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Add ExtraDataFunctor for integration with pasha #299
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philsmt
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,50 @@ | ||
from os import getpid | ||
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import numpy as np | ||
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from . import DataCollection, KeyData | ||
from .read_machinery import split_trains | ||
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class ExtraDataFunctor: | ||
"""Pasha functor for EXtra-data objects. | ||
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This functor wraps an EXtra-data DataCollection, SourceData or | ||
KeyData and performs the map operation over its trains. The kernel | ||
is passed the current train's index in the collection, the train ID | ||
and the data mapping (for DataCollection) or data entry (for KeyData). | ||
""" | ||
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def __init__(self, obj): | ||
self.obj = obj | ||
self.n_trains = len(self.obj.train_ids) | ||
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# Save PID of parent process where the functor is created to | ||
# close files as appropriately later on, see comment below. | ||
self._parent_pid = getpid() | ||
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@classmethod | ||
def wrap(cls, value): | ||
if isinstance(value, (DataCollection, KeyData)): | ||
return cls(value) | ||
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def split(self, num_workers): | ||
return split_trains(self.n_trains, parts=num_workers) | ||
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def iterate(self, share): | ||
subobj = self.obj.select_trains(np.s_[share]) | ||
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# Older versions of HDF < 1.10.5 are not robust against sharing | ||
# a file descriptor across threads or processes. If running in a | ||
# different process than the functor was initially created in, | ||
# close all file handles inherited from the parent collection to | ||
# force re-opening them again in each child process. | ||
if getpid() != self._parent_pid: | ||
for f in subobj.files: | ||
f.close() | ||
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index_it = range(*share.indices(self.n_trains)) | ||
data_it = subobj.trains() | ||
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for index, (train_id, data) in zip(index_it, data_it): | ||
yield index, train_id, data |
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Do we still need this at all with the dependencies we require?
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Probably not really.
It's a bit tricky to be definitive because the change is in HDF5, and h5py can be built with a wide range of HDF5 versions. EXtra-data supports h5py 2.10, and the pre-built packages of 2.10 on PyPI have HDF5 1.10.4. So I might bump the required version to >= 3.0, just to be a bit cautious. I hope very few people are still stuck on h5py 2.x.