commit | 119d4ba918a4034157bd5f5a2edd67b049a7d5ab | [log] [tgz] |
---|---|---|
author | mmckerns <mmckerns@caltech.edu> | Tue Oct 25 18:37:46 2022 -0400 |
committer | mmckerns <mmckerns@caltech.edu> | Tue Oct 25 18:37:46 2022 -0400 |
tree | 5faa8ddfa1ac4dda337cc8e8dcd70ef8c5e5f9f9 | |
parent | eb98c5939b537599ada52a6be0b39c5941fbc5d8 [diff] |
formally support python 3.11
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:
dill
can also pickle more ‘exotic’ standard types:
dill
cannot yet pickle these standard types:
dill
also provides the capability to:
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
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
), >=3.7setuptools
, >=42Optional requirements:
objgraph
, >=1.7.2pyreadline
, >=1.7.1 (on windows)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:
pickle
module, DEFAULT_PROTOCOL.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.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.
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.