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klepto
extends Python's lru_cache
to utilize different keymaps and
alternate caching algorithms, such as lfu_cache
and mru_cache
.
While caching is meant for fast access to saved results, klepto
also
has archiving capabilities, for longer-term storage. klepto
uses a
simple dictionary-sytle interface for all caches and archives, and all
caches can be applied to any Python function as a decorator. Keymaps
are algorithms for converting a function's input signature to a unique
dictionary, where the function's results are the dictionary value.
Thus for y = f(x)
, y
will be stored in cache[x]
(e.g. {x:y}
).
klepto
provides both standard and "safe" caching, where "safe" caches
are slower but can recover from hashing errors. klepto
is intended
to be used for distributed and parallel computing, where several of
the keymaps serialize the stored objects. Caches and archives are
intended to be read/write accessible from different threads and
processes. klepto
enables a user to decorate a function, save the
results to a file or database archive, close the interpreter,
start a new session, and reload the function and it's cache.
klepto
is part of pathos
, a Python framework for heterogeneous computing.
klepto
is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/klepto/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
klepto
has standard and "safe" variants of the following:
- ``lfu_cache`` - the least-frequently-used caching algorithm
- ``lru_cache`` - the least-recently-used caching algorithm
- ``mru_cache`` - the most-recently-used caching algorithm
- ``rr_cache`` - the random-replacement caching algorithm
- ``no_cache`` - a dummy caching interface to archiving
- ``inf_cache`` - an infinitely-growing cache
klepto
has the following archive types:
- ``file_archive`` - a dictionary-style interface to a file
- ``dir_archive`` - a dictionary-style interface to a folder of files
- ``sqltable_archive`` - a dictionary-style interface to a sql database table
- ``sql_archive`` - a dictionary-style interface to a sql database
- ``hdfdir_archive`` - a dictionary-style interface to a folder of hdf5 files
- ``hdf_archive`` - a dictionary-style interface to a hdf5 file
- ``dict_archive`` - a dictionary with an archive interface
- ``null_archive`` - a dictionary-style interface to a dummy archive
klepto
provides the following keymaps:
- ``keymap`` - keys are raw Python objects
- ``hashmap`` - keys are a hash for the Python object
- ``stringmap`` - keys are the Python object cast as a string
- ``picklemap`` - keys are the serialized Python object
klepto
also includes a few useful decorators providing:
- simple, shallow, or deep rounding of function arguments
- cryptographic key generation, with masking of selected arguments
The latest released version of klepto
is available from:
https://pypi.org/project/klepto
klepto
is distributed under a 3-clause BSD license.
You can get the latest development version with all the shiny new features at:
https://github.com/uqfoundation
If you have a new contribution, please submit a pull request.
klepto
can be installed with pip
::
$ pip install klepto
To include optional archive backends, such as HDF5 and SQL, in the install::
$ pip install klepto[archives]
To include optional serializers, such as jsonpickle
, in the install::
$ pip install klepto[crypto]
klepto
requires:
- ``python`` (or ``pypy``), **>=3.8**
- ``setuptools``, **>=42**
- ``dill``, **>=0.3.8**
- ``pox``, **>=0.3.4**
Optional requirements:
- ``h5py``, **>=2.8.0**
- ``pandas``, **>=0.17.0**
- ``sqlalchemy``, **>=1.4.0**
- ``jsonpickle``, **>=0.9.6**
- ``cloudpickle``, **>=0.5.2**
Probably the best way to get started is to look at the documentation at
http://klepto.rtfd.io. Also see klepto.tests
for a set of scripts that
test the caching and archiving functionalities in klepto
.
You can run the test suite with python -m klepto.tests
. The
source code is also generally well documented, so further questions may
be resolved by inspecting the code itself. Please feel free to submit
a ticket on github, or ask a question on stackoverflow (@Mike McKerns).
If you would like to share how you use klepto
in your work, please send
an email (to mmckerns at uqfoundation dot org).
If you use klepto
to do research that leads to publication, we ask that you
acknowledge use of klepto
by citing the following in your publication::
Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;
https://uqfoundation.github.io/project/pathos
Please see https://uqfoundation.github.io/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.
FAQs
persistent caching to memory, disk, or database
We found that klepto demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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