How to construct `Dataset` with iterator for multi-process `DataLoader`?

I want to do something like this:

class myDataset():
    def __init__():
        self.iter = some_iterator_on_my_dataset
    def __len__():
        return len_of_my_dataset
    def __getitem__():

loader = DataLoader(myDataset, num_workers=16)

which works well when num_workers=1, failed when num_workers>1 however.
I guess that self.iter, the iterator failed in multi-process loader.
How shuold I do that?

It usually works good for more number of workers, have you tried with 2 or 4 workers?
Also, how many cores does your computer have?

I got more than 40 cores in my computer, I tried some num_workers like 20, 25, it failed anyway.

You might want to try out the new IterableDataset on PyTorch master or nightly release. However note that to correctly use num_workers>0 you will have to configure your dataset based on the worker info to avoid generating duplicate data.

It doesn’t seem to work for me. I was trying to benchmark the old dataset with the new IterableDataset.
Can you look out what’s wrong here?

My Cifar100 images are in folders.

def read_files(files):
    images = [torch.tensor(np.array( for i in files]
    yield images

class Cifar100Dataset(IterableDataset):
    def __init__(self, path, folder, start, end):
        self.files = [file for directory in (path / folder).ls() for file in]
        self.start = start
        self.end = end
    def __iter__(self):
        worker_info =
        if worker_info is None:
            return read_files(self.files[self.start:self.end])
            per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
            worker_id =
            iter_start = self.start + worker_id * per_worker
            iter_end = min(iter_start + per_worker, self.end)
            return iter(read_files(self.files[iter_start:iter_end]))

It works well with num_workers = 0, but does not work with num_worker>1

ds = Cifar100Dataset(path, train_folder, start=0, end=1024)

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x000001F99F5CB9D8>
Traceback (most recent call last):
  File "C:\Users\Divyansh J\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\", line 883, in __del__
  File "C:\Users\Divyansh J\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\", line 860, in _shutdown_workers
    if self.workers_status[worker_id]:
IndexError: list index out of range

BrokenPipeError                           Traceback (most recent call last)
<timed exec> in <module>

~\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\ in __iter__(self)
    240             return _SingleProcessDataLoaderIter(self)
    241         else:
--> 242             return _MultiProcessingDataLoaderIter(self)
    244     @property

~\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\data\ in __init__(self, loader)
    639             #     before it starts, and __del__ tries to join but will get:
    640             #     AssertionError: can only join a started process.
--> 641             w.start()
    642             self.index_queues.append(index_queue)
    643             self.workers.append(w)

~\Anaconda3\envs\pytorch\lib\multiprocessing\ in start(self)
    110                'daemonic processes are not allowed to have children'
    111         _cleanup()
--> 112         self._popen = self._Popen(self)
    113         self._sentinel = self._popen.sentinel
    114         # Avoid a refcycle if the target function holds an indirect

~\Anaconda3\envs\pytorch\lib\multiprocessing\ in _Popen(process_obj)
    221     @staticmethod
    222     def _Popen(process_obj):
--> 223         return _default_context.get_context().Process._Popen(process_obj)
    225 class DefaultContext(BaseContext):

~\Anaconda3\envs\pytorch\lib\multiprocessing\ in _Popen(process_obj)
    320         def _Popen(process_obj):
    321             from .popen_spawn_win32 import Popen
--> 322             return Popen(process_obj)
    324     class SpawnContext(BaseContext):

~\Anaconda3\envs\pytorch\lib\multiprocessing\ in __init__(self, process_obj)
     87             try:
     88                 reduction.dump(prep_data, to_child)
---> 89                 reduction.dump(process_obj, to_child)
     90             finally:
     91                 set_spawning_popen(None)

~\Anaconda3\envs\pytorch\lib\multiprocessing\ in dump(obj, file, protocol)
     58 def dump(obj, file, protocol=None):
     59     '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60     ForkingPickler(file, protocol).dump(obj)
     62 #

BrokenPipeError: [Errno 32] Broken pipe

Also I want to know the usage of start, and end and what should be the batch_size I should pass in the Dataloader, because I am already specifying the start and end in Dataloader.