Re: [Pytables-users] PyTables and Multiprocessing

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Hi Anthony,

Thank you very much for your answer (it works). I will try to remodel my 
code around this trick but I'm not sure it's possible because I use a 
framework that need arrays.

Can somebody explain what is going on? I was thinking that PyTables keep 
weakref to the file for lazy loading but I'm not sure.

How

In any case, the PyTables community is very helpful.

Thanks,
Mathieu

Le 12/07/2013 00:44, Anthony Scopatz a écrit :
> Hi Mathieu,
>
> I think you should try opening a new file handle per process.  The 
> following works for me on v3.0:
>
> import tables
> import random
> import multiprocessing
>
> # Reload the data
>
> # Use multiprocessing to perform a simple computation (column average)
>
> def f(filename):
>     h5file = tables.openFile(filename, mode='r')
>     name = multiprocessing.current_process().name
>     column = random.randint(0, 10)
>     print '%s use column %i' % (name, column)
>     rtn = h5file.root.X[:, column].mean()
>     h5file.close()
>     return rtn
>
> p = multiprocessing.Pool(2)
> col_mean = p.map(f, ['test.hdf5', 'test.hdf5', 'test.hdf5'])
>
> Be well
> Anthony
>
>
> On Thu, Jul 11, 2013 at 3:43 PM, Mathieu Dubois 
> <dub...@ya... <mailto:dub...@ya...>> wrote:
>
>     Le 11/07/2013 21:56, Anthony Scopatz a écrit :
>>
>>
>>
>>     On Thu, Jul 11, 2013 at 2:49 PM, Mathieu Dubois
>>     <dub...@ya...
>>     <mailto:dub...@ya...>> wrote:
>>
>>         Hello,
>>
>>         I wanted to use PyTables in conjunction with multiprocessing
>>         for some
>>         embarrassingly parallel tasks.
>>
>>         However, it seems that it is not possible. In the following (very
>>         stupid) example, X is a Carray of size (100, 10) stored in
>>         the file
>>         test.hdf5:
>>
>>         import tables
>>
>>         import multiprocessing
>>
>>         # Reload the data
>>
>>         h5file = tables.openFile('test.hdf5', mode='r')
>>
>>         X = h5file.root.X
>>
>>         # Use multiprocessing to perform a simple computation (column
>>         average)
>>
>>         def f(X):
>>
>>              name = multiprocessing.current_process().name
>>
>>              column = random.randint(0, n_features)
>>
>>              print '%s use column %i' % (name, column)
>>
>>              return X[:, column].mean()
>>
>>         p = multiprocessing.Pool(2)
>>
>>         col_mean = p.map(f, [X, X, X])
>>
>>         When executing it the following error:
>>
>>         Exception in thread Thread-2:
>>
>>         Traceback (most recent call last):
>>
>>            File "/usr/lib/python2.7/threading.py", line 551, in
>>         __bootstrap_inner
>>
>>              self.run()
>>
>>            File "/usr/lib/python2.7/threading.py", line 504, in run
>>
>>              self.__target(*self.__args, **self.__kwargs)
>>
>>            File "/usr/lib/python2.7/multiprocessing/pool.py", line
>>         319, in _handle_tasks
>>
>>              put(task)
>>
>>         PicklingError: Can't pickle <type 'weakref'>: attribute
>>         lookup __builtin__.weakref failed
>>
>>
>>         I have googled for weakref and pickle but can't find a solution.
>>
>>         Any help?
>>
>>
>>     Hello Mathieu,
>>
>>     I have used multiprocessing and files opened in read mode many
>>     times so I am not sure what is going on here.
>     Thanks for your answer. Maybe you can point me to an working example?
>
>
>>     Could you provide the test.hdf5 file so that we could try to
>>     reproduce this.
>     Here is the script that I have used to generate the data:
>
>     import tables
>
>     import numpy
>
>     # Create data & store it
>
>     n_features = 10
>
>     n_obs      = 100
>
>     X = numpy.random.rand(n_obs, n_features)
>
>     h5file = tables.openFile('test.hdf5', mode='w')
>
>     Xatom = tables.Atom.from_dtype(X.dtype)
>
>     Xhdf5 = h5file.createCArray(h5file.root, 'X', Xatom, X.shape)
>
>     Xhdf5[:] = X
>
>     h5file.close()
>
>     I hope it's not a stupid mistake. I am using PyTables 2.3.1 on
>     Ubuntu 12.04 (libhdf5 is 1.8.4patch1).
>
>
>>         By the way, I have noticed that by slicing a Carray, I get a
>>         numpy array
>>         (I created the HDF5 file with numpy). Therefore, everything
>>         is copied to
>>         memory. Is there a way to avoid that?
>>
>>
>>     Only the slice that you ask for is brought into memory an it is
>>     returned as a non-view numpy array.
>     OK. I may be careful about that.
>
>
>>
>>     Be Well
>>     Anthony
>>
>>
>>         Mathieu
>>
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