I am not sure I get what you are trying to do, but let me try to answer your questions:
Will knime support saving the model using PySpark…In case of PySpark model is saved as folder??
I assume you are relating to a ml/mllib model that was created in a PySpark snippet node. Currently the PySpark nodes to not support the output of a model via a KNIME Port. But you can save the model in the HDFS of the Cluster. It is as easy as calling model.save(sc,“path in hdfs”). You are then able to load the same model in another snippet with ModelCLass.load(sc,“path in hdfs”).
Some models can also be saved as PMML https://spark.apache.org/docs/latest/mllib-pmml-model-export.html which could then be used in any KNIME node with PMML input.
I want to implement models as branching one after the other one model output is input for other model Nothing but an hierarchial of models as shown in below figure.
My first idea, here would be to use three spark rowfilters, that filter the needed rows after the first model and then use it in the corresponding PySpark snippet.
Will python learner support sprak mlib libraries/pyspark models and can we execute PySpark models using python learner node?
I am afraid I do not understand what you want to do here. My best guess is that you would like to PySpark within the normal Python node and then have a pickled object of the ml/mllib model? What is the use case for this?
best regards Mareike