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serialize all of python

Project description

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 a 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 an 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 issues is located at, with a legacy list maintained at

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 released version of dill is available from:

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:

If you have a new contribution, please submit a pull request.


dill can be installed with pip:

$ pip install dill

To optionally include the objgraph diagnostic tool in the install:

$ pip install dill[graph]

For windows users, to optionally install session history tools:

$ pip install dill[readline]


dill requires:

  • python (or pypy), ==2.7 or >=3.7

  • setuptools, >=42

Optional requirements:

  • objgraph, >=1.7.2

  • pyreadline, >=1.7.1 (on windows)

Basic Usage

dill is a drop-in replacement for pickle. Existing code can be updated to allow complete pickling using:

>>> import dill as pickle


>>> from dill import dumps, loads

dumps converts the object to a unique byte string, and loads performs the inverse operation:

>>> squared = lambda x: x**2
>>> loads(dumps(squared))(3)

There are a number of options to control serialization which are provided as keyword arguments to several dill functions:

  • with protocol, the pickle protocol level can be set. This uses the same value as the pickle module, HIGHEST_PROTOCOL or DEFAULT_PROTOCOL.

  • with byref=True, dill to behave a lot more like pickle with certain objects (like modules) pickled by reference as opposed to attempting to pickle the object itself.

  • with recurse=True, objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary.

  • with fmode, the contents of the file can be pickled along with the file handle, which is useful if the object is being sent over the wire to a remote system which does not have the original file on disk. Options are HANDLE_FMODE for just the handle, CONTENTS_FMODE for the file content and FILE_FMODE for content and handle.

  • with ignore=False, objects reconstructed with types defined in the top-level script environment use the existing type in the environment rather than a possibly different reconstructed type.

The default serialization can also be set globally in dill.settings. Thus, we can modify how dill handles references to the global dictionary locally or globally:

>>> import dill.settings
>>> dumps(absolute) == dumps(absolute, recurse=True)
>>> dill.settings['recurse'] = True
>>> dumps(absolute) == dumps(absolute, recurse=True)

dill also includes source code inspection, as an alternate to pickling:

>>> import dill.source
>>> print(dill.source.getsource(squared))
squared = lambda x:x**2

To aid in debugging pickling issues, use dill.detect which provides tools like pickle tracing:

>>> import dill.detect
>>> dill.detect.trace(True)
>>> f = dumps(squared)
F1: <function <lambda> at 0x108899e18>
F2: <function _create_function at 0x108db7488>
# F2
Co: <code object <lambda> at 0x10866a270, file "<stdin>", line 1>
F2: <function _create_code at 0x108db7510>
# F2
# Co
D1: <dict object at 0x10862b3f0>
# D1
D2: <dict object at 0x108e42ee8>
# D2
# F1
>>> dill.detect.trace(False)

With trace, we see how dill stored the lambda (F1) by first storing _create_function, the underlying code object (Co) and _create_code (which is used to handle code objects), then we handle the reference to the global dict (D2). A # marks when the object is actually stored.

More Information

Probably the best way to get started is to look at the documentation at 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 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;

Michael McKerns and Michael Aivazis,
"pathos: a framework for heterogeneous computing", 2010- ;

Please see or for further information.

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