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Luke Dixon
2020-05-08 14:39:22 +01:00
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pip

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BSD 3-Clause License
Copyright (c) 2008-2016, The joblib developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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Metadata-Version: 2.1
Name: joblib
Version: 0.14.0
Summary: Lightweight pipelining: using Python functions as pipeline jobs.
Home-page: https://joblib.readthedocs.io
Author: Gael Varoquaux
Author-email: gael.varoquaux@normalesup.org
License: BSD
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Classifier: Topic :: Software Development :: Libraries
Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular:
1. transparent disk-caching of functions and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
Joblib is optimized to be **fast** and **robust** on large
data in particular and has specific optimizations for `numpy` arrays. It is
**BSD-licensed**.
==================== ===============================================
**Documentation:** https://joblib.readthedocs.io
**Download:** https://pypi.python.org/pypi/joblib#downloads
**Source code:** https://github.com/joblib/joblib
**Report issues:** https://github.com/joblib/joblib/issues
==================== ===============================================
Vision
--------
The vision is to provide tools to easily achieve better performance and
reproducibility when working with long running jobs.
* **Avoid computing the same thing twice**: code is often rerun again and
again, for instance when prototyping computational-heavy jobs (as in
scientific development), but hand-crafted solutions to alleviate this
issue are error-prone and often lead to unreproducible results.
* **Persist to disk transparently**: efficiently persisting
arbitrary objects containing large data is hard. Using
joblib's caching mechanism avoids hand-written persistence and
implicitly links the file on disk to the execution context of
the original Python object. As a result, joblib's persistence is
good for resuming an application status or computational job, eg
after a crash.
Joblib addresses these problems while **leaving your code and your flow
control as unmodified as possible** (no framework, no new paradigms).
Main features
------------------
1) **Transparent and fast disk-caching of output value:** a memoize or
make-like functionality for Python functions that works well for
arbitrary Python objects, including very large numpy arrays. Separate
persistence and flow-execution logic from domain logic or algorithmic
code by writing the operations as a set of steps with well-defined
inputs and outputs: Python functions. Joblib can save their
computation to disk and rerun it only if necessary::
>>> from joblib import Memory
>>> cachedir = 'your_cache_dir_goes_here'
>>> mem = Memory(cachedir)
>>> import numpy as np
>>> a = np.vander(np.arange(3)).astype(np.float)
>>> square = mem.cache(np.square)
>>> b = square(a) # doctest: +ELLIPSIS
________________________________________________________________________________
[Memory] Calling square...
square(array([[0., 0., 1.],
[1., 1., 1.],
[4., 2., 1.]]))
___________________________________________________________square - 0...s, 0.0min
>>> c = square(a)
>>> # The above call did not trigger an evaluation
2) **Embarrassingly parallel helper:** to make it easy to write readable
parallel code and debug it quickly::
>>> from joblib import Parallel, delayed
>>> from math import sqrt
>>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
3) **Fast compressed Persistence**: a replacement for pickle to work
efficiently on Python objects containing large data (
*joblib.dump* & *joblib.load* ).
..
>>> import shutil ; shutil.rmtree(cachedir)

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