Closed Kwentar closed 2 years ago
Hi, in DDP training we have DistributedSampler which has .set_epoch method (https://pytorch.org/docs/stable/data.html) It looks like:
.set_epoch
>>> for epoch in range(start_epoch, n_epochs): ... if is_distributed: ... sampler.set_epoch(epoch) ... train(loader)
How does it work here? Do we need do something for each epoch or we can just remove sampler.set_epoch while migrating to ffcv?
sampler.set_epoch
Hi @Kwentar
We do not require that in FFCV, just use it normally. Please refer to our imagenet example which supports distributed training
Hi, in DDP training we have DistributedSampler which has
.set_epoch
method (https://pytorch.org/docs/stable/data.html) It looks like:How does it work here? Do we need do something for each epoch or we can just remove
sampler.set_epoch
while migrating to ffcv?