Vision

The vision is to provide tools to easily achieve better performance and reproducibility when working with long running jobs. In addition, Joblib can also be used to provide a light-weight make replacement or caching solution.

• 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 aleviate 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 implicitely 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 strives to address 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
   >>> mem = Memory(cachedir='/tmp/joblib')
   >>> import numpy as np
   >>> a = np.vander(np.arange(3))
   >>> 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 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.

4. Fast compressed Persistence: a replacement for pickle to work efficiently on Python objects containing large data ( joblib.dump & joblib.load ).