prevent overflow when pickling large numpy arrays #920
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netbsd-srcmastr
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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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Fixes #859
It is hard to test as it requires CI engines with more than 16GB of RAM.