Extended pickling support for Python objects
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README.md

cloudpickle

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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.

Installation

The latest release of cloudpickle is available from pypi:

pip install cloudpickle

Examples

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)
4

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)
85

Running the tests

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

    pip install tox
    tox
    

    or alternatively for a specific environment:

    tox -e py27
    
  • With py.test to only run the tests for your current version of Python:

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

History

cloudpickle was initially developed by picloud.com and shipped as part of the client SDK.

A copy of cloudpickle.py 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.