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Extended pickling support for Python objects

Project description

# cloudpickle

[![Build Status](

`cloudpickle` makes it possible to serialize Python constructs not supported
by the default `pickle` module from the Python standard library.

`cloudpickle` is especially useful for cluster computing where Python
expressions are shipped over the network to execute on remote hosts, possibly
close to the data.

Among other things, `cloudpickle` supports pickling for lambda expressions,
functions and classes defined interactively in the `__main__` module.

`cloudpickle` uses `pickle.HIGHEST_PROTOCOL` by default: it is meant to
send objects between processes running the same version of Python. It is
discouraged to use `cloudpickle` for long-term storage.


The latest release of `cloudpickle` is available from

pip install cloudpickle


Pickling a lambda expression:

>>> import cloudpickle
>>> squared = lambda x: x ** 2
>>> pickled_lambda = cloudpickle.dumps(squared)

>>> import pickle
>>> new_squared = pickle.loads(pickled_lambda)
>>> new_squared(2)

Pickling a function interactively defined in a Python shell session
(in the `__main__` module):

>>> CONSTANT = 42
>>> def my_function(data):
... return data + CONSTANT
>>> pickled_function = cloudpickle.dumps(my_function)
>>> pickle.loads(pickled_function)(43)

Running the tests

- With `tox`, to test run the tests for all the supported versions of
Python and PyPy:

pip install tox

or alternatively for a specific environment:

tox -e py27

- With `py.test` to only run the tests for your current version of

pip install -r dev-requirements.txt
PYTHONPATH='.:tests' py.test


`cloudpickle` was initially developed by []( and shipped as part of
the client SDK.

A copy of `` was included as part of PySpark, the Python
interface to [Apache Spark]( Davies Liu, Josh
Rosen, Thom Neale and other Apache Spark developers improved it significantly,
most notably to add support for PyPy and Python 3.

The aim of the `cloudpickle` project is to make that work available to a wider
audience outside of the Spark ecosystem and to make it easier to improve it
further notably with the help of a dedicated non-regression test suite.

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