- Enable tests

- specfile:
* remove devel requirement
- update to version 0.12.1:
* Make sure that any exception triggered when serializing jobs in
the queue will be wrapped as a PicklingError as in past versions
of joblib.
* Fix kwonlydefaults key error in filter_args (#715)
- changes from version 0.12:
* Implement the 'loky' backend with @ogrisel. This backend relies on
a robust implementation of concurrent.futures.ProcessPoolExecutor
with spawned processes that can be reused accross the Parallel
calls. This fixes the bad interation with third paty libraries
relying on thread pools, described in
https://pythonhosted.org/joblib/parallel.html#bad-interaction-of-multiprocessing-and-third-party-libraries
* Limit the number of threads used in worker processes by
C-libraries that relies on threadpools. This functionality works
for MKL, OpenBLAS, OpenMP and Accelerated.
* Prevent numpy arrays with the same shape and data from hashing to
the same memmap, to prevent jobs with preallocated arrays from
writing over each other.
* Reduce overhead of automatic memmap by removing the need to hash
the array.
* Make Memory.cache robust to PermissionError (errno 13) under
Windows when run in combination with Parallel.
* The automatic array memory mapping feature of Parallel does no
longer use /dev/shm if it is too small (less than 2 GB). In
particular in docker containers /dev/shm is only 64 MB by default
which would cause frequent failures when running joblib in Docker