maybe try to disable memory pinning in the data loader By changing line 62 inrun_training.pyand following from this: dataloader=DataLoader(train_data,batch_size=batch_size,drop_last=True,shuffle=True,num_workers=workers,collate_fn=cut_paste_collate_fn,persistent_workers=True,pin_memory=True,pref...
from torch.utils.data import DataLoader DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None, *, prefetch_factor=2, persistent_workers=False) 简单描述一下以下...
I felt raw but ebullient, for the first time in recent memory. But then, a sharp, reflected light hit my eye, completely derailing my diatribe about my very unremarkable fifth birthday party. I looked over and I saw a six-foot, chain-link spider web being wheeled out onto the stage, ...
.persistentAggregate(new MemoryMapState.Factory(), new Count(), new Fields("count")).parallelismHint(16); // .persistentAggregate(new HazelCastStateFactory(), new Count(), // new Fields("aggregates_words")).parallelismHint(2); return topology.build(); } public static void main(String[] ...
In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so cache invalidation algorithms such as ...
然后这边将persistent_workers置为False,我没有单独设置no_pin_memory和no_persistent_workers,也不知道怎么在config中指定persistent_workers。 可不可以贴一下你使用的 config? 我又检查了下config,果然设置了persistent_workers=True,删掉就OK了 谢谢 Read more comments on GitHub> ...
否则的话只指定 pin_memory=True 应该是可以工作的 dev1.x版本不是默认为True吗,https://github.com/open-mmlab/mmclassification/blob/743ca2d602631856a971510090c386712d0eac32/tools/train.py#L117-L118 然后这边将persistent_workers置为False,我没有单独设置no_pin_memory和no_persistent_workers,也不知道...