dill

serialize all of python

About Dill

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 funcitons 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, with a public ticket list at https://github.com/uqfoundation/dill/issues.

Major Features

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

Current Release

The latest stable release of dill is available from:: http://trac.mystic.cacr.caltech.edu/project/pathos

or:: https://github.com/uqfoundation/dill/releases

or also:: https://pypi.python.org/pypi/dill

Dill is distributed under a 3-clause BSD license.

Development Version

You can get the latest development version with all the shiny new features at:: https://github.com/uqfoundation

Feel free to fork the github mirror of our svn trunk. If you have a new contribution, please submit a pull request.

More Information

Probably the best way to get started is to look at the tests that are provide within dill. See dill.tests for a set of scripts that demonstrate dill's ability to serialize different python objects. Since dill conforms to the 'pickle' interface, the examples and documentation 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 also feel free to submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns).

Dill is an active research tool. There are a growing number of publications and presentations that discuss real-world examples and new features of dill in greater detail than presented in the user's guide. If you would like to share how you use dill in your work, please post a link or send an email (to mmckerns at caltech dot edu).

Citation

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://dev.danse.us/trac/pathos

Please see http://trac.mystic.cacr.caltech.edu/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.