Log Message:
py-joblib: updated to 0.14.0

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). https://github.com/joblib/joblib/pull/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). \ 
https://github.com/joblib/joblib/pull/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. \ 
https://github.com/joblib/joblib/pull/879
Upgrade to cloudpickle 1.1.1 that add supports for the upcoming Python 3.8 \ 
release among other things. https://github.com/joblib/joblib/pull/878
Fix semaphore availability checker to avoid spawning resource trackers on module \ 
import. https://github.com/joblib/joblib/pull/893
Fix the oversubscription protection to only protect against nested Parallel \ 
calls. This allows joblib to be run in background threads. \ 
https://github.com/joblib/joblib/pull/934
Fix ValueError (negative dimensions) when pickling large numpy arrays on \ 
Windows. https://github.com/joblib/joblib/pull/920
Upgrade to loky 2.6.0 that add supports for the setting environment variables in \ 
child before loading any module. https://github.com/joblib/joblib/pull/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.