[MRG] Parallelisation of decomposition/sparse_encode #13005
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qinhanmin2014
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doc/whats_new/v0.20.rst
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| :mod:`sklearn.decomposition` | ||
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| - |Fix| Fixed a bug in :meth:`decomposition.sparse_encode` where computation was single |
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I think we should use :func: here.
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qinhanmin2014
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jnothman
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jnothman
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I've checked all the |
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…arn#13005)" This reverts commit 1ffbb6e.
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…arn#13005)" This reverts commit 1ffbb6e.
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I have a multiprocessing setup, where each process runs a SparseCoder with n_jobs=1, and in the main thread I split my data to these worker processes. In scikit-learn=0.20.2 it works perfectly fine (parallelization makes processing faster as expected), but when I updated to 0.20.3 it gets super slow (parallel computing takes ~10 times longer than doing it in just one process). |
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Reference Issues/PRs
xref #12955
What does this implement/fix? Explain your changes.
sparse encode (dict learning uses sparse encode) is running in single-threaded mode irrespective of n_jobs parameters, the parallel execution code is unreachable since
effective_n_jobsalways returns a positive integer.scikit-learn/sklearn/decomposition/dict_learning.py
Lines 303 to 316 in ff46f6e