Dill

Dill is a module which extends python's 'pickle' module for serializing and de-serializing python objects to the majority of the built-in python types

Pickle

An object serialization module for Python



Serializer socket modules

Example

"I searched a bit and found that dill can perform better than pickle with classes but i am having problems to implement it"

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Pickle, dill - ImportError: No module named moduleA.moduleB.moduleC

"Dill has a better serializer that can pickle socket objects on any os and thus sending the socket object with multiprocess works in either case"

from question  

Why can Linux accept sockets in multiprocessing?

"I think you can do exactly what you want with dill which provides better serialization than pickle"

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Pickle dump an object referencing a module

"Modules such as dill are doing a better job than pickle"

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Multiprocessing.apply_async(myfunc, ...) within a class doesn't seem to call myfunc

"Cloudpickle might be a good idea for your problem as well cloudpickle has better support in pickling objects than pickle not per see better than dill and you can pickle code easily as well"

from question  

What are the pitfalls of using Dill to serialise scikit-learn/statsmodels models?

Others

Example

If you re using multiprocess which is a forked version of multiprocessing that pre-substitutes dill for pickle it s less obvious how to patch that

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Serializing objects for multiprocessing is slow -- is there a way to serialize just once?

Then you d pickle a mock object which would then pickle the class source code and then you d be able to pass that to the django cache;i m the author of dill which is a better serializer and also the author of klepto which is a caching package and this is exactly what i do to store any object in a sql table on disk or in an in-memory cache

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Pickle can't store an object in django locmem cache during tests?

This means pickle does not work on objects defined in __main__ and it also doesn t work on many dynamically modified objects;dill registers __main__ as a module so it has a valid namespace

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Pickling class method

You use pickle might fail dill is more stable

from question  

What are the pitfalls of using Dill to serialise scikit-learn/statsmodels models?

You may also want to have a look at dill which covers slightly more cases than pickle

from question  

How can I know if pickle.dump() successfully saved the file

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