Joblib 0.5.7

Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers:<br /><br /> 1. transparent disk-caching of the output values and lazy re-evaluation (memoize pattern)<br /> 2. easy simple parallel computing<br /> 3. logging and tracing of the execution<br /><br />Joblib is optimized to be fast and robust in particular on large data and has specific optimizations for numpy arrays.<br /><br />Main features :<br /><br /> 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:<br /><br /> >>> from joblib import Memory<br /> >>> mem = Memory(cachedir='/tmp/joblib')<br /> >>> import numpy as np<br /> >>> a = np.vander(np.arange(3))<br /> >>> square = mem.cache(np.square)<br /> >>> b = square(a)<br /> ________________________________________________________________________________<br /> [Memory] Calling square...<br /> square(array([[0, 0, 1],<br /> [1, 1, 1],<br /> [4, 2, 1]]))<br /> ___________________________________________________________square - 0.0s, 0.0min<br /><br /> >>> c = square(a)<br /> >>> # The above call did not trigger an evaluation<br /><br /> 2. Embarrassingly parallel helper: to make is easy to write readable parallel code and debug it quickly:<br /><br /> >>> from joblib import Parallel, delayed<br /> >>> from math import sqrt<br /> >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))<br /> [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]<br /><br /> 3. Logging/tracing: The different functionalities will progressively acquire better logging mechanism to help track what has been ran, and capture I/O easily. In addition, Joblib will provide a few I/O primitives, to easily define define logging and display streams, and provide a way of compiling a report. We want to be able to quickly inspect what has been run.

Requirements:	No special requirements
Platforms:	*nix, Linux
Keyword:	Diskcaching,  Easily,  Functions,  Gtgtgt,  Import,  Joblib,  Large,  Logging,  Logic,  Memoize,  Memory,  Numpy,  Parallel,  Provide,  Python,  Quickly,  Square,  Squarea
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