我已经从使用单个 gpu 转变为使用多个 gpu。代码抛出错误
epoch main/loss validation/main/loss elapsed_time
Exception in main training loop: '<' not supported between instances of
'list' and 'int'
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/chainer_p36/lib/python3.6/site-
packages/chainer/training/trainer.py", line 318, in run
entry.extension(self)
File "/home/ubuntu/anaconda3/envs/chainer_p36/lib/python3.6/site-
packages/chainer/training/extensions/evaluator.py", line 157, in
__call__
result = self.evaluate()
File "/home/ubuntu/anaconda3/envs/chainer_p36/lib/python3.6/site-
packages/chainer/training/extensions/evaluator.py", line 206, in evaluate
in_arrays = self.converter(batch, self.device)
File "/home/ubuntu/anaconda3/envs/chainer_p36/lib/python3.6/site-
packages/chainer/dataset/convert.py", line 150, in concat_examples
return to_device(device, _concat_arrays(batch, padding))
File "/home/ubuntu/anaconda3/envs/chainer_p36/lib/python3.6/site-
packages/chainer/dataset/convert.py", line 35, in to_device
elif device < 0:
将在重新提出异常之前完成培训师扩展和更新程序。
我试过不使用gpu它工作得很好。但是当使用单个 gpu 时，出现内存不足的错误。所以，移动了 p28xlarge 实例，现在它抛出了上述错误。问题出在哪里以及如何解决？
使用 8 个 gpu 完成更改
num_gpus = 8
chainer.cuda.get_device_from_id(0).use()
3.#更新程序
if num_gpus > 0:
updater = training.updater.ParallelUpdater(
train_iter,
optimizer,
devices={('main' if device == 0 else str(device)): device for
device in range(num_gpus)},
)
else:
updater = training.updater.StandardUpdater(train_iter, optimizer,
device=args.gpus)
4.and 儿子.. 5.Training :
trainer.run()
输出 -- epoch main/loss validation/main/loss elapsed_time 主训练循环中的异常：在 'list' 和 'int' 的实例之间不支持 '<'
我期望输出为
epoch main/loss validation/main/loss elapsed_time
1.
2.
3. and so on till it converge's.