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
---------------
The latest released version of
---------------
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
-------------------
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
------------
-------------------
You can get the latest development version with all the shiny new features at:
https://github.com/uqfoundationdill 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), >=3.10
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: at 0x7fe074f8c280>
├┬ F2:
│└ # F2 [34 B]
├┬ Co: at 0x7fe07501eb30, file "", line 1>
│├┬ F2:
││└ # F2 [19 B]
│└ # Co [87 B]
├┬ D1:
│└ # D1 [22 B]
├┬ D2:
│└ # D2 [2 B]
├┬ D2:
│├┬ D2:
││└ # 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.