R models are bundled and serialized using the .Rdata format. The yhatr client inspects the user's model and identifies which functions, source code, modules, objects, etc. it needs in order to execute the model.predict function. Once it has a list of those, it uses the save function in R to bundle them into an .Rdata file which can then be transported to ScienceOps.


Python models are bundled and serialized using the yhat Python package. During a deployment, yhat will inspect the user's workspace and identify source code, functions, modules, objects, etc. that it needs in order to run the execute method of the user's model class (either a YhatModel or a SplitTestModel). Once it's identified the list of requirements, the yhat package uses terragon (yes, it's spelled wrong) to serialize those objects. terragon uses a combination of serialization formats in Python in order to accomodate as many different libraries as possible. The preferred format is pickle, however not all Python objects can be serialized this way. For example, models built using Tensorflow and PySpark have their own respective serialization formats which terragon uses accordingly.

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