119 lines
4.4 KiB
Plaintext
119 lines
4.4 KiB
Plaintext
Metadata-Version: 2.1
|
|
Name: joblib
|
|
Version: 0.14.1
|
|
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)
|
|
|
|
|
|
|