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
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) 4
Pickling a function interactively defined in a Python shell session
>>> CONSTANT = 42 >>> def my_function(data): ... return data + CONSTANT ... >>> pickled_function = cloudpickle.dumps(my_function) >>> pickle.loads(pickled_function)(43) 85
Running the tests
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
py.testto only run the tests for your current version of Python:
pip install -r dev-requirements.txt PYTHONPATH='.:tests' py.test
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.