joblib 0.4.1.dev

Lightweight pipelining: using Python functions as pipeline jobs.

Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers:

1. transparent disk-caching of the output values and lazy re-evaluation (memoize pattern)

2. easy simple parallel computing

3. logging and tracing of the execution

Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays.

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• The latest user documentation for joblib can be found on http://packages.python.org/joblib/

• The latest packages can be downloaded from http://pypi.python.org/pypi/joblib

• Instructions for developpers can be found at: http://github.com/joblib/joblib

joblib is BSD-licensed.

Vision

Joblib came out of long-running data-analysis Python scripts. The long term vision is to provide tools for scientists to achieve better reproducibility when running jobs, without changing the way numerical code looks like. However, Joblib can also be used to provide a light-weight make replacement.

The main problems identified are:

1. Lazy evaluation: People need to rerun over and over the same script as it is tuned, but end up commenting out steps, or uncommenting steps, as they are needed, as they take long to run.

2. Persistence: It is difficult to persist in an efficient way arbitrary objects containing large numpy arrays. In addition, hand-written persistence to disk does not link easily the file on disk to the corresponding Python object it was persists from in the script. This leads to people not a having a hard time resuming the job, eg after a crash and persistence getting in the way of work.

The approach taken by Joblib to address these problems is not to build a heavy framework and coerce user into using it (e.g. with an explicit pipeline). It strives to leave your code and your flow control as unmodified as possible.

Current features

1. Transparent and fast disk-caching of output value: a make-like functionality for Python functions that works well with large numpy arrays. The goal is to separate operations in a set of steps with well-defined inputs and outputs, that are saved and reran only if necessary, by using standard Python functions:

   >>> from joblib import Memory
   >>> mem = Memory(cachedir='/tmp/joblib')
   >>> import numpy as np
   >>> a = np.vander(np.arange(3))
   >>> square = mem.cache(np.square)
   >>> b = square(a)
   ________________________________________________________________________________
   [Memory] Calling square...
   square(array([[0, 0, 1],
          [1, 1, 1],
          [4, 2, 1]]))
   ___________________________________________________________square - 0.0s, 0.0min
   
   >>> c = square(a)
   >>> # The above call did not trigger an evaluation

2. Embarrassingly parallel helper: to make is 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. 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.

Contributing

The code is hosted on github. It is easy to clone the project and experiment with making your own modifications. If you need extra features, don’t hesitate to contribute them.