使用多处理和h5py

Question

I get an error when trying to run a command with joblib/multiprocessing in parallel:

Here the traceback:

Process PoolWorker-263:
Traceback (most recent call last):
File "/home/marcel/anaconda/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
            self.run()
          File "/home/marcel/anaconda/lib/python2.7/multiprocessing/process.py", line 114, in run
            self._target(*self._args, **self._kwargs)
          File "/home/marcel/anaconda/lib/python2.7/multiprocessing/pool.py", line 102, in worker
            task = get()
          File "/home/marcel/.local/lib/python2.7/site-packages/joblib/pool.py", line 363, in get
          File "_objects.pyx", line 240, in h5py._objects.ObjectID.__cinit__ (h5py/_objects.c:2994)
        TypeError: __cinit__() takes exactly 1 positional argument (0 given)

As you can see from the error message I work with data loaded using h5py. To complicate things further the routine I want to parallelize uses numba in one of its subroutines, but I hope that does not matter.

Here is a running example, which you can copy and paste:

from joblib import Parallel,delayed
import numpy as np
import h5py as h5
import os

def testfunc(h5data, row):
    # some very boneheaded CPU work
    data_slice = h5data[:,row,...]
    ma = np.mean(data_slice, axis = 1)
    x = row
    return ma, x

def run():
    data = np.random.random((100,100,100)) 
    print data
    f_out = h5.File('tmp.h5', 'w')
    dset = f_out.create_dataset('mydata', data = data )
    f_out.close()
    f_in = h5.File('tmp.h5', 'r')
    h5data = f_in['mydata']
    pool = Parallel(n_jobs=-1,verbose=1,pre_dispatch='all')
    results = pool(delayed(testfunc)(h5data, i) for i in range(h5data.shape[1]))
    f_in.close()
    os.remove('tmp.h5')


if __name__ == '__main__':
    run()

Any ideas, what I'm doing wrong?

Edit: Okay at least I can exclude numba from the list of evildoers...

Answer 1

1You can try to replace ˋjoblib with [pathos][1] which replaces pickle with dill`. This solves generally all pickling issues.