./devel/py-joblib, Set of tools to provide lightweight pipelining

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Branch: CURRENT, Version: 0.13.2, Package name: py27-joblib-0.13.2, Maintainer: jihbed.research

Joblib is a set of tools to provide lightweight pipelining in Python.
In particular, joblib offers transparent disk-caching of the output
values and lazy re-evaluation (memoize pattern), easy simple parallel
computing, and logging and tracing of the execution. Joblib is
optimized to be fast and robust in particular on large data and has
specific optimizations for numpy arrays.


Required to run:
[lang/python27]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: c8aa6b12af1a072e4477623ed488de668ef7c3bf
RMD160: 64116ec0e9f568c95f1b16cb1c6a1138ad1b35c4
Filesize: 280.701 KB

Version history: (Expand)


CVS history: (Expand)


   2019-02-14 10:01:45 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
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).
   2018-11-30 13:20:44 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
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
   2018-08-14 19:39:32 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
Log message:
py-joblib: updated to 0.12.2

Release 0.12.2:
Integrate loky 2.2.0 to fix regression with unpicklable arguments and functions \ 
reported by users.
Loky 2.2.0 also provides a protection against memory leaks long running \ 
applications when psutil is installed.
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.
Restored some private API attribute and arguments (MemorizedResult.argument_hash \ 
and BatchedCalls.__init__'s pickle_cache) for backward compat.
Fix a deprecation warning message (for Memory's cachedir).

Release 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
   2018-07-06 05:13:36 by Min Sik Kim | Files touched by this commit (4) | Package updated
Log message:
devel/py-joblib: Import version 0.12.0

Joblib is a set of tools to provide lightweight pipelining in Python.
In particular, joblib offers transparent disk-caching of the output
values and lazy re-evaluation (memoize pattern), easy simple parallel
computing, and logging and tracing of the execution.  Joblib is
optimized to be fast and robust in particular on large data and has
specific optimizations for numpy arrays.

Packaged by Kamel Ibn Aziz Derouiche for pkgsrc-wip and updated by me.