Serialization by reference is thus limited in that it assumes that the moduleĬontaining the function or class is available/importable in the unpicklingĮnvironment. Instructions that trigger the import of their module at load time. Serialization by reference treatsįunctions and classes as attributes of modules, and pickles them through loads ( pickled_function ) > depickled_function int > depickled_function ( 43 ) 85 Overriding pickle's serialization mechanism for importable constructs:Īn important difference between cloudpickle and pickle is thatĬloudpickle can serialize a function or class by value, whereas pickleĬan only serialize it by reference. dumps ( my_function ) > depickled_function = pickle. (in the _main_ module): > CONSTANT = 42 > def my_function ( data : int ) -> int. Pickling a function interactively defined in a Python shell session loads ( pickled_lambda ) > new_squared ( 2 ) 4 dumps ( squared ) > import pickle > new_squared = pickle. Pickling a lambda expression: > import cloudpickle > squared = lambda x : x ** 2 > pickled_lambda = cloudpickle. The latest release of cloudpickle is available from Otherwise pickle.load can lead to arbitrary code execution resulting in a critical Security notice: one should only load pickle data from trusted sources as Using cloudpickle for long-term object storage is not supported and _main_ module (for instance in a script, a shell or a Jupyter notebook).Ĭloudpickle can only be used to send objects between the exact same version Cloudpickle makes it possible to serialize Python constructs not supportedīy the default pickle module from the Python standard library.Ĭloudpickle is especially useful for cluster computing where PythonĬode is shipped over the network to execute on remote hosts, possibly closeĪmong other things, cloudpickle supports pickling for lambda functionsĪlong with functions and classes defined interactively in the
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