149 lines
5.0 KiB
Python
149 lines
5.0 KiB
Python
"""Joblib is a set of tools to provide **lightweight pipelining in
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Python**. In particular:
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1. transparent disk-caching of functions and lazy re-evaluation
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(memoize pattern)
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2. easy simple parallel computing
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Joblib is optimized to be **fast** and **robust** on large
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data in particular and has specific optimizations for `numpy` arrays. It is
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**BSD-licensed**.
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==================== ===============================================
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**Documentation:** https://joblib.readthedocs.io
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**Download:** https://pypi.python.org/pypi/joblib#downloads
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**Source code:** https://github.com/joblib/joblib
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**Report issues:** https://github.com/joblib/joblib/issues
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==================== ===============================================
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Vision
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--------
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The vision is to provide tools to easily achieve better performance and
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reproducibility when working with long running jobs.
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* **Avoid computing the same thing twice**: code is often rerun again and
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again, for instance when prototyping computational-heavy jobs (as in
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scientific development), but hand-crafted solutions to alleviate this
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issue are error-prone and often lead to unreproducible results.
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* **Persist to disk transparently**: efficiently persisting
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arbitrary objects containing large data is hard. Using
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joblib's caching mechanism avoids hand-written persistence and
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implicitly links the file on disk to the execution context of
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the original Python object. As a result, joblib's persistence is
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good for resuming an application status or computational job, eg
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after a crash.
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Joblib addresses these problems while **leaving your code and your flow
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control as unmodified as possible** (no framework, no new paradigms).
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Main features
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------------------
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1) **Transparent and fast disk-caching of output value:** a memoize or
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make-like functionality for Python functions that works well for
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arbitrary Python objects, including very large numpy arrays. Separate
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persistence and flow-execution logic from domain logic or algorithmic
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code by writing the operations as a set of steps with well-defined
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inputs and outputs: Python functions. Joblib can save their
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computation to disk and rerun it only if necessary::
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>>> from joblib import Memory
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>>> cachedir = 'your_cache_dir_goes_here'
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>>> mem = Memory(cachedir)
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>>> import numpy as np
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>>> a = np.vander(np.arange(3)).astype(float)
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>>> square = mem.cache(np.square)
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>>> b = square(a) # doctest: +ELLIPSIS
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______________________________________________________________________...
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[Memory] Calling square...
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square(array([[0., 0., 1.],
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[1., 1., 1.],
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[4., 2., 1.]]))
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_________________________________________________...square - ...s, 0.0min
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>>> c = square(a)
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>>> # The above call did not trigger an evaluation
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2) **Embarrassingly parallel helper:** to make it easy to write readable
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parallel code and debug it quickly::
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>>> from joblib import Parallel, delayed
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>>> from math import sqrt
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>>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
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[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
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3) **Fast compressed Persistence**: a replacement for pickle to work
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efficiently on Python objects containing large data (
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*joblib.dump* & *joblib.load* ).
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..
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>>> import shutil ; shutil.rmtree(cachedir)
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"""
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# PEP0440 compatible formatted version, see:
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# https://www.python.org/dev/peps/pep-0440/
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#
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# Generic release markers:
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# X.Y
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# X.Y.Z # For bugfix releases
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#
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# Admissible pre-release markers:
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# X.YaN # Alpha release
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# X.YbN # Beta release
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# X.YrcN # Release Candidate
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# X.Y # Final release
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#
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# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
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# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
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#
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__version__ = '1.4.2'
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import os
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from .memory import Memory
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from .memory import MemorizedResult
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from .memory import register_store_backend
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from .memory import expires_after
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from .logger import PrintTime
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from .logger import Logger
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from .hashing import hash
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from .numpy_pickle import dump
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from .numpy_pickle import load
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from .compressor import register_compressor
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from .parallel import Parallel
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from .parallel import delayed
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from .parallel import cpu_count
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from .parallel import register_parallel_backend
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from .parallel import parallel_backend
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from .parallel import parallel_config
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from .parallel import effective_n_jobs
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from ._cloudpickle_wrapper import wrap_non_picklable_objects
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__all__ = ['Memory', 'MemorizedResult', 'PrintTime', 'Logger', 'hash', 'dump',
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'load', 'Parallel', 'delayed', 'cpu_count', 'effective_n_jobs',
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'register_parallel_backend', 'parallel_backend', 'expires_after',
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'register_store_backend', 'register_compressor',
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'wrap_non_picklable_objects', 'parallel_config']
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# Workaround issue discovered in intel-openmp 2019.5:
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# https://github.com/ContinuumIO/anaconda-issues/issues/11294
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os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
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