I have a training script which I launch using
torch.distributed.launch on multiple GPUs. I would like to set a time limit, so that my training will early stop without surpassing this limit. Something like this:
# for storing the running times of the last 3 epochs epoch_time_queue = deque(maxlen=3) start_time = time.time() for epoch in range(start_epoch, args.epochs): start_epoch_time = time.time() # training train_epoch(...) # validation eval_epoch(...) # epoch time in minutes epoch_time = (time.time() - start_epoch_time)/60 # average duration of the last 3 epochs epoch_time_queue.append(epoch_time) avg_time = sum(epoch_time_queue)/len(epoch_time_queue) # if the next epoch will likely surpass the time limit, then stop here estimated_next_total_time = (time.time() - start_time)/60 + avg_time if args.time_limit > 0 and estimated_next_total_time > args.time_limit: break
The issue is that the elapsed time may be different between processes. For example, at the end of the 5th epoch, the process on GPU1 may think that it will surpass the time limit at the next (6th) epoch by a few seconds, so it stops; while GPU2 thinks that it will be able to finish the 6th epoch a few seconds before the limit, so it will continue, which is not good.
I would like to know if there is a way for the processes to communicate about this. Ideally, a process should wait for all the other processes to finish the current epoch to decide whether to go for the next epoch or not.