INNER CODE UNIT · Python
end_epoch
NVIDIA/DeepLearningExamples · PyTorch/Classification/ConvNets/main.py:633
end_epoch=min((start_epoch + args.run_epochs), args.epochs)
if args.run_epochs != -1
else args.epochs,
early_stopping_patience=args.early_stopping_patience,
best_prec1=best_prec1,
prof=args.prof,
skip_training=args.evaluate,
skip_validation=args.training_only,
save_checkpoints=args.save_checkpoints and not args.evaluate,
checkpoint_dir=args.workspace,
checkpoint_filename=args.checkpoint_filename,
keep_last_n_checkpoints=args.gather_checkpoints,
topk=args.topk,
)
exp_duration = time.time() - exp_start_time
if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
logger.end()
print("Experiment ended")