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 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 feasible 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 https://github.com/uqfoundation/dill/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
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
The latest released version of dill is available from:
https://pypi.org/project/dill
dill 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.
dill is a drop-in replacement for pickle. Existing code can be
updated to allow complete pickling using::
>>> import dill as pickle
or::
>>> 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)
9
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
picklemodule, HIGHEST_PROTOCOL or DEFAULT_PROTOCOL. - with byref=True,
dillto 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)
False
>>> dill.settings['recurse'] = True
>>> dumps(absolute) == dumps(absolute, recurse=True)
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.
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- ;
https://uqfoundation.github.io/project/pathos
Please see https://uqfoundation.github.io/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.