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prevent overflow when pickling large numpy arrays #920



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@pierreglaser pierreglaser commented Jul 31, 2019

Fixes #859

It is hard to test as it requires CI engines with more than 16GB of RAM.

@ogrisel ogrisel merged commit f7f3c33 into joblib:master Sep 12, 2019
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netbsd-srcmastr pushed a commit to NetBSD/pkgsrc that referenced this issue Oct 31, 2019
Release 0.14.0
Improved the load balancing between workers to avoid stranglers caused by an excessively large batch size when the task duration is varying significantly (because of the combined use of joblib.Parallel and joblib.Memory with a partially warmed cache for instance). joblib/joblib#899
Add official support for Python 3.8: fixed protocol number in Hasher and updated tests.
Fix a deadlock when using the dask backend (when scattering large numpy arrays). joblib/joblib#914
Warn users that they should never use joblib.load with files from untrusted sources. Fix security related API change introduced in numpy 1.6.3 that would prevent using joblib with recent numpy versions. joblib/joblib#879
Upgrade to cloudpickle 1.1.1 that add supports for the upcoming Python 3.8 release among other things. joblib/joblib#878
Fix semaphore availability checker to avoid spawning resource trackers on module import. joblib/joblib#893
Fix the oversubscription protection to only protect against nested Parallel calls. This allows joblib to be run in background threads. joblib/joblib#934
Fix ValueError (negative dimensions) when pickling large numpy arrays on Windows. joblib/joblib#920
Upgrade to loky 2.6.0 that add supports for the setting environment variables in child before loading any module. joblib/joblib#940
Fix the oversubscription protection for native libraries using threadpools (OpenBLAS, MKL, Blis and OpenMP runtimes). The maximal number of threads is can now be set in children using the inner_max_num_threads in parallel_backend. It defaults to cpu_count() // n_jobs.
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