The :pylenskit.sharing module provides utilities for managing models and sharing them between processes, particularly for the multiprocessing in :pylenskit.batch.
The only piece algorithm developers usually need to directly handle is the concept of 'sharing mode' when implementing custom pickling logic. To save space, it is reasonable to exclude intermediate data structures, such as caches or inverse indexes, from the pickled representation of an algorithm, and reconstruct them when the model is loaded.
However, LensKit's multi-process sharing also uses pickling to capture the object state while using shared memory for :pynumpy.ndarray objects. In these cases, the structures should be pickled, so they can be shared between model instances.
To support this, we have the concept of sharing mode. Code that excludes objects when pickling should call :pyin_share_context to determine if that exclusion should actually happen.
in_share_context
sharing_mode
These functions are used for internal LensKit infrastructure code to persist models into shared memory for parallel processing.
persist
PersistedModel