Pickler class to extend the standard pickle.Pickler functionality
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
code is shipped over the network to execute on remote hosts, possibly close
to the data.
Among other things,
cloudpickle supports pickling for lambda functions
along with functions and classes defined interactively in the
__main__ module (for instance in a script, a shell or a Jupyter notebook).
Cloudpickle can only be used to send objects between the exact same version of Python.
cloudpickle for long-term object storage is not supported and
Security notice: one should only load pickle data from trusted sources as
pickle.load can lead to arbitrary code execution resulting in a critical
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: int) -> int: ... return data + CONSTANT ... >>> pickled_function = cloudpickle.dumps(my_function) >>> depickled_function = pickle.loads(pickled_function) >>> depickled_function <function __main__.my_function(data:int) -> int> >>> depickled_function(43) 85
Overriding pickle's serialization mechanism for importable constructs:
An important difference between
pickle is that
cloudpickle can serialize a function or class by value, whereas
can only serialize it by reference. Serialization by reference treats
functions and classes as attributes of modules, and pickles them through
instructions that trigger the import of their module at load time.
Serialization by reference is thus limited in that it assumes that the module
containing the function or class is available/importable in the unpickling
environment. This assumption breaks when pickling constructs defined in an
interactive session, a case that is automatically detected by
that pickles such constructs by value.
Another case where the importability assumption is expected to break is when
developing a module in a distributed execution environment: the worker
processes may not have access to the said module, for example if they live on a
different machine than the process in which the module is being developed. By
cloudpickle cannot detect such "locally importable" modules and
switch to serialization by value; instead, it relies on its default mode, which
is serialization by reference. However, since
cloudpickle 2.0.0, one can
explicitly specify modules for which serialization by value should be used,
>>> import cloudpickle >>> import my_module >>> cloudpickle.register_pickle_by_value(my_module) >>> cloudpickle.dumps(my_module.my_function) # my_function is pickled by value >>> cloudpickle.unregister_pickle_by_value(my_module) >>> cloudpickle.dumps(my_module.my_function) # my_function is pickled by reference
Using this API, there is no need to re-install the new version of the module on all the worker nodes nor to restart the workers: restarting the client Python process with the new source code is enough.
Note that this feature is still experimental, and may fail in the following situations:
If the body of a function/class pickled by value contains an
>>> def f(): >>> ... from another_module import g >>> ... # calling f in the unpickling environment may fail if another_module >>> ... # is unavailable >>> ... return g() + 1
If a function pickled by reference uses a function pickled by value during its execution.
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 py312
pytestto only run the tests for your current version of Python:
pip install -r dev-requirements.txt PYTHONPATH='.:tests' pytest
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.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for cloudpickle-3.0.0-py3-none-any.whl