Github user jkbradley commented on the issue:
https://github.com/apache/spark/pull/18313
Oh, you're right; I overlooked that it only holds all of the models for a
single split. In that case, I agree it could be problematic to keep all in
memory by default. How does this sound then: We can do 2 separate PRs, each of
which adds a separate option:
1. Add an option for keeping the models in memory so that they are
available as a data field in the Model object after training. This caters to
smaller use cases, focusing on ease-of-use.
2. (your PR) Add an option to dump models to a directory, not keeping them
in memory. This caters to big use cases, focusing on scalability.
What do you think? If this sounds good, feel free to reopen your current
PR.
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