@odats this is done to have reproducble trainings = user can resume the training from a checkpoint (based on epoch or iteration) and have “almost” the same training behaviour. Please, see this : https://pytorch.org/ignite/concepts.html#resume-training 1
To alter the seed, it is possible to specify the seed as the argument in the run:
trainer.run(dataloader, seed=1234)

That being said we think to change this behaviour and master/nightly releases already setup the seed using torch default random generator : https://github.com/pytorch/ignite/pull/799
Hope this helps. Feel free to ask other questions.
PS. @ptrblck thanks for the notification ! I should have missed the email somehow…