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https://issues.apache.org/jira/browse/SPARK-14311?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-14311.
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Resolution: Fixed
> Model persistence in SparkR 2.0
> -------------------------------
>
> Key: SPARK-14311
> URL: https://issues.apache.org/jira/browse/SPARK-14311
> Project: Spark
> Issue Type: Umbrella
> Components: ML, SparkR
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
>
> In Spark 2.0, we are going to have 4 ML models in SparkR: GLMs, k-means,
> naive Bayes, and AFT survival regression. Users can fit models, get summary,
> and make predictions. However, they cannot save/load the models yet.
> ML models in SparkR are wrappers around ML pipelines. So it should be
> straightforward to implement model persistence. We need to think more about
> the API. R uses save/load for objects and datasets (also objects). It is
> possible to overload save for ML models, e.g., save.NaiveBayesWrapper. But
> I'm not sure whether load can be overloaded easily. I propose the following
> API:
> {code}
> model <- glm(formula, data = df)
> ml.save(model, path, mode = "overwrite")
> model2 <- ml.load(path)
> {code}
> We defined wrappers as S4 classes. So `ml.save` is an S4 method and ml.load
> is a S3 method (correct me if I'm wrong).
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