python-joblib-0.16.0-bp153.2.3.1

- Add CVE-2022-21797.patch (bsc#1204232)

- disable test_hash_numpy_noncontiguous,
  test_hashes_are_different_between_c_and_fortran_contiguous_arrays,
  test_hashes_stay_the_same_with_numpy_objects,
  test_non_contiguous_array_pickling [bsc#1177209]

- disable test_nested_loop_error_in_grandchild_resource_tracker_silent
  [bsc#1177209]

- disable yet another tests [bsc#1177209]

- Disable tests failing often in OBS:
  * joblib-disable-unrelialble-tests.patch

- New disable_test_on_big_endian.patch as per upstream issue
  https://github.com/joblib/joblib/issues/279

- update to 0.16.0
  - Fix a problem in the constructors of of Parallel backends classes that
    inherit from the `AutoBatchingMixin` that prevented the dask backend to
    properly batch short tasks.
    https://github.com/joblib/joblib/pull/1062
  - Fix a problem in the way the joblib dask backend batches calls that would
    badly interact with the dask callable pickling cache and lead to wrong
    results or errors.
    https://github.com/joblib/joblib/pull/1055
  - Prevent a dask.distributed bug from surfacing in joblib's dask backend
    during nested Parallel calls (due to joblib's auto-scattering feature)
    https://github.com/joblib/joblib/pull/1061
  - Workaround for a race condition after Parallel calls with the dask backend
    that would cause low level warnings from asyncio coroutines:
    https://github.com/joblib/joblib/pull/1078

- update to 0.15.1:
  - Make joblib work on Python 3 installation that do not ship with the lzma
    package in their standard library.
  - Drop support for Python 2 and Python 3.5. All objects in
    ``joblib.my_exceptions`` and ``joblib.format_stack`` are now deprecated and
    will be removed in joblib 0.16. Note that no deprecation warning will be
    raised for these objects Python < 3.7.
    https://github.com/joblib/joblib/pull/1018
  - Fix many bugs related to the temporary files and folder generated when
    automatically memory mapping large numpy arrays for efficient inter-process
    communication. In particular, this would cause `PermissionError` exceptions
    to be raised under Windows and large leaked files in `/dev/shm` under Linux
    in case of crash.
    https://github.com/joblib/joblib/pull/966
  - Make the dask backend collect results as soon as they complete
    leading to a performance improvement:
    https://github.com/joblib/joblib/pull/1025
  - Fix the number of jobs reported by ``effective_n_jobs`` when ``n_jobs=None``
    called in a parallel backend context.
    https://github.com/joblib/joblib/pull/985
  - Upgraded vendored cloupickle to 1.4.1 and loky to 2.8.0. This allows for
    Parallel calls of dynamically defined functions with type annotations
    in particular.

- version update to 0.14.1
  - Configure the loky workers' environment to mitigate oversubsription with
    nested multi-threaded code in the following case:
  - allow for a suitable number of threads for numba (``NUMBA_NUM_THREADS``);
  - enable Interprocess Communication for scheduler coordination when the
    nested code uses Threading Building Blocks (TBB) (``ENABLE_IPC=1``)
    https://github.com/joblib/joblib/pull/951
  - Fix a regression where the loky backend was not reusing previously
    spawned workers.
    https://github.com/joblib/joblib/pull/968
  - Revert https://github.com/joblib/joblib/pull/847 to avoid using
    `pkg_resources` that introduced a performance regression under Windows:
    https://github.com/joblib/joblib/issues/965
  - 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``.
    https://github.com/joblib/joblib/pull/940
- deleted patches
  - numpy16.patch (upstreamed)

- Switch to %pytest
- Add patch to work well with new numpy:
  * numpy16.patch

- Update to 0.13.2:
  * Upgrade to cloudpickle 0.8.0
  * Add a non-regression test related to joblib issues #836 and #833, reporting that cloudpickle versions between 0.5.4 and 0.7 introduced a bug where global variables changes in a parent process between two calls to joblib.Parallel would not be propagated into the workers

- Remove no longer necessary pytest argument
  - k 'not test_no_blas_crash_or_freeze_with_subprocesses'

- Update to Release 0.13.1:
  * Memory now accepts pathlib.Path objects as ``location``
    parameter. Also, a warning is raised if the returned backend
    is None while ``location`` is not None.
  * Make ``Parallel`` raise an informative ``RuntimeError`` when
    the active parallel backend has zero worker.
  * Make the ``DaskDistributedBackend`` wait for workers before
    trying to schedule work. This is useful in particular when
    the workers are provisionned dynamically but provisionning is
    not immediate (for instance using Kubernetes, Yarn or an HPC
    job queue).

- update to Release 0.13.0
  * Include loky 2.4.2 with default serialization with ``cloudpickle``.
    This can be tweaked with the environment variable ``LOKY_PICKLER``.
  * Fix nested backend in SequentialBackend to avoid changing the default
    backend to Sequential. (#792)
  * Fix nested_backend behavior to avoid setting the default number of
    workers to -1 when the backend is not dask. (#784)
- Update to Release 0.12.5
  * Include loky 2.3.1 with better error reporting when a worker is
    abruptly terminated. Also fixes spurious debug output.
  * Include cloudpickle 0.5.6. Fix a bug with the handling of global
    variables by locally defined functions.
- Update to Release 0.12.4
  * Include loky 2.3.0 with many bugfixes, notably w.r.t. when setting
    non-default multiprocessing contexts. Also include improvement on
    memory management of long running worker processes and fixed issues
    when using the loky backend under PyPy.
  * Raises a more explicit exception when a corrupted MemorizedResult is loaded.
  * Loading a corrupted cached file with mmap mode enabled would
    recompute the results and return them without memmory mapping.
- Update to Release 0.12.3
  * Fix joblib import setting the global start_method for multiprocessing.
  * Fix MemorizedResult not picklable (#747).
  * Fix Memory, MemorizedFunc and MemorizedResult round-trip pickling +
    unpickling (#746).
  * Fixed a regression in Memory when positional arguments are called as
    kwargs several times with different values (#751).
  * Integration of loky 2.2.2 that fixes issues with the selection of the
    default start method and improve the reporting when calling functions
    with arguments that raise an exception when unpickling.
  * Prevent MemorizedFunc.call_and_shelve from loading cached results to
    RAM when not necessary. Results in big performance improvements
- Update to Release 0.12.2
  * Integrate loky 2.2.0 to fix regression with unpicklable arguments and
    functions reported by users (#723, #643).
  * Loky 2.2.0 also provides a protection against memory leaks long running
    applications when psutil is installed (reported as #721).
  * Joblib now includes the code for the dask backend which has been updated
    to properly handle nested parallelism and data scattering at the same
    time (#722).
  * Restored some private API attribute and arguments
    (`MemorizedResult.argument_hash` and `BatchedCalls.__init__`'s
    `pickle_cache`) for backward compat. (#716, #732).
  * Fix a deprecation warning message (for `Memory`'s `cachedir`) (#720).

- Disable blas test as it is very flaky outside of x86_64

- Use noun phrase in summary.

- 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
    containers.
  * Make it possible to hint for thread-based parallelism with
    prefer='threads' or enforce shared-memory semantics with
    require='sharedmem'.
  * Rely on the built-in exception nesting system of Python 3 to
    preserve traceback information when an exception is raised on a
    remote worker process. This avoid verbose and redundant exception
    reports under Python 3.
  * Preserve exception type information when doing nested Parallel
    calls instead of mapping the exception to the generic
    JoblibException type.
  * Introduce the concept of 'store' and refactor the Memory internal
    storage implementation to make it accept extra store backends for
    caching results. backend and backend_options are the new options
    added to Memory to specify and configure a store backend.
  * Add the register_store_backend function to extend the store
    backend used by default with Memory. This default store backend is
    named 'local' and corresponds to the local filesystem.
  * The store backend API is experimental and thus is subject to
    change in the future without deprecation.
  * The cachedir parameter of Memory is now marked as deprecated, use
    location instead.
  * Add support for LZ4 compression if lz4 package is installed.
  * Add register_compressor function for extending available
    compressors.
  * Allow passing a string to compress parameter in dump funtion. This
    string should correspond to the compressor used (e.g. zlib, gzip,
    lz4, etc). The default compression level is used in this case.
  * Allow parallel_backend to be used globally instead of only as a
    context manager. Support lazy registration of external parallel
    backends

- Ensure neutrality of description.

- Implement single-spec version.
- Run tests.
- Fix source URL.
- Update to version 0.11.
  * For a full changelog please see:
    https://github.com/joblib/joblib/blob/0.11/CHANGES.rst

- Disable non-functional documentation

- Update to version 0.7.1
  + MISC: capture meaningless argument (n_jobs=0) in Parallel
  + ENH Handles tuples, sets and Python 3's dict_keys type the same as
    lists. in pre_dispatch
  + ENH: fix function caching for IPython

- Require python-setuptools instead of distribute (upstreams merged)

- Update to version 0.7.0d
  * No changelog available
- Clean the specfile and remove .buildinfo file

- Add Group field in doc package for SLE 11

- Initial version