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
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
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, 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 version is
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-0.2.7.1.tar.gz $ cd dill-0.2.7.1 $ 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.
dill can be installed with
$ pip install dill
- python2, version >= 2.5 *or* python3, version >= 3.1 *or* pypy - pyreadline, version >= 1.7.1 (on windows)
- setuptools, version >= 0.6 - objgraph, version >= 1.7.2
Probably the best way to get started is to look at the tests that are
dill.tests for a set of scripts that demonstrate
dill can serialize different python objects. Since
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
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 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.