice to be able to set the number of iterations to be executed directly.

Describe the solution you’d like Add a n_iterations argument (or similar) that allows overwriting epoch-based definitions of the training steps number of training steps to be executed. Note, this should allow the training to resume from the final iteration if n_iterations is reached. Related to #4554.

Describe alternatives you’ve considered Live with adjusting epoch_length and max_epochs but that seems confusing.

Additional context Add any other context or screenshots about the feature request here.

[holgerroth]

epoch_length corresponds to number of iterations needed to iterate once of the data (i.e., one epoch). It defaults to len(train_data_loader).

[vfdev-5]

@holgerroth we have on master and in nightly releases max_iters arg for Engine.run(), https://pytorch.org/ignite/master/generated/ignite.engine.engine.Engine.html#ignite.engine.engine.Engine.run It probably works as you asked for:

assert len(data) == 100
s = trainer.run(data, max_iters=123)
assert s.iteration = 123

However, we haven’t yet released that in stable as there can be issues with how this is saved/loaded in checkpoints etc.

A workaround for stable release to that can be

max_iters = 1234
epoch_length = len(data)
max_epochs = max_iters // epoch_length + 1

@trainer.on(Events.ITERATION_COMPLETED(once=max_iters))
def stop():
    trainer.terminate()

trainer.run(data, max_epochs=max_epochs)