单GPU跑的程序,而且是在docker中,迭代了几百步后,程序突然崩掉了,
程序停在了 for step,data in enumerate(loader),下面是部分bug信息
Traceback (most recent call last):
........
File ".../torch/utils/data/dataloader.py", line 206, in __next__
idx, batch = self.data_queue.get()
File "/usr/lib/python2.7/multiprocessing/queues.py", line 378, in get
return recv()
File ".../torch/multiprocessing/queue.py", line 22, in recv
return pickle.loads(buf)
File "/usr/lib/python2.7/pickle.py", line 1388, in loads
return Unpickler(file).load()
File "/usr/lib/python2.7/pickle.py", line 864, in load
dispatch[key](self)
File "/usr/lib/python2.7/pickle.py", line 1139, in load_reduce
value = func(*args)
File ".../torch/multiprocessing/reductions.py", line 68, in rebuild_storage_fd
fd = multiprocessing.reduction.rebuild_handle(df)
File "/usr/lib/python2.7/multiprocessing/reduction.py", line 155, in rebuild_handle
conn = Client(address, authkey=current_process().authkey)
File "/usr/lib/python2.7/multiprocessing/connection.py", line 175, in Client
answer_challenge(c, authkey)
File "/usr/lib/python2.7/multiprocessing/connection.py", line 432, in answer_challenge
message = connection.recv_bytes(256) # reject large message
IOError: [Errno 104] Connection reset by peer
我以为是enumerate的问题,出现了脏数据,但细想不可能啊,都迭代了一个epoch了,
查看资料,追踪这个error,Connection reset by peer,网上说是https://github.com/pytorch/pytorch/issues/9127,
以前版本有bug,
需要将新版本的 torch/_six.py
and torch/utils/data/dataloader.py
替换以前的版本,
工作量大,被这个思路带着走,完全跑偏了。放弃了,
查询DataLoader的参数,有建议把batch_size调小,调到了1,
num_workers值也调到了1,还是报错,
DataLoader的函数定义如下:
DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
num_workers=0, collate_fn=default_collate, pin_memory=False,
drop_last=False)
1. dataset:加载的数据集
2. batch_size:batch size
3. shuffle::是否将数据打乱
4. sampler: 样本抽样
5. num_workers:使用多进程加载的进程数,0代表不使用多进程
6. collate_fn: 如何将多个样本数据拼接成一个batch,一般使用默认的拼接方式即可
7. pin_memory:是否将数据保存在pin memory区,pin memory中的数据转到GPU会快一些
8. drop_last:dataset中的数据个数可能不是batch_size的整数倍,drop_last为True会将多出来不足一个batch的数据丢弃
于是将num_workers参数值改成了默认值 0,不用多进程跑,程序可以运行了,激动万分,感激涕零啊
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