ó ‡ˆ\c@s2dZdZddlmZmZmZddlmZddlmZddl m Z ddl m Z ddl m Z dd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZdddddddddddddddd gZd!S("sß 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** in particular on large data and has specific optimizations for `numpy` arrays. It is **BSD-licensed**. ==================== =============================================== **Documentation:** https://joblib.readthedocs.io **Download:** http://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 twice the same thing**: code is rerun over an over, for instance when prototyping computational-heavy jobs (as in scientific development), but hand-crafted solution to alleviate this issue is error-prone and often leads to unreproducible results * **Persist to disk transparently**: persisting in an efficient way 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 sklearn.externals.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 sklearn.externals.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) s0.13.0i(tMemorytMemorizedResulttregister_store_backend(t PrintTime(tLogger(thash(tdump(tload(tregister_compressor(tParallel(tdelayed(t cpu_count(tregister_parallel_backend(tparallel_backend(teffective_n_jobs(twrap_non_picklable_objectsRRRRRRRR R R RR R RRRN(t__doc__t __version__tmemoryRRRtloggerRRthashingRt numpy_pickleRRt compressorRtparallelR R R R R Rtexternals.lokyRt__all__(((s@lib/python2.7/site-packages/sklearn/externals/joblib/__init__.pyt[s(