Joblib: running Python functions as pipeline jobs

Introduction

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 data and has specific optimizations for numpy arrays. It is BSD-licensed.

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 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:

    >>> import numpy as np
    >>> from joblib import Memory
    >>> mem = Memory(cachedir='/tmp/joblib')
    >>> import numpy as np
    >>> a = np.vander(np.arange(3)).astype(np.float)
    >>> square = mem.cache(np.square)
    >>> b = square(a)                                   
    ________________________________________________________________________________
    [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 ).

Module reference

Memory A context object for caching a function’s return value each time it is called with the same input arguments.
Parallel Helper class for readable parallel mapping.
dump (value, filename[, compress, cache_size]) Fast persistence of an arbitrary Python object into a files, with dedicated storage for numpy arrays.
load (filename[, mmap_mode]) Reconstruct a Python object from a file persisted with joblib.dump.
hash (obj[, hash_name, coerce_mmap]) Quick calculation of a hash to identify uniquely Python objects containing numpy arrays.

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