Lightweight pipelining: using Python functions as pipeline jobs.
Joblib is a set of tools to provide lightweight pipelining in Python. In particular:
- transparent disk-caching of functions and lazy re-evaluation (memoize pattern)
- 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: https://pypi.python.org/pypi/joblib#downloads Source code: https://github.com/joblib/joblib Report issues: https://github.com/joblib/joblib/issues
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).
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
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]
Fast compressed Persistence: a replacement for pickle to work efficiently on Python objects containing large data ( joblib.dump & joblib.load ).
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