I am working with large dataset of videos. And I modified a  torch.utils.data.Dataset which returens video path and send to my video decoder using opencv.
There is:
batch_proc = multiprocessing.Process(target=video_batcher, args=(queue, fv, batchsize))
which starts a new process for each video clip and calls join() after all batch of a video are processed. queue is multiprocessing.Queue object which stores a batch of converted frames, say, 128 images per batch.
fv is an object which maintains a buffer and read from video consistently using Threading.
I intended to concatenate every batch of feature map(after a pretrained CNN) and save as hdf5. but error reports like this for each loop:

Traceback (most recent call last):
File “/opt/conda/lib/python2.7/multiprocessing/queues.py”, line 268, in _feed
send(obj)
File “/opt/conda/lib/python2.7/site-packages/torch/multiprocessing/queue.py”, line 17, in send
ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(obj)
File “/opt/conda/lib/python2.7/pickle.py”, line 224, in dump
self.save(obj)
File “/opt/conda/lib/python2.7/pickle.py”, line 286, in save
f(self, obj) # Call unbound method with explicit self
File “/opt/conda/lib/python2.7/multiprocessing/forking.py”, line 67, in dispatcher
self.save_reduce(obj=obj, *rv)
File “/opt/conda/lib/python2.7/pickle.py”, line 401, in save_reduce
save(args)
File “/opt/conda/lib/python2.7/pickle.py”, line 286, in save
f(self, obj) # Call unbound method with explicit self
File “/opt/conda/lib/python2.7/pickle.py”, line 554, in save_tuple
save(element)
File “/opt/conda/lib/python2.7/pickle.py”, line 286, in save
f(self, obj) # Call unbound method with explicit self
File “/opt/conda/lib/python2.7/multiprocessing/forking.py”, line 66, in dispatcher
rv = reduce(obj)
File “/opt/conda/lib/python2.7/site-packages/torch/multiprocessing/reductions.py”, line 113, in reduce_storage
fd, size = storage.share_fd()
RuntimeError: $ Torch: unable to mmap memory: you tried to mmap 0GB. at /b/wheel/pytorch-src/torch/lib/TH/THAllocator.c:317

Is that allocator not working well with `multiprocessing.Queue``` ?