[jira] [Commented] (PIO-192) Enhance PySpark support

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/PIO-192?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16706994#comment-16706994 ] 

ASF GitHub Bot commented on PIO-192:
------------------------------------

shimamoto commented on issue #494: [PIO-192] Enhance PySpark support
URL: https://github.com/apache/predictionio/pull/494#issuecomment-443670773
 
 
   @marevol @takezoe I updated the JIRA ticket. Again, could you please review this PR?

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> Enhance PySpark support
> -----------------------
>
>                 Key: PIO-192
>                 URL: https://issues.apache.org/jira/browse/PIO-192
>             Project: PredictionIO
>          Issue Type: Improvement
>          Components: Core
>    Affects Versions: 0.13.0
>            Reporter: Takako Shimamoto
>            Assignee: Takako Shimamoto
>            Priority: Major
>
> h3. Summary
> Enhance the pypio, which is the Python API for PIO.
> h3. Goals
> The limitations of the current Python support always force developers to have access to sbt. This enhancement will get rid of the build phase.
> h3. Description
> A Python engine has nothing to need. Developers can use the pypio module with jupyter notebook and Python code.
> First, import the necessary modules.
> {code:python}
> import pypio
> {code}
> Once the module in imported, the first step is to initialize the pypio module.
> {code:python}
> # not create App
> pypio.init()
> # create App (pio app new BHPApp)
> pypio.init('BHPApp')
> {code}
> Next, find data from the event store.
> {code:python}
> event_df = pypio.find_events('BHPApp')
> {code}
> And then, save the model.
> {code:python}
> # model is a PipelineModel, which is produced after a Pipeline’s fit() method runs
> pipeline = Pipeline(...)
> model = pipeline.fit(train_df)
> engine_instance_id = pypio.save_model(model, ["prediction"])
> {code}
> h4. Run & Deploy
> h5. Run Jupyter
> {code:sh}
> pio-shell --with-pyspark
> {code}
> h5. Run on Spark
> {code:sh}
> pio train --main-py-file xxxx.py
> {code}
> h5. Deploy App
> {code:sh}
> pio deploy --engine-instance-id <engine_instance_id>
> {code}



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