Log message:
py-joblib: updated to 1.0.1

1.0.1
dask: avoid redundant scattering of large arguments to make a more efficient
use of the network resources and avoid crashing dask with “OSError: [Errno 55]
No buffer space available” or “ConnectionResetError: [Errno 104] connection
reset by peer”.

Log message:
py-joblib: updated to 1.0.0

1.0.0
Make joblib.hash and joblib.Memory caching system compatible with numpy >= \ 
1.20.0. Also make it explicit in the documentation that users should now expect \ 
to have their joblib.Memory cache invalidated when either joblib or a third \ 
party library involved in the cached values definition is upgraded. In \ 
particular, users updating joblib to a release that includes this fix will see \ 
their previous cache invalidated if they contained reference to numpy objects.
Remove deprecated check_pickle argument in delayed.

Log message:
py-joblib: updated to 0.17.0

Release 0.17.0

- Fix a spurious invalidation of `Memory.cache`'d functions called with
  `Parallel` under Jupyter or IPython.

- Bump vendored loky to 2.9.0 and cloudpickle to 1.6.0. In particular
  this fixes a problem to add compat for Python 3.9.

Log message:
py-joblib: updated to 0.16.0

Release 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.

- 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.

- Prevent a dask.distributed bug from surfacing in joblib's dask backend
  during nested Parallel calls (due to joblib's auto-scattering feature)

- Workaround for a race condition after Parallel calls with the dask backend
  that would cause low level warnings from asyncio coroutines:

Log message:
py-joblib: updated 0.15.1

Release 0.15.1
- Make joblib work on Python 3 installation that do not ship with the lzma
  package in their standard library.

Log message:
py-joblib: updated to 0.15.0

Release 0.15.0

- 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.

- 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.

- Make the dask backend collect results as soon as they complete
  leading to a performance improvement:

- Fix the number of jobs reported by ``effective_n_jobs`` when ``n_jobs=None``
  called in a parallel backend context.

- 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.

Log message:
py-joblib: updated to 0.14.1

Release 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

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.

Log message:
py-joblib: updated to 0.13.2

0.13.2:
Add a non-regression test, 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

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).

Log message:
py-joblib: updated to 0.13.0

Release 0.13.0:
   Fix nested backend in SequentialBackend to avoid changing the default
   backend to Sequential.

    Fix nested_backend behavior to avoid setting the default number of
    workers to -1 when the backend is not dask.

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.

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.

Release 0.12.3

    Fix joblib import setting the global start_method for multiprocessing.

    Fix MemorizedResult not picklable.

    Fix Memory, MemorizedFunc and MemorizedResult round-trip pickling +
    unpickling.

    Fixed a regression in Memory when positional arguments are called as
    kwargs several times with different values.

    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