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venv/Lib/site-packages/joblib/__init__.py

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+"""Joblib is a set of tools to provide **lightweight pipelining in
+Python**. In particular:
+
+1. transparent disk-caching of functions and lazy re-evaluation
+   (memoize pattern)
+
+2. easy simple parallel computing
+
+Joblib is optimized to be **fast** and **robust** on large
+data in particular 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
+    ==================== ===============================================
+
+
+Vision
+--------
+
+The vision is to provide tools to easily achieve better performance and
+reproducibility when working with long running jobs.
+
+ *  **Avoid computing the same thing twice**: code is often rerun again and
+    again, for instance when prototyping computational-heavy jobs (as in
+    scientific development), but hand-crafted solutions to alleviate this
+    issue are error-prone and often lead to unreproducible results.
+
+ *  **Persist to disk transparently**: efficiently persisting
+    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).
+
+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
+      >>> 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
+
+2) **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]
+
+
+3) **Fast compressed Persistence**: a replacement for pickle to work
+   efficiently on Python objects containing large data (
+   *joblib.dump* & *joblib.load* ).
+
+..
+    >>> import shutil ; shutil.rmtree(cachedir)
+
+"""
+
+# PEP0440 compatible formatted version, see:
+# https://www.python.org/dev/peps/pep-0440/
+#
+# Generic release markers:
+# X.Y
+# X.Y.Z # For bugfix releases
+#
+# Admissible pre-release markers:
+# X.YaN # Alpha release
+# X.YbN # Beta release
+# X.YrcN # Release Candidate
+# X.Y # Final release
+#
+# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
+# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
+#
+__version__ = '0.17.0'
+
+
+import os
+from .memory import Memory, MemorizedResult, register_store_backend
+from .logger import PrintTime
+from .logger import Logger
+from .hashing import hash
+from .numpy_pickle import dump
+from .numpy_pickle import load
+from .compressor import register_compressor
+from .parallel import Parallel
+from .parallel import delayed
+from .parallel import cpu_count
+from .parallel import register_parallel_backend
+from .parallel import parallel_backend
+from .parallel import effective_n_jobs
+
+from .externals.loky import wrap_non_picklable_objects
+
+
+__all__ = ['Memory', 'MemorizedResult', 'PrintTime', 'Logger', 'hash', 'dump',
+           'load', 'Parallel', 'delayed', 'cpu_count', 'effective_n_jobs',
+           'register_parallel_backend', 'parallel_backend',
+           'register_store_backend', 'register_compressor',
+           'wrap_non_picklable_objects']
+
+
+# Workaround issue discovered in intel-openmp 2019.5:
+# https://github.com/ContinuumIO/anaconda-issues/issues/11294
+os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")