Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18888#discussion_r132744158
  
    --- Diff: python/pyspark/ml/pipeline.py ---
    @@ -242,3 +327,65 @@ def _to_java(self):
                 JavaParams._new_java_obj("org.apache.spark.ml.PipelineModel", 
self.uid, java_stages)
     
             return _java_obj
    +
    +
    +@inherit_doc
    +class SharedReadWrite():
    +    """
    +    Functions for :py:class:`MLReader` and :py:class:`MLWriter` shared 
between
    +    :py:class:'Pipeline' and :py:class'PipelineModel'
    +
    +    .. versionadded:: 2.3.0
    +    """
    +
    +    @staticmethod
    +    def validateStages(stages):
    +        """
    +        Check that all stages are Writable
    +        """
    +        for stage in stages:
    +            if not isinstance(stage, MLWritable):
    +                raise ValueError("Pipeline write will fail on this pipline 
" +
    +                                 "because stage %s of type %s is not 
MLWritable",
    +                                 stage.uid, type(stage))
    +
    +    @staticmethod
    +    def saveImpl(instance, stages, sc, path):
    +        """
    +        Save metadata and stages for a :py:class:`Pipeline` or 
:py:class:`PipelineModel`
    +        - save metadata to path/metadata
    +        - save stages to stages/IDX_UID
    +        """
    +        stageUids = [stage.uid for stage in stages]
    +        jsonParams = {'stageUids': stageUids, 'savedAsPython': True}
    +        DefaultParamsWriter.saveMetadata(instance, path, sc, 
paramMap=jsonParams)
    +        stagesDir = os.path.join(path, "stages")
    --- End diff --
    
    This is as good as it gets, as far as I know


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