register_generic(cls, serializer_name='dask', serialize_func=<dask.utils.Dispatch object at 0x0000000>, deserialize_func=<dask.utils.Dispatch object at 0x0000000>)
Normally when registering new classes for Dask's custom serialization you need to manage headers and frames, which can be tedious. If all you want to do is traverse through your object and apply serialize to all of your object's attributes then this function may provide an easier path.
This registers a class for the custom Dask serialization family. It serializes it by traversing through its __dict__ of attributes and applying serialize
and deserialize
recursively. It collects a set of frames and keeps small attributes in the header. Deserialization reverses this process.
This is a good idea if the following hold:
Most of the bytes of your object are composed of data types that Dask's custom serializtion already handles well, like Numpy arrays.
Your object doesn't require any special constructor logic, other than object.__new__(cls)
Register (de)serialize to traverse through __dict__
>>> import sklearn.baseSee :
... from distributed.protocol import register_generic
... register_generic(sklearn.base.BaseEstimator)
The following pages refer to to this document either explicitly or contain code examples using this.
distributed.protocol.serialize.register_generic
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