dill

serialize all of python

uqfoundation
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Python

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 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.

Major Features

dill can pickle the following standard types:

  • none, type, bool, int, float, complex, bytes, str,
  • tuple, list, dict, file, buffer, builtin,
  • Python classes, namedtuples, dataclasses, metaclasses,
  • instances of 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,
  • methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor,
  • dictproxy, 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
Downloads
Conda Downloads
Stack Overflow

The latest released version of dill is available from:
https://pypi.org/project/dill

dill is distributed under a 3-clause BSD license.

Development Version
Support
Documentation Status
Build Status
codecov

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.

Installation

dill can be installed with pip::

$ pip install dill

To optionally include the objgraph diagnostic tool in the install::

$ pip install dill[graph]

To optionally include the gprof2dot diagnostic tool in the install::

$ pip install dill[profile]

For windows users, to optionally install session history tools::

$ pip install dill[readline]

Requirements

dill requires:

  • python (or pypy), >=3.8
  • setuptools, >=42

Optional requirements:

  • objgraph, >=1.7.2
  • gprof2dot, >=2022.7.29
  • 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

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 pickle module, 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)
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
>>> with dill.detect.trace():
>>>     dumps(squared)
┬ F1: <function <lambda> at 0x7fe074f8c280>
├┬ F2: <function _create_function at 0x7fe074c49c10>
│└ # F2 [34 B]
├┬ Co: <code object <lambda> at 0x7fe07501eb30, file "<stdin>", line 1>
│├┬ F2: <function _create_code at 0x7fe074c49ca0>
││└ # F2 [19 B]
│└ # Co [87 B]
├┬ D1: <dict object at 0x7fe0750d4680>
│└ # D1 [22 B]
├┬ D2: <dict object at 0x7fe074c5a1c0>
│└ # D2 [2 B]
├┬ D2: <dict object at 0x7fe074f903c0>
│├┬ D2: <dict object at 0x7fe074f8ebc0>
││└ # D2 [2 B]
│└ # D2 [23 B]
└ # F1 [180 B]

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) plus other dictionaries (D1 and D2) that
save the lambda object’s state. A # marks when the object is actually stored.

More Information

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).

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- ;
https://uqfoundation.github.io/project/pathos

Please see https://uqfoundation.github.io/project/pathos or
http://arxiv.org/pdf/1202.1056 for further information.