The question about running lstm.sh

[speaker-lover]

When i run the lstm.sh which in eend/egs/mini_librisppech/v1/local/run_blstm, i get the usingwarning: shared memory size is too small.
Please set shared_mem option for MultiprocessIterator.
Expect shared memory size: 4298700 bytes.
Actual shared meory size: 3118900 bytes.
But i use v100, 16G memory!
The error information is follow.

Exception in main training loop: 'cupy.cuda.memory.MemoryPointer' object is not iterable
Traceback (most recent call last):
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/trainer.py", line 316, in run
update()
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 175, in update
self.update_core()
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 189, in update_core
optimizer.update(loss_func, **in_arrays)
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/optimizer.py", line 825, in update
loss = lossfun(*args, **kwds)
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 588, in call
F.stack([dc_loss(em, t) for (em, t) in zip(ems, ts)]))
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 588, in
F.stack([dc_loss(em, t) for (em, t) in zip(ems, ts)]))
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 333, in dc_loss
[int(''.join(str(x) for x in t), base=2) for t in label.data]] = 1
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 333, in dc_loss
[int(''.join(str(x) for x in t), base=2) for t in label.data]] = 1
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
File "../../../eend/bin/train.py", line 82, in
train(args)
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/train.py", line 223, in train
trainer.run()
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/trainer.py", line 349, in run
six.reraise(*exc_info)
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/six.py", line 719, in reraise
raise value
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/trainer.py", line 316, in run
update()
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 175, in update
self.update_core()
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/training/updaters/standard_updater.py", line 189, in update_core
optimizer.update(loss_func, **in_arrays)
File "/home/chenyafeng.cyf/EEND-master/tools/miniconda3/envs/eend/lib/python3.7/site-packages/chainer/optimizer.py", line 825, in update
loss = lossfun(*args, **kwds)
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 588, in call
F.stack([dc_loss(em, t) for (em, t) in zip(ems, ts)]))
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 588, in
F.stack([dc_loss(em, t) for (em, t) in zip(ems, ts)]))
File "/home/chenyafeng.cyf/EEND-master/eend/chainer_backend/models.py", line 333, in dc_loss
[int(''.join(str(x) for x in t), base=2) for t in label.data]] = 1
TypeError: 'cupy.cuda.memory.MemoryPointer' object is not iterable

What should i do? I have no idea to solve it.

[DiLiangWU]

I have the same question when i run local/run_blstm.sh. Have you solved the problem yet?

TypeError: 'cupy.cuda.memory.MemoryPointer' object is not iterable

[DiLiangWU]

Maybe I have solved the error [TypeError: 'cupy.cuda.memory.MemoryPointer' object is not iterable]. You need change "for t in label.data" to "for t in label" in eend/chainer_backend/models.py, line 333.

But i still get the userwarning same with you. The warning information is:
shared memory size is too small.
Please set shared_mem option for MultiprocessIterator.
Expect shared memory size: 2085180 bytes.
Actual shared memory size: 1643796 bytes.