Parallel processing using Joblib #973
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| @@ -124,37 +124,26 @@ def sample(draws, step=None, start=None, trace=None, chain=0, njobs=1, tune=None | |||
| step = assign_step_methods(model, step) | |||
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| if njobs is None: | |||
| import multiprocessing | |||
| njobs = max(mp.cpu_count() - 2, 1) | |||
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I wonder if we should just set the default to 2. That way everyone gets 2 chains unless they say otherwise, which allows Gelman-Rubin to be calculated.
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Sounds good to me. But should make sure it's stable across platforms first.
Add Binary Gibbs Metropolis sampler for mixture models.
Improved handling of discrete variables by metropolis step method
COSMIT PEP-8-ted ``pymc3.models``
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@fonnesbeck Not sure what the status here is but it looks good to me. I suppose it doesn't affect the recursion problem but simplifies the code? |
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Was just awaiting review. Will rebase and merge. |
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February 23, 2016 07:35
fonnesbeck
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Feb 23, 2016
Parallel processing using Joblib
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I simply get a segmentation fault (core dumped) now when using njobs>1 for a complex model. No trace-back nothing. For simple models it works fine! |
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Implemented Joblib to control parallel processing, rather than
multiprocessing. Greatly simplifiessampleas a side-benefit. Closes #879