serialize all of python
dill extends python’s pickle module for serializing and de-serializing python objects to the majority of the built-in python types. Serialization is the process of converting an object to a byte stream, and the inverse of which is converting a byte stream back to on python object hierarchy.
dill provides the user the same interface as the pickle module, and also includes some additional features. In addition to pickling python objects, dill provides the ability to save the state of an interpreter session in a single command. Hence, it would be feasable to save a interpreter session, close the interpreter, ship the pickled file to another computer, open a new interpreter, unpickle the session and thus continue from the ‘saved’ state of the original interpreter session.
dill can be used to store python objects to a file, but the primary usage is to send python objects across the network as a byte stream. dill is quite flexible, and allows arbitrary user defined classes and functions to be serialized. Thus dill is not intended to be secure against erroneously or maliciously constructed data. It is left to the user to decide whether the data they unpickle is from a trustworthy source.
dill is part of pathos, a python framework for heterogeneous computing. dill is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of known issues is maintained at http://trac.mystic.cacr.caltech.edu/project/pathos/query.html, with a public ticket list at https://github.com/uqfoundation/dill/issues.
dill can pickle the following standard types:
- none, type, bool, int, long, float, complex, str, unicode,
- tuple, list, dict, file, buffer, builtin,
- both old and new style classes,
- instances of old and new style classes,
- set, frozenset, array, functions, exceptions
dill can also pickle more ‘exotic’ standard types:
- functions with yields, nested functions, lambdas,
- cell, method, unboundmethod, module, code, methodwrapper,
- dictproxy, methoddescriptor, getsetdescriptor, memberdescriptor,
- wrapperdescriptor, xrange, slice,
- notimplemented, ellipsis, quit
dill cannot yet pickle these standard types:
- frame, generator, traceback
dill also provides the capability to:
- save and load python interpreter sessions
- save and extract the source code from functions and classes
- interactively diagnose pickling errors
This documentation is for version dill-0.3.0.
The latest released version of dill is available from:
dill is distributed under a 3-clause BSD license.
>>> import dill >>> print (dill.license())
You can get the latest development version with all the shiny new features at:
If you have a new contribution, please submit a pull request.
dill is packaged to install from source, so you must download the tarball, unzip, and run the installer:
[download] $ tar -xvzf dill-0.3.0.tar.gz $ cd dill-0.3.0 $ python setup py build $ python setup py install
You will be warned of any missing dependencies and/or settings after you run the “build” step above.
Alternately, dill can be installed with pip or easy_install:
$ pip install dill
- python, version >= 2.6 or version >= 3.1, or pypy
- setuptools, version >= 0.6
- pyreadline, version >= 1.7.1 (on windows)
- objgraph, version >= 1.7.2
Probably the best way to get started is to look at the documentation at http://dill.rtfd.io. Also see dill.tests for a set of scripts that demonstrate how dill can serialize different python objects. You can run the test suite with python -m dill.tests. The contents of any pickle file can be examined with undill. As dill conforms to the pickle interface, the examples and documentation found at http://docs.python.org/library/pickle.html also apply to dill if one will import dill as pickle. 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 dill in your work, please send an email (to mmckerns at uqfoundation dot org).
If you use dill to do research that leads to publication, we ask that you acknowledge use of dill by citing the following in your publication:
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, "Building a framework for predictive science", Proceedings of the 10th Python in Science Conference, 2011; http://arxiv.org/pdf/1202.1056 Michael McKerns and Michael Aivazis, "pathos: a framework for heterogeneous computing", 2010- ; http://trac.mystic.cacr.caltech.edu/project/pathos
Please see http://trac.mystic.cacr.caltech.edu/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.
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