Metadata-Version: 2.1
Name: cloudpickle
Version: 1.6.0
Summary: Extended pickling support for Python objects
Home-page: https://github.com/cloudpipe/cloudpickle
Author: Cloudpipe
Author-email: cloudpipe@googlegroups.com
License: BSD 3-Clause License
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: System :: Distributed Computing
Requires-Python: >=3.5
Description-Content-Type: text/markdown

# cloudpickle

[![Automated Tests](https://github.com/cloudpipe/cloudpickle/workflows/Automated%20Tests/badge.svg?branch=master&event=push)](https://github.com/cloudpipe/cloudpickle/actions)
[![codecov.io](https://codecov.io/github/cloudpipe/cloudpickle/coverage.svg?branch=master)](https://codecov.io/github/cloudpipe/cloudpickle?branch=master)

`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**.

Using `cloudpickle` for **long-term object storage is not supported and
strongly discouraged.**

**Security notice**: one should **only load pickle data from trusted sources** as
otherwise `pickle.load` can lead to arbitrary code execution resulting in a critical
security vulnerability.


Installation
------------

The latest release of `cloudpickle` is available from
[pypi](https://pypi.python.org/pypi/cloudpickle):

    pip install cloudpickle


Examples
--------

Pickling a lambda expression:

```python
>>> 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):

```python
>>> 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
```

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 py37


- 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](http://web.archive.org/web/20140721022102/http://blog.picloud.com/2013/11/17/picloud-has-joined-dropbox/) 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](https://spark.apache.org/). 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.